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Candida albicans biofilm formation is a key virulence trait that involves hyphal growth and adhesin expression . Pyocyanin ( PYO ) , a phenazine secreted by Pseudomonas aeruginosa , inhibits both C . albicans biofilm formation and development of wrinkled colonies . Using a genetic screen , we identified two mutants , ssn3Δ/Δ and ssn8Δ/Δ , which continued to wrinkle in the presence of PYO . Ssn8 is a cyclin-like protein and Ssn3 is similar to cyclin-dependent kinases; both proteins are part of the heterotetrameric Cdk8 module that forms a complex with the transcriptional co-regulator , Mediator . Ssn3 kinase activity was also required for PYO sensitivity as a kinase dead mutant maintained a wrinkled colony morphology in the presence of PYO . Furthermore , similar phenotypes were observed in mutants lacking the other two components of the Cdk8 module—Srb8 and Srb9 . Through metabolomics analyses and biochemical assays , we showed that a compromised Cdk8 module led to increases in glucose consumption , glycolysis-related transcripts , oxidative metabolism and ATP levels even in the presence of PYO . In the mutant , inhibition of respiration to levels comparable to the PYO-treated wild type inhibited wrinkled colony development . Several lines of evidence suggest that PYO does not act through Cdk8 . Lastly , the ssn3 mutant was a hyperbiofilm former , and maintained higher biofilm formation in the presence of PYO than the wild type . Together these data provide novel insights into the role of the Cdk8 module of Mediator in regulation of C . albicans physiology and the links between respiratory activity and both wrinkled colony and biofilm development .
Candida albicans is a fungus that can switch from growth as a natural human commensal to a pathogen and , if host defenses are compromised , consequently cause life-threatening systemic infections [1]–[6] . This organism readily forms biofilms , which are complex communities composed of various cellular morphologies that are held together by the production of adhesins and extracellular matrix polymeric substances that are mainly composed of polysaccharides [7] . Morphological plasticity is a key requirement for biofilm formation [8] , as this process involves initial attachment to a surface by yeast cells which germinate to form a matrix-encased hyphal network [9] . As a natural member of the human microbiota , C . albicans readily encounters indwelling biomedical devices such as intravascular catheters that can act as biofilm substrates [10] , [11] . Consequently , C . albicans is currently highly ranked among the fungi most commonly isolated from catheter-based biofilm infections [12] , [13] . In addition , biofilm formation by C . albicans confers increased resistance to antifungal agents [14]–[18] . Given biofilms play a key role in C . albicans disease , it is important to understand the physiological pathways that can promote or prevent biofilm development . C . albicans growth as wrinkled colonies also requires hypha formation and the production of adhesins [19]–[23] . Formation of these wrinkled communities by C . albicans strains has been found to facilitate increased access to oxygen and may thus serve as a means to maintain redox balance [24] , [25] . Consistent with this model , low oxygen conditions stimulate filamentation and wrinkled colony formation in various Candida species [7] , [26] , [27] . In contrast , anoxic environments reportedly inhibit biofilm formation while still permitting hyphal growth [28] , [29] . Interestingly , Watanabe and colleagues described that the induction of hyphal growth is positively regulated by the respiratory chain , due to their observation that disruption of electron flow between complex II and Coenzyme Q by treatment with thenoyltrifluoroacetone drastically repressed filamentation [30] . Supporting their observation , we recently reported that pyocyanin ( PYO ) , a redox-active phenazine secreted by Pseudomonas aeruginosa , and related compounds ( e . g . methylene blue ) reduce respiratory activity and inhibit formation of wrinkled colonies and biofilms [25] . These findings suggest a link between respiration and biofilm formation in C . albicans . To gain further insight into the link between PYO-mediated inhibition of respiration and the inhibition of biofilm formation , we screened mutant collections to identify strains that continued to form wrinkled colonies in the presence of non-toxic concentrations of PYO . This genetic approach revealed that the absence of Ssn3 or Ssn8 increased resistance to PYO-induced repression of hyphal growth and colony wrinkling . Ssn3 and Ssn8 are components of the Cdk8 module of Mediator , which is a eukaryote-specific multi-protein complex [31] that serves as a bridge between regulatory proteins and RNA polymerase ( Figure 1 ) [32] , [33] . Mediator is an integral component of transcriptional regulation , and this process may be a key way by which cells can modulate the transition to biofilm or wrinkled colony formation . The heterotetrameric Cdk8 module of Mediator is primarily involved in transcriptional repression [34] , [35] and studies in other fungi such as Saccharomyces cerevisiae and Schizosaccharomyces pombe have shown that this module regulates hundreds of genes , some of which have been linked to carbon source utilization , stress responses and adhesin expression [36]–[41] . We found that disruption of Cdk8 module activity , but not non-essential regulatory subunits of the Core module of Mediator , resulted in increased glucose uptake , metabolic activity and ATP production . These data suggested that increased respiratory activity conferred resistance to the effects of PYO . Our work indicated a tight link between metabolic activity and the formation of wrinkled colonies; it also showed that the stability of Ssn3 and Ssn8 were unaffected by PYO . Analysis of the metabolite profiles in the wild type and ssn3 mutant grown in the absence and presence of PYO was performed; these data provided insight into both additional roles of Ssn3 and the effects of PYO on C . albicans . Taken together , these results increase our understanding of the role played by the Cdk8 module in the regulation of metabolism and morphological plasticity , the links between these two processes and the mechanism by which phenazines , such as PYO , affect the biology of C . albicans .
We previously demonstrated that PYO inhibits both biofilm formation on plastic and wrinkled colony formation [25] . Using the wrinkled colony phenotype that develops in C . albicans colonies when they are incubated at 37°C on medium with GlcNAc , we performed a genetic screen for mutants that were resistant to the repressive effects of PYO . In a screen of approximately 1 , 500 strains from homozygous knockout collections [42]–[46] , only thirteen were found to wrinkle under inducing conditions in the presence of 20 µM PYO ( Table S1 ) . Eleven of the thirteen mutants also wrinkled under non-inducing conditions in which colonies formed by wild-type strains are smooth and contain yeast cells , and these were excluded from subsequent studies due to the lack of specific “phenazine-resistance” phenotype . The remaining two mutants , ssn3Δ/Δ and ssn8Δ/Δ , formed colonies similar to those of the wild type on non-inducing and inducing media without PYO , but formed wrinkled colonies in the presence of PYO . The PYO-resistance phenotype persisted when the mutants were restored to prototrophy using the vector pDDB78 , and complementation with their respective native alleles restored sensitivity to PYO ( Figure 2 ) . Similar to the wild type strain , both mutants and their complemented derivatives formed smooth colonies under non-inducing conditions ( Figure 2 ) . Analysis of the cellular morphology showed that wrinkled colonies contained a mixture of hyphae and yeast on medium with vehicle [25] . PYO caused the wild type to grow as a smooth colony composed exclusively of yeast , while ssn3 and ssn8 mutant colonies contained both yeast and hyphal cells , as is characteristic of wrinkled colonies . Ssn3 and Ssn8 of C . albicans are orthologous to S . cerevisiae Srb10 and Srb11 , which make up a cyclin-dependent kinase/cyclin pair . These proteins are components of the Cdk8 module of Mediator ( Figure 1 ) , which is an RNA polymerase II ( RNAPII ) co-regulator of gene expression [32] , [33] . The Cdk8 module is best known for its repressive activities while the Core module of Mediator generally promotes transcription [32] , [35] , [47]–[49] . Ssn3 ( Srb10 ) kinase activity participates in transcriptional regulation through phosphorylation of RNAPII [50]–[52] and phosphorylation of target transcription factors [53]–[58] . We therefore determined if loss of Ssn3 kinase activity led to a phenotype similar to the ssn3Δ/Δ mutant , in terms of resistance to PYO , using the previously published strain expressing the ssn3D325A allele that is predicted to encode a kinase-dead Ssn3 variant [56] . Given the strain expressing the kinase-dead variant maintained wrinkling in the presence of PYO , whereas the comparable strain expressing the native allele was still sensitive , we concluded that the loss of Ssn3 kinase activity was sufficient to promote C . albicans PYO-resistance ( Figure 3 ) . In S . cerevisiae , changes in levels of the Ssn3 and Ssn8 orthologs have been shown to regulate transcriptional profiles [59] . Because the ssn3 and ssn8 mutants continued to wrinkle on medium with PYO , we sought to determine if PYO-induced repression of filamentation and wrinkled colony formation was mediated by increased levels of Ssn3 , Ssn8 or both proteins . To test this model , we performed a Western blot analysis of strains bearing HA-tagged variants of either Ssn3 or Ssn8 grown to exponential phase in liquid medium , containing the filament inducer GlcNAc , in the presence and absence of PYO . While PYO inhibits filamentation under these growth conditions on both agar medium ( Figure 2 ) and in liquid [25] , it did not affect the levels of Ssn3-HA or Ssn8-HA ( Figure 4 ) relative to the tubulin control , indicating that PYO-mediated repression of morphology was likely not due to increased levels of these proteins . Having identified two mutants of the heterotetrameric Cdk8 module of Mediator as having increased resistance to PYO , we sought to determine if mutants lacking other components of the Cdk8 module—Srb8 and Srb9—also had increased resistance to PYO-mediated repression of wrinkling . We found that absence of either SRB8 or SRB9 also increased resistance to PYO ( Figure S1A ) . Restoration of the SRB9 gene complemented the PYO-resistance phenotype , though robust complementation was not achieved with a single allele of SRB8 suggesting haploinsufficiency . Several reviews have described the Cdk8 module of Mediator acting primarily as a transcriptional repressor when in complex with the Core module , while the Core module without Cdk8 is largely involved in transcriptional activation ( Figure 1 ) [32] , [35] , [47]–[49] , [60] . Studies of S . cerevisiae suggest a high degree of diversity in the effects of non-essential Mediator subunits on transcription , however some Cdk8 module phenotypes are reportedly shared by the subunits of the Core module [38] . To determine if the PYO-resistance phenotype was also observed upon loss of other non-essential Mediator components , we tested the phenotypes for mutants lacking MED1 , MED3 , MED5 , MED9 , MED16 and MED20 . None of these Core module mutants maintained wrinkling in the presence of PYO ( Figure S1B ) , though a slight level of resistance was observed in med3Δ/Δ . These data suggest that the PYO-resistance phenotype is specifically associated with defects in the Cdk8 module of Mediator . In S . cerevisiae , the Cdk8 module can negatively regulate transcription factors that act in concert with Tup1 [36] , [61] . In C . albicans , the loss of Tup1 or its co-regulator Nrg1 is sufficient to lead to constitutive filamentation and wrinkled colony formation [62] , [63] . To determine if the phenotype of the Cdk8 module mutants could be attributed to lower levels of Nrg1 or other Tup1-co-regulators , we determined if tup1Δ/Δ and nrg1Δ/Δ strains were also resistant to the effects of PYO . This did not appear to be the case , as both mutants showed a marked reduction in wrinkling with PYO indicating that the Cdk8 module was not likely acting through Tup1 or Nrg1 in smooth colonies and that hyperwrinkling does not necessarily confer enhanced resistance to PYO ( Figure S2 ) . We previously reported that the inhibition of respiratory metabolism in C . albicans by PYO correlates with increased acetic acid production and a concomitant inability to alkalinize the extracellular milieu [25] . To determine if the Cdk8 module influenced the ability to alkalinize the medium , we added the pH indicator bromocresol purple to the medium to assess changes in extracellular pH over time . We initially focused on the ssn3 mutant , which encodes the Cdk8-like kinase . Our results indicated that , in the absence of PYO , the ssn3 mutant alkalinized the medium more quickly ( Figure S3A ) and to a greater extent ( Figure 5 and S3B ) than the wild-type controls . Furthermore , in contrast to the wild type [25] and complemented strain , the ssn3 mutant alkalinized the medium even in the presence of PYO ( Figure 5 ) . We found similar phenotypes in the other three Cdk8 module mutants—ssn8 , srb8 , and srb9 ( Figure S3B ) ; phenotypic similarity among different Cdk8 module mutants has been observed in other studies in other species [52] , [64] , [65] . Because pH is a strong modulator of morphology [66] , [67] , we sought to determine if the increased extracellular pH around ssn3 mutant colonies contributed to the ability to filament in the presence of PYO . To test this idea , we cultured our strains of interest on inducing medium that was buffered to pH 7 in the presence or absence of PYO . Both strains formed wrinkled colonies on the buffered medium in the absence of PYO ( Figure S4A ) , but while the filamentation of the wild type was still largely repressed by PYO on medium buffered to pH 7 , the ssn3 mutant continued to form wrinkled colonies ( Figure S4A ) . Furthermore , on medium buffered to pH 7 containing glucose and GlcNAc without amino acids , the ssn3Δ/Δ strain wrinkled in both the presence and absence of PYO while the wild type was only wrinkled on PYO-free medium ( Figure S4B ) . Thus , we concluded that differences in amino acid catabolism and the concomitant increases in the pH of the medium , were not sufficient to cause the persistent colony wrinkling of the ssn3 mutant in the presence of PYO . To determine if the changes in colony morphology induced by PYO were sufficient to cause differences in metabolism that led to increased acidification , wild type and mutant strains were grown under non-inducing ( yeast growth ) conditions in the presence and absence of PYO on medium containing a pH indicator . As under conditions that promote colony wrinkling , PYO prevented alkalinization of medium by the wild type , but not the ssn3 mutant ( Figure S4C ) leading us to conclude that the mutant had fundamental differences in metabolism that were not dependent on changes in morphology . The alkalinization or other differences in metabolism in the ssn3 mutant may , however , contribute to colony wrinkling as the addition of amino acids to the medium caused slight wrinkling in the ssn3 mutant under yeast growth conditions ( compare Figure S4C to Figure 2 ) . Based on these findings , we concluded that the higher pH in ssn3Δ/Δ cultures was not sufficient to explain wrinkling in the presence of PYO , and that the effects of PYO on metabolism were independent of differences in colony and cellular morphology . We recently reported that the lower pH of the extracellular milieu in the presence of PYO is due to changes in fungal metabolism induced by this bacterial product [25] . The higher pH in mutant cultures , when compared to those of the wild type , prompted us to test the hypothesis that there was a difference in the basal metabolism of the ssn3Δ/Δ mutant compared to the wild type strain , and that these differences contributed to a change in PYO resistance . To gain insight into the role of the Cdk8 module in C . albicans metabolism , we performed a metabolomics analysis of the wild type , ssn3Δ/Δ , and ssn3Δ/Δ+SSN3 strains . More than 250 metabolites were reported ( Table S2 ) , and we focused on those that were different between the ssn3Δ/Δ mutant and both the wild type and complemented strain , but were not different between the wild type and the complemented strain . The 221 metabolites that met this criterion were divided into eight metabolite categories: carbohydrate , amino acid , energy , nucleotide , peptide , lipid , cofactors & vitamins and xenobiotics ( namely pyocyanin ) ( Table S2 ) . Under our vehicle-treated conditions , the two categories with the largest number of compounds higher in the mutant were “carbohydrates” and “amino acids” . Interestingly , there were a large number of compounds at lower levels in the “amino acids” and “peptides” categories when the mutant was compared to the wild type and SSN3 complemented strain ( Figure S5 ) . These categories included intermediates in both biosynthesis and catabolism of the canonical amino acids as well as several amino acid derivatives such as glutathione . Initially , we focused on the increased representation among the sugars and their derivatives; the patterns evident in amino acid and peptide categories are considered further in the Discussion . Our metabolomics data suggested the intracellular levels of glucose and glucose-6-phoshate were higher in the mutant compared to the wild type , and led us to propose that the mutant had increased glycolytic activity ( Table 1 ) . Fructose-6-phosphate and 3-phosphoglycerate , two other glycolytic intermediates , showed similar increases in the mutant compared to the wild type and the SSN3 complemented strain , but did not meet the criterion of statistical significance ( p>0 . 05 ) . To gain additional insight into the observed changes in glycolytic intermediates , we used RT-PCR to determine if transcripts involved in glycolysis were upregulated in the ssn3 mutant . Our results indicated majority of the glycolysis-related transcripts were expressed at higher relative levels in the absence of SSN3 when normalized to the housekeeping transcript PMA1 ( Figure 6A ) . Specifically , there was a significant increase in the levels of transcripts encoding the enzymes HXK2 ( hexokinase II ) , PFK1 ( 6-phosphofructokinase I ) , FBA1 ( fructose-bisphosphate adolase ) , TPI1 ( triose-phosphate isomerase ) , PGK1 ( phosphoglycerate kinase ) , GPM1 ( phosphoglycerate mutase I ) , GPM2 ( phosphoglycerate mutase II ) , ENO1 ( enolase ) and CDC19 ( phosphoglycerate kinase ) . PGI1 ( phosphoglucose isomerase ) and PFK2 ( 6-phosphofructokinase I ) were not significantly higher in the mutant . An analysis of transcripts associated with glucose transport also showed increased levels of mRNAs for two glucose transporters whose expression is associated with colony morphology [68] or biofilm growth [69]: HXT5 and HGT2 . Both were significantly higher in the mutant ( Figure 6B ) compared to both reference strains . Interestingly , van de Peppel and colleagues found that transcript levels of HXT5 were also higher in the S . cerevisiae Cdk8 module mutants [38] . To directly test the hypothesis that glucose uptake and catabolism were faster in the ssn3Δ/Δ strain , we compared glucose consumption by the wild type and ssn3Δ/Δ over 12 h . Yeast growth conditions ( 30°C and the absence of GlcNAc ) were used rather than hypha-inducing conditions in order to prevent cell clumping which could impact nutrient uptake or oxygen availability . Our analysis of extracellular glucose concentrations by high-performance liquid chromatography ( HPLC ) indicated that significantly higher levels of glucose remained in supernatants from wild type cultures , when compared to those from mutant cultures , at both 3 h and 6 h ( Figure 7 ) , supporting our hypothesis that the mutant consumed glucose at a faster rate than the wild type . No extracellular glucose was detected in the supernatants of the wild type or mutant cultures at later time points . To further understand glucose utilization by the ssn3 mutant , we assessed products in other pathways related to glucose catabolism . First , we determined levels of ethanol and glycerol , two C . albicans fermentation products . Our data revealed no notable differences in ethanol or glycerol production between the mutant and wild type strain by HPLC ( Figure S6A ) . Intracellular glycerol levels did not differ between these strains in the metabolomics study ( Table S2 ) , and ethanol was not detected as an intracellular metabolite . Second , we assessed acidification of the extracellular milieu , indicative of acetic acid production , by the wild type and ssn3 mutant using the pH indicator bromocresol green in medium with glucose as the sole carbon source . We found no differences in acidification levels between strains ( Figure S6B ) . Taken together , these results suggested that the absence of Ssn3 did not promote the conversion of glucose to known fermentation products . Other glucose-related metabolic pathways were also considered . The canonical intermediates of the pentose phosphate pathway ( PPP ) were either decreased or unaffected by the absence of Ssn3 ( Table 1 ) . Additionally , some metabolites that are associated with the PPP , such as gluconate , were at lower levels in the ssn3 mutant compared to the wild type or the complemented strain ( Table 1 ) . These findings suggest that glycolysis , but not all glucose-related pathways , was altered upon loss of Ssn3 function . Given our evidence that ssn3Δ/Δ acquired glucose more rapidly than the wild type , without having higher levels of fermentation products or PPP intermediates , we determined if the absence of Ssn3 increased oxidative metabolism using the reduction of alamarBlue assay as a read out [70] , [71] . The same growth conditions as those in the HPLC studies were used except that glucose , at a higher concentration ( 100 mM ) , was provided as the sole carbon source . We found that the ssn3 mutant reduced alamarBlue at a significantly faster rate than strains with functional SSN3 , indicating that the mutant had higher levels of respiratory activity ( Figure 8A ) . A similar phenotype was observed for the remaining Cdk8 module mutants ( Figure S7 ) . While there was a slight enhancement in early growth rates in early exponential phase cultures , the maximal growth rates and culture yields were similar for both mutant and reference strains ( Figure S8 ) . Consistent with the observed elevation in oxidative metabolism upon loss of SSN3 , there were also higher levels of ATP in the ssn3Δ/Δ strain in both inducing conditions comparable to those used in the genetic screen and plate-based assays ( Figure 9 ) . As in the other experiments described above , the ssn8 , srb8 and srb9 mutants phenocopied the ssn3 mutant in that they too had higher levels of ATP when compared to the wild type ( Figure S9 ) . PYO has been shown to interfere with components of the electron transport chain in both fungi and mammals due to its ability to accept and donate electrons [72] . Because respiratory metabolism has been linked to hyphal growth [30] and wrinkled colony formation [25] , and loss of Ssn3 function results in higher basal oxidative metabolism and continued colony wrinkling in the presence of PYO , we sought to determine the effects of PYO on metabolism of the ssn3 mutant . When we evaluated the effects of PYO on ATP levels , we found that ATP levels in the ssn3 mutant were significantly higher than the wild type or the complemented variant in both the presence and absence of PYO ( Figure 9 ) . To further explore the relationship between the inhibition of respiration and the inhibition of colony wrinkling , we repeated the alamarBlue assay with increasing concentrations of PYO . We found that the ssn3 mutant grown in medium with 20 µM PYO had significantly higher levels of metabolic activity when compared to the wild type at the same concentration and , unlike the wild type , the mutant wrinkled at this concentration of PYO ( Figure 8B and Figure 8C ) . In fact , the ssn3 mutant treated with 20 µM PYO had respiratory activity comparable to the vehicle-treated wild type , and wrinkled colonies were formed in both cases ( Figure 8B and Figure 8C ) . In contrast , at 100 µM PYO , there was not a significant difference in oxidative metabolism between the ssn3 mutant and the wild type ( Figure 8B ) , and both colonies were smooth and largely comprised of yeast with this treatment concentration ( Figure 8B and Figure 8C ) . These data strongly support a model in which respiratory metabolism promotes , or is required , for hyphal growth , wrinkled colony formation and , likely , biofilm formation . To determine if the increased metabolism of the mutants conferred resistance to other compounds with the potential to impact respiration , we compared the effects of other mitochondrial inhibitors on the colony morphologies and alkalinization phenotypes of the wild type and ssn3 mutant . For these studies , we employed the use of methylene blue ( MB ) , a sulfur-containing phenothiazine known to impair electron transfer [73] , [74] , and antimycin A ( AA ) an inhibitor of complex III of the electron transport chain . As with PYO , the ssn3 mutant formed wrinkled colonies in the presence of 1 µM MB , while the wild type and respective complemented strain grew as smooth colonies ( Figure S10A ) . Interestingly , while the effect of 1 µM MB and 20 µM PYO on wrinkling were comparable , PYO caused acidification of the medium while MB did not inhibit alkalinization suggesting that the metabolic response to different types of mitochondrial inhibition may differ ( Figure S10A ) . It should be noted that the concentration of MB that inhibits wrinkling of C . albicans is lower than the concentration of bioavailable MB following oral administration for ailments such as malaria [75] . When the effects of AA were assessed , it was found that the growth of the mutant was much more strongly impaired than either the wild type or the complemented derivative making it challenging to assess the effects of this compound on metabolism and morphology ( Figure S10B ) . The reason for this sensitivity is unknown , but may relate to the altered sensitivity to ROS which can be generated upon treatment with AA [76] . To learn more about the effects of PYO on C . albicans , we further analyzed the metabolomics data , and made several observations . First , we obtained the reassuring result that PYO was only detected in PYO-treated cells ( Table S2 ) . Second , in accordance with previous findings that this phenazine can inhibit the activity of succinate dehydrogenase ( complex II in the electron transport chain and a component of the TCA cycle ) [77] ) , PYO significantly ( p<0 . 05 ) increased the levels of succinate by 4 . 7-fold and 8 . 1-fold in the wild type and the ssn3Δ/Δ respectively ( Table S2 ) . Other TCA cycle intermediates ( citrate , homocitrate and α-ketoglutarate ) were significantly lower upon PYO treatment of both strains ( Table S2 ) suggesting there was not a general increase in TCA cycle intermediates . Third , metabolites within the various categories were typically altered in the same direction in the wild type and the mutant ( Figure S11 and Table S2 ) . Fourth , we found that the mutant retained 40% higher glucose levels than the wild type in the presence of PYO even though PYO-treatment resulted in a 33 and 37% reduction of intracellular levels of glucose for the wild type and the ssn3 mutant respectively . This is consistent with our model that the mutant had higher levels of glycolysis associated metabolites ( Table 1 ) and transcripts ( Figure 6 ) , glucose consumption ( Figure 7 ) and oxidative metabolism ( Figure 8 and Figure 9 ) and was thus more resistant to the effects of PYO . Overall , these four results suggest the difference in PYO-sensitivity is due at least in part to differences in basal metabolism between the wild type and ssn3 mutant , rather than an altered response to PYO . The loss of Ssn3 and other components of the Cdk8 module of Mediator increased resistance to the repressive effect of PYO on respiratory activity and colony wrinkling . To determine if the colony phenotypes in the presence and absence of PYO extended to ssn3Δ/Δ biofilm phenotypes in a clinically-relevant C . albicans biofilm model , we assessed biofilm formation on silicone catheter material by the wild type , ssn3Δ/Δ and ssn3Δ/Δ+SSN3 strains in medium with and without PYO . While PYO decreased biofilm formation by all strains , the PYO-treated mutant biofilms had more biomass than the PYO-treated reference strains ( Figure 10 ) . In fact , the PYO-treated ssn3 mutant biofilms produced more biofilm than the wild type and complemented strains grown in the absence of PYO . Under the vehicle-control conditions , the ssn3 mutant produced significantly larger biofilm biomass compared to the control strains ( Figure 10 ) .
The studies presented here , in conjunction with previous studies [25] , [30] , demonstrate positive links between metabolic activity and the induction of hyphal growth and biofilm formation programs . Here , we show that mutation of any component of the Cdk8 module of the transcriptional co-regulator Mediator results in increased metabolic activity . Using the ssn3 mutant as a model , we also show that elevated glycolysis and respiration correlate with a need for higher concentrations of PYO to both inhibit metabolism and prevent wrinkled colony formation . Previous studies have shown that glycolysis-related genes are upregulated during biofilm formation [78]–[80] . We found these transcripts , along with the chemical intermediates in the glycolysis pathway , were also higher in the ssn3 mutant when compared to the control strains . These differences likely contributed to the hyperwrinkly and hyperbiofilm phenotype of the mutant . Both in C . albicans and other microbial species [24] , wrinkled colonies have increased oxygen availability within the colony despite higher levels of respiration [25] , suggesting that , like biofilms , these architectures provide a structural mechanism for gaining access to oxygen as a terminal electron acceptor and alleviating redox stress . The inhibition of wrinkled colony or biofilm development by reducing respiratory potential , without inhibiting growth due to the potential for fermentation , suggests the existence of a feedback mechanism between metabolism and cellular and colony morphology . This model is supported by work of other groups which show that biofilm formation and colony wrinkling are enhanced in low oxygen environments [7] , [26] , [27] . d'Enfert and colleagues [7] have shown that inactivation of TYE7 , a positive regulator of glycolysis , results in hyperfilamentation which compromises the integrity of biofilms formed by the tye7 mutant . It should be noted however that this phenotype was observed specifically in low oxygen environments , which is in contrast to our data which were gathered under atmospheric oxygen concentrations . This difference is likely important to consider , as the tye7 mutant did not exhibit a hyperwrinkly phenotype in our assays or an altered response to PYO ( Figure S12 ) . We speculate that the decision to form a biofilm incorporates information on the flux through the glycolysis pathway and mitochondrial activity , and future studies will test this hypothesis . The direct signals between metabolism and the pathways that control filamentation and biofilm development are not yet known . However , the higher levels of ATP in the ssn3 mutant in the presence of PYO ( Figure 9 ) might indicate increased potential for activation of the Ras1-adenylate cyclase signaling pathway that is required for hyphal growth as ATP is the substrate for cAMP synthesis ( Figure 11 [30] , [81] ) . Consistent with a model in which cAMP signaling is elevated in the ssn3 mutant , HSP12 and CTA1 , two cAMP repressed genes [82]–[84] were found to be significantly lower in the ssn3 mutant in comparison to the wild type ( Table S3 ) . The next steps in this research will be to understand how loss of Cdk8 function influences the transcription of these and related genes . As our data indicate that metabolic activity is de-repressed upon mutation of any of the four Cdk8 module subunits or the loss of kinase activity , we propose that Ssn3 might be negatively regulating a transcriptional repressor by decreasing its stability or activity upon phosphorylation , or positively regulating a factor involved in metabolic downregulation [85] . Upon future discovery of direct targets of Cdk8 module activity , we will also be able to determine if changes in Cdk8-regulation contribute to morphological transitions and biofilm formation . In S . cerevisiae , the Cdk8 module has been shown to control both metabolic pathways and morphology suggesting that connections between morphology and metabolism may be common across fungi [36] , [86]–[88] . We previously reported that PYO inhibits both wrinkling and alkalinization in a wild type strain of C . albicans , while its thioanalogue MB only inhibits wrinkling [25] . Using the Cdk8 mutants , we uncovered further evidence that all respiratory inhibitors do not exert the same effect on the behavior of C . albicans . Specifically , we show that the effect of MB or AA on C . albicans was not identical to that of PYO , as MB did not have a marked effect on alkalinization while the Cdk8 module mutants exhibited a growth defect upon treatment with AA , likely due to AA-induced generation of ROS [89] . Our observation that the Cdk8 mutants have markedly reduced growth in the presence of AA , which inhibits transfer of electrons from cytochrome b to cytochrome c of the electron transport chain without affecting other respiratory chains [89] , suggests these alternate electron transport chains may be affected by absence of Ssn3 . Taken together , the variation in response to PYO , MB and AA suggests C . albicans responds differently to different respiratory inhibitors in terms of pH , morphology and overall growth . While catabolism of amino acids promotes hyphal growth and wrinkled colony formation by raising the extracellular pH [66] , and the ssn3 mutant was found to be a hyperalkalinizer ( Figure 5 and Figure S4C ) , the ability of the ssn3 mutant to filament in the presence of PYO was not attributable to medium pH differences ( Figure S4A ) . In addition , the ssn3 mutant was also resistant to PYO in the absence of amino acids suggesting that resistance to PYO was not due to differences in amino acid catabolism . It is , however , interesting to consider how the Cdk8 module may impact amino acid metabolism . Our metabolomics analyses found that the loss of Ssn3 impacted levels of various metabolites involved in the amino acid super pathway . Of the twenty key amino acids , only three—leucine , isoleucine and phenylalanine—were present at lower levels in the mutant compared to the control strains and 55% of the downregulated peptides contained these amino acids ( Table S4 ) . The biosynthesis of leucine and isoleucine in S . cerevisiae is controlled by the transcription factor Leu3 [90] , [91] . Interestingly , Leu3 has been identified as a C . albicans pro-biofilm transcription factor , whose absence results in decreased transcription of hypha-specific genes encoding hyphal wall protein 1 ( HWP1 ) and the agglutinin-like sequence protein 3 ( ALS3 ) [92] , [93] . Negative regulation of Leu3 by Ssn3 kinase activity could result in altered metabolite pools and increased expression of these genes , which could aid in explaining the hyperwrinkly and hyperbiofilm phenotypes of the ssn3 mutant . Future studies will be required to explore relationships between Leu3 or other transcription factors involved in amino acid metabolism and the Cdk8 module . By virtue of the fact that the Mediator complex is a co-regulator of transcription , its function will change based on the transcription factors that are active in a given condition . This may explain why the srb9 mutant exhibits a filamentation and biofilm defect in nutrient-rich media [94] , but not in liquid or in the defined medium used in these studies . Furthermore , while all four Cdk8 module mutants yielded identical phenotypes in our studies ( Figure 2 and Figure S1 ) and in some S . cerevisiae studies , there are instances where mutation of the individual components reveal differences [38] , [46] , [65] , [94] . The difference between the enhanced biofilm formation we observed in the ssn3 mutant compared to the decreased biofilm formation in the srb9 mutant observed by Uwamahoro and colleagues [94] may reflect such a difference as biofilm formation is a complex trait involving the expression of many genes . An understanding of these differences will require an analysis of the specific transcription factors with altered activity upon the alteration of Cdk8 function . All glycolysis genes that were found to be upregulated in the absence of Ssn3 ( Figure 6A ) are reportedly regulated by both Tye7 and Gal4 [95] , and future studies will determine if these transcriptional regulators are controlled in part by some or all components of the Cdk8 module of Mediator . Cdk8 module mutants have previously been shown to be more susceptible to hydrogen peroxide [46] , [94] . Our metabolomics study revealed that the ssn3 mutant had lower levels of PPP intermediates and their derivatives , and this pattern could reflect an altered ability to cope with oxidative stress because the PPP is an important source of NADPH for glutathione reduction during reactive oxygen species ( ROS ) exposure . Using the ratio of reduced glutathione ( GSH ) to oxidized glutathione ( GSSG ) as measure of redox stress [96] , [97] , we found no significant differences between the wild type , ssn3 mutant , and the complemented derivative in control conditions . When the GSH∶GSSG ratios for wild type in control cultures versus those with PYO were compared , a large shift was evident ( 5 . 1±1 . 0 SD versus 0 . 56±0 . 07 SD ) , consistent with ROS being generated from PYO directly or upon altered respiration . Furthermore , the wild type had a significantly higher GSH∶GSSG ratio than the ssn3 mutant grown with PYO ( 0 . 56±0 . 07 SD versus 0 . 32±0 . 08 , p<0 . 01 , n = 5 ) ( Table S2 ) indicating that the mutant metabolism may confer a disadvantage during stress adaptation . This is in agreement with our finding that the mutants exhibit increased sensitivity to AA which has previously been shown to correlate with increased production of ROS and oxidation of glutathione [76] . There is a growing body of evidence showing that Ssn3 contributes to virulence in important ways . First , mutation of SSN3 increased catheter-biofilm formation and increased resistance to the inhibitory effects of PYO on biofilm formation . These mutants may be useful in future studies to understand the link between metabolism and resistance to other agents such as antifungals . Second , the ssn3 mutant has decreased virulence in a murine model [56] . Future studies will determine if this is due to altered in vivo metabolism , morphology or both . It is likely that , as in many cases , the phenotypes related to defects in the Cdk8 module are complicated , which is in accordance with the published reports that hundreds of genes can be impacted by this module of the Mediator complex in species from yeast to man [38] . Due to its ability to modulate gene regulation by altering the ability of multiple transcription factors to interact with RNA polymerase , the Cdk8 module in humans is currently being investigated as a target for chemotherapy drugs [98]–[100] . The possibility of influencing various cellular pathways in eukaryotic cells renders the Cdk8 module an attractive target for novel antifungals . Thus , future studies on the similarities and differences between human and fungal Mediator are needed .
The C . albicans strains used in this study are described in Table S5 . All strains were streaked from −80°C onto YPD ( 1% yeast extract , 2% peptone , 2% glucose ) plates every 8 days . Overnight cultures were grown in 5 ml of YPD , supplemented with uridine as indicated . Cultures were incubated at 30°C in a roller drum for 14 h , unless stated otherwise , and washed in distilled water ( dH2O ) prior to subsequent use . Stock solutions of pyocyanin ( PYO , Cayman Chemicals ) , bromocresol purple ( Sigma ) , bromocresol green ( Sigma ) and antimycin A ( AA , Sigma ) were prepared at 30 mM , 5% , 5% and 100 µM respectively in 100% ethanol . The methylene blue ( MB , Fisher ) stock solution of 3 mM was prepared in dH2O . All experiments that included PYO , AA or MB were conducted in the dark . For wrinkled colony formation , cells from overnight cultures resuspended in dH2O at an optical density at 600 nm ( OD600 ) of 0 . 03 were spotted onto 3 ml of 2% agar containing 0 . 67% yeast nitrogen base medium with ammonium sulfate ( YNB-agar; RPI Corp ) supplemented with 10 mM glucose ( YNBG10-agar ) , 5 mM N-acetylglucosamine ( GlcNAc; YNBG10N-agar ) and , where applicable , 2% [w/v] casamino acids ( YNBAG10N-agar; BD Bacto ) . In the presence of amino acids ( YNBAG10N ) , the medium was adjusted to an initial pH of 5 . 0–5 . 4 , where any phenotype resulting from an increase in pH could be attributed to amino acid catabolism; when amino acids were excluded ( YNBG10N ) , the medium was buffered to pH 7 using 1 M phosphate buffer ( YNBG10NP ) , in order to assess the effects of high pH alone on morphology . The medium was amended with a final concentration of 20 µM PYO or 500 nM MB from a 30 mM or 3 mM stock solution respectively . AA was added at 75 , 150 and 300 nM from the 100 µM stock while MB was added at 0 . 5 and 1 µM from the 3 mM stock solution . An equivalent volume of 100% ethanol ( vehicle ) was used as the vehicle control for PYO and AA , while water was used for assays including MB . An equivalent volume of 100% ethanol ( vehicle ) was used as the vehicle control for PYO and AA , while water was used for assays including MB . For those experiments designed to determine the effect of PYO or MB on extracellular pH , bromocresol purple or bromocresol green was added to the molten agar at 0 . 01% [v/v] of the medium . In all cases , cells were incubated at 37°C . The above medium conditions are referred to as “inducing” conditions . “Non-inducing conditions” were used for smooth ( yeast ) colony growth . For these assays , cells from overnight cultures re-suspended in dH2O were spotted onto YNB-agar supplemented with 100 mM glucose ( YNBG100-agar ) . This higher concentration of glucose , compared to the 10 mM used under inducing conditions , was utilized due to the absence of amino acids as an alternative carbon source , longer incubation times , or a combination thereof . The filamentation inducer GlcNAc was excluded from experiments conducted under non-inducing conditions . To assess the effect of PYO on extracellular pH under non-inducing conditions , cells were spot onto YNBAG10-agar to which bromocresol purple was added . YNBG100-agar and YNBAG10-agar were adjusted to pH 5 . 0–5 . 4 , and cells were incubated at 30°C . As of this point , all incubations completed at 30°C to stimulate yeast growth are referred to as “non-inducing” . As stated , colonies were imaged after 48 h with a Nikon SMZ1500 dissecting stereoscope at ×7 . 5 magnification or a digital camera . Unless otherwise noted , all spot assays were completed as at least three independent replicates and a representative data set is shown . A spot assay for wrinkled colony formation was used to screen approximately 1 , 500 mutants obtained from the Noble [42] and Mitchell [43]–[46] collections to identify PYO-resistant strains . Strains were grown under ( 1 ) inducing conditions ( YNBAG10N-agar ) in the presence of 20 µM PYO or an equivalent volume of vehicle and ( 2 ) non-inducing conditions ( yeast growth; YNBG100-agar ) in the absence of treatment for 48 h . A resistance phenotype was designated as the ability to wrinkle in the presence of PYO under inducing conditions without exhibiting wrinkling under non-inducing conditions . Histidine prototrophy was restored to the ssn3 and ssn8 mutants by transformation with EcoRI/NotI linearized pDDB78 plasmid ( Table S5 ) . SSN3 and SSN8 mutations in the auxotrophic strains were complemented with the respective native alleles , concomitant with restoration of histidine prototrophy , as described by Blankenship et al . [46] using the plasmids and primers shown in Tables S5 and S6 . C-terminal tagging of Candida albicans proteins with 3X hemagglutinin ( HA ) epitope was performed as previously described [101] . Briefly , to construct SSN3-3HA/SSN3-3HA ( DH2209 ) , the two copies of SSN3 were sequentially 3XHA tagged by integrating DNA cassettes amplified by the primers of SSN3 tag sense/SSN3 tag anti from pFA-3XHA-HIS1 and pFA-3XHA-SAT1 in frame with the 3′ end of the SSN3 ORF . To construct SSN8-3HA/SSN8-3HA ( DH2216 ) , the two copies of SSN8 were 3XHA tagged by integrating DNA cassettes amplified by the primers of SSN8 tag sense/SSN8 tag anti from pFA-3XHA-HIS1 and pFA-3XHA-ARG4 in frame with the 3′ end of SSN8 ORF . Both strains had wild-type sensitivity to PYO under wrinkling-inducing conditions . Overnight YPD cultures ( supplemented with 0 . 2 mM uridine ) were washed , re-suspended and inoculated to an OD600 of 0 . 05 into YNBG10NP supplemented with uridine , arginine and histidine ( 0 . 67% YNB , 0 . 2% glucose , 5 mM GlcNAC 25 mM phosphate buffer pH 7 . 0 , 0 . 2 mM uridine , 20 mg/L histidine , 20 mg/L arginine ) treated with 20 µM PYO or an equivalent volume of vehicle . Cells were grown at 37°C ( 175 rpm ) for 6–7 hours before collection . In this period , cultures underwent approximately 3 . 5 cell divisions , which was estimated by the 10-fold cell density increase ( from OD 0 . 05 to OD 0 . 5 ) of parallel cultures grown in YNBG10 ( YNBG10N without GlcNAC ) at 30°C . At this time , approximately 5 OD of cells ( ≈10 ml ) were collected , washed in cold water , pelleted in 1 . 5 ml screw-top tube and flash-frozen in liquid nitrogen . To lyse the cells , the pellets were re-suspended in 50 µl ESB ( 80 mM Tris-HCl pH 6 . 8 , 2% sodium dodecyl sulfate ( SDS ) , 1 . 5% dithiothreitol , 10% glycerol and 0 . 1 mg/ml bromophenol blue ) , boiled for 3 minutes then bead-beaten for 4 minutes by mini-beadbeater ( BIOSPEC ) . An additional 70 µl of ESB buffer was added to each sample , which was then boiled for an additional 2 minutes . Approximately 5 µl of the resultant whole cell extract was resolved by 10% SDS polyacrylamide gel electrophoresis ( SDS-PAGE ) , transferred to a polyvinylidene fluoride ( PVDF ) membrane ( Millipore ) and probed by rat anti-HA antibody ( Roche ) . Goat anti-rat IgG AP conjugate ( Santa Cruz ) was used as the secondary antibody . The signal was developed against enhanced chemifluorescence substrate ( General Electric ) and scanned using Typhoon Molecular Imager ( Molecular Dynamics ) . ECF substrates for developing HA signals were washed away by following the PVDF membrane manual ( Millipore ) and the same membrane was re-probed by rat anti-tubulin alpha ( AbD SeroTec ) and goat anti-rat IgG AP conjugate to monitor tubulin contents . Western blot signal volume was quantified by ImageQuant ( Molecular Dynamics ) and HA/Tubulin signal ratio of each PYO treated sample was normalized against its untreated control . Spot assays were completed as previously described on YNBG10NP-agar and incubated at 37°C for 24 h . Cells were harvested , by scraping colonies from the surface of the agar using a coverslip , and then snap-frozen in an ethanol/dry ice bath . A total of 5 biological replicates were submitted to Metabolon for metabolite profiling , by GC/MS and LC/MS , of vehicle-treated wild type , ssn3Δ/Δ and ssn3Δ/Δ+SSN3 as well as 20 µM PYO-treated wild type and ssn3Δ/Δ . Cells from exponential phase cultures were washed and then inoculated to an OD600 of 0 . 05 in 5 ml YNBAG10P . Cultures were incubated at 30°C in a roller drum for 3 h , pelleted and then re-suspended in 5 ml of dH2O . The cell suspension was added at an equal volume to a microtiter dish containing 2X YNBAG10P . Absorbance was measured at 30°C every hour for 6 h using a SpectraMax M5 spectrophotometer . After 6 h had elapsed , 200 µl of the yeast culture was removed from the microtiter dish for filtration and HPLC analysis using the protocol previously described by Morales and colleagues [25] . Two independent experiments with 2–3 biological replicates were performed and the combined results , normalized to OD600 , are shown . Overnight cultures re-suspended in dH2O were pelleted and then re-suspended in a volume of YNBG100P equivalent to the original volume of dH2O . The OD600 was then measured and 100 µl of fresh medium was inoculated to a final OD of 0 . 1 in a microtiter dish . Next , 10 µl of alamarBlue ( AB; Invitrogen ) was added to each well . Where applicable , PYO was added at a final concentration of 20 or 100 µM and vehicle was added at the same volume as the latter concentration . Duplicate wells were prepared to which dH2O was added instead of AB . The absorbance of the cultures at 30°C was measured at 570 and 600 nm , as recommended by the manufacturer , every 1 . 5 h over a 6 h period using a SpectraMax M5 spectrophotometer ( Molecular Devices ) . The reduction of AB was calculated as described by the manufacturer and all data were normalized to the OD600 of cultures grown in the absence of AB . The data shown are representative of at least three independent replicates that each included three technical replicates . Overnight cultures re-suspended in dH2O were pelleted and then re-suspended to an OD600 of 0 . 3 in 5 ml of YNBAG10NP and treated with either 20 µM PYO or an equivalent volume of vehicle . Cultures were incubated at 37°C ( inducing ) . After 6 h had elapsed , ATP levels were measured using a CellTiter-Glo Luminescent Cell Viability Assay ( Promega ) . The luminescent signal , which is proportional to ATP levels , was measured using a Tecan Infinite 200 Pro equipped with Magellan software ( Tecan ) . All data were normalized to the OD600 at 6 h . Three independent replicates , each including three technical replicates , were conducted and a representative data set is presented . For ATP measurements under non-inducing conditions , strains were cultured in YNBG100P . Colonies grown under wrinkling inducing conditions for 24 h on unbuffered YNBAG10N-agar were harvested using the protocol previously described for the metabolomics study . Immediately afterwards , RNA isolation , DNase treatment , cDNA synthesis and RT-PCR were carried out as previously described by Davis-Hanna and colleagues [82] , except cDNA was synthesized using 1 µg of RNA . Transcript levels were measured for HGT2 and HXT5 , which encode glucose transporters [102] , [103]; HXK2 , PGI1 , PFK1 , PFK2 , FBA1 , TPI1 , PGK1 , GPM1 , GPM2 , ENO1 and CDC19 which encode glycolytic enzymes; and HSP12 and CTA1 which encode oxidative stress response genes , using a 7500 Fast Real-time PCR System ( Bio-Rad ) with the following thermocycler conditions: 95°C for 3 min , 95°C for 10 s , 58°C for 30 s for 34 cycles . A total of two biological replicates were completed and each included three technical replicates . All transcript levels were normalized to PMA1 which encodes a plasma membrane protein . All primers are shown in Table S6 . Biofilms were grown on silicone squares ( Bentec Medical , Inc . ) using the protocol described by Desai and colleagues [104] , except YNBG10P and treated with 20 µM PYO or an equivalent volume of vehicle was used as the growth medium . Biomass of biofilms was determined as previously described [105] .
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Candida albicans is currently one of the most common causes of nosocomial infections , and causes diseases ranging from oral thrush to life-threatening systemic infections . C . albicans readily forms biofilms on implanted devices , such as catheters and dentures , and biofilms are associated with increased risk of systemic infections and resistance to antifungals . We previously showed that biofilm formation positively correlates with oxidative metabolism . Here , we show that respiration , wrinkled colony formation , biofilm formation , and resistance to the inhibitory effects of pyocyanin were increased when the Cdk8 module of Mediator , a co-regulator of transcription , was compromised . Cdk8 module mutants all exhibited differences in basal metabolism that were indicative of increased metabolic activity . Furthermore , using these strains , we showed a direct correlation between the inhibition of respiratory activity , in the absence of growth defects , and inhibition of biofilm formation , supporting a model in which C . albicans metabolism and morphology are linked . The human Cdk8 module is currently being investigated as a target for cancer chemotherapy , and thus an understanding of the consequences or potential benefits of inhibiting this module is required .
|
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2014
|
Analysis of Candida albicans Mutants Defective in the Cdk8 Module of Mediator Reveal Links between Metabolism and Biofilm Formation
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In chordates , neural induction is the first step of a complex developmental process through which ectodermal cells acquire a neural identity . In ascidians , FGF-mediated neural induction occurs at the 32-cell stage in two blastomere pairs , precursors respectively of anterior and posterior neural tissue . We combined molecular embryology and cis-regulatory analysis to unveil in the ascidian Ciona intestinalis the remarkably simple proximal genetic network that controls posterior neural fate acquisition downstream of FGF . We report that the combined action of two direct FGF targets , the TGFβ factor Nodal , acting via Smad- and Fox-binding sites , and the transcription factor Otx suffices to trigger ascidian posterior neural tissue formation . Moreover , we found that this strategy is conserved in the distantly related ascidian Phallusia mammillata , in spite of extreme sequence divergence in the cis-regulatory sequences involved . Our results thus highlight that the modes of gene regulatory network evolution differ with the evolutionary scale considered . Within ascidians , developmental regulatory networks are remarkably robust to genome sequence divergence . Between ascidians and vertebrates , major fate determinants , such as Otx and Nodal , can be co-opted into different networks . Comparative developmental studies in ascidians with divergent genomes will thus uncover shared ascidian strategies , and contribute to a better understanding of the diversity of developmental strategies within chordates .
Neural tissue formation is a multi-step process through which embryonic cells acquire a neural phenotype . In vertebrate central nervous system ( CNS ) development , the first step is called neural induction . Naive ectodermal cells undergo a binary fate decision between epidermis and neural tissue in response to endomesodermal signals that modulate the FGF , BMP and Wnt signaling pathways [1]–[3] . While there may be variations between species , BMP inhibition together with FGF signaling activation are key events in neural induction . Concomitantly or following neural induction , neural tissue is patterned along the antero-posterior and medio-lateral axes . Acquisition of a differentiated neural phenotype involves further processes such as stabilization and reinforcement of the neural fate , specification of cellular identity and progression towards final differentiation . Each of these steps is controlled by complex mechanisms involving a variety of molecular players [4]–[6] . Non-vertebrate chordates include ascidians ( tunicates ) and amphioxus ( cephalochordates ) . They form prototypical tadpole-like larvae with a dorsal hollow neural tube patterned similarly to vertebrates [7] , [8] . The embryological process of neural induction also takes place in these animals but our current knowledge does not provide a unified view . In amphioxus , BMP activation represses neural tissue formation but FGF inhibition does not abolish neural tissue formation [9] , [10] . In ascidians by contrast , FGF is essential for neural induction while BMP inhibition does not seem to be involved [11] , [12] . Comparative embryology within each of these groups and with vertebrates provides an outstanding opportunity to assess the diversity of regulatory strategies leading to a common shared body plan and to test models of gene regulatory network evolution proposed in other bilaterian groups [13] , [14] . In this context , ascidians can be regarded as interesting chordate evolutionary outliers with unique developmental and genomic features . Their mode of development , based on small cell numbers and invariant cell lineages , diverges markedly from that found in vertebrates and amphioxus [15] . In addition , ascidians also display a fast rate of evolution with extensive genome rearrangements and compaction as well as gene losses [16] , [17] . Ascidian genomes are thus very different from other chordate genomes ( for example , synteny and ultra conserved elements conserved between vertebrates and amphioxus are not found in ascidians ) [18] , [19] . Finally , the high conservation of ascidian cell lineages throughout ascidian groups allows the comparison of genomically divergent ascidian embryos with a cellular level of resolution [20]–[22] . The dorsal hollow neural tube of the ascidian larva is composed of three morphologically distinct regions: the sensory vesicle anteriorly , the visceral ganglion and the tail nerve cord posteriorly ( Figure 1 ) . While there are still debates on their precise homology to vertebrate CNS domains , they are thought to be equivalent to fore/midbrain , hindbrain and spinal cord respectively [23] , [24] . The ascidian CNS has a dual origin and specification logic ( reviewed in [25] ) . Three separate lineages , named according to the founding blastomeres of the 8-cell stage embryo , form the ascidian CNS ( Figure 1 ) . The A-line neural lineage originates from vegetal blastomeres and gives rise to the posterior part of the sensory vesicle and to the ventral and lateral parts of both visceral ganglion and tail nerve cord . Ectodermal blastomeres give rise to the anterior part of the sensory vesicle ( a-line ) and to the dorsal part of the visceral ganglion and tail nerve cord ( b-line ) . While A-line CNS is specified autonomously [26] , a- and b-line are specified through neural induction by FGF9/16/20 secreted from the vegetal hemisphere at the 16- to 32-cell stage transition [11] , [12] , [27] , [28] . Early target genes including Otx , Nodal , Elk and Erf are expressed at the 32-cell stage in all or part of the neural precursors ( a6 . 5 and b6 . 5 blastomeres; Figures 1 and S2 ) where ERK signaling is active [11] , [29] , [30] . Interestingly , each of these precursors also contributes to the peripheral nervous system ( PNS ) following FGF9/16/20 induction [31] , [32] . For example , the b6 . 5 blastomere gives rise to the dorsal midline of the tail epidermis , a neurogenic territory from which the epidermal sensory neurons of the PNS form ( Figure 2A ) . Beside the requirement of Otx for anterior neural tissue formation [33] and the key role of Nodal in A-line CNS patterning and formation of the b6 . 5 derivatives [23] , [29] , [32] , [34] , [35] , little is known for the function of these immediate target genes in neural fate acquisition or stabilization . In order to gain insights into post-neural induction events , we focused our attention on the regulation of Msxb and Delta2 , markers of the progeny of the b6 . 5 blastomeres . Both genes are expressed from the 64-cell stage ( after neural induction ) in the b6 . 5 progeny ( b7 . 9 and b7 . 10 blastomere pairs; Figure 2A and [36] , [37] ) and are required for further specification and differentiation of these progenitors . Msxb is a marker of the entire b6 . 5 lineage until neurula stages , and is required for tail dorsal epidermal midline and dorsal nerve cord formation [23] , [35] . Delta2 is involved in the specification of epidermal sensory neurons within the epidermal midline [32] , [38] . In this study , we show that FGF signaling is necessary and sufficient for b6 . 5 fate acquisition in posterior ectoderm . Downstream of FGF , Nodal is necessary for b6 . 5 fate . Although it cannot induce neural tissue on its own , it is sufficient to posteriorize FGF-induced neural tissue . This led us to search for other factors acting with Nodal downstream of FGF . We uncovered a critical function for the transient expression of Otx in posterior neural fate acquisition . Using this simple model of regulation , we were able to isolate b6 . 5 lineage specific enhancers for both Msxb and Delta2 . We further show that this mode of regulation is shared with the distantly related ascidian Phallusia mammillata , strengthening our proposal that Otx , a well known regulator of anterior neural tissues in many metazoans , has been co-opted in ascidians for posterior nervous system formation .
Previous reports indicated that induced b6 . 5 fates are lost after abolition of FGF signaling [11] , [28] , [35] . We extended these results using a pharmacological inhibitor of FGF/MEK signaling ( U0126 ) , three early markers of b6 . 5 progeny ( Msxb , Delta2 and Chordin ) and two tailbud markers of dorsal tail epidermis midline and dorsal nerve cord , Klf1/2/4 and KH . C7 . 391 respectively ( Figures 2 and S1 ) . MEK inhibition led to a conversion of neural b6 . 5 progenitors into epidermis as demonstrated by the loss of expression of all neural markers , coupled to the ectopic expression of the epidermal marker Ap2-like2 at gastrula stages ( Figure S1 ) . Previous reports indicated that activation of the FGF pathway in explanted ectodermal precursors leads to the induction of neural fate in cells normally fated to form epidermis , with different neural fates achieved in a-line and b-line blastomeres [11] , [12] , [27] , [32] . We confirmed that this was also the case in whole embryos . We treated whole embryos either with recombinant FGF protein from the 16-cell stage or overexpressed FGF9/16/20 by electroporation using the pFOG driver ( expressed from the 16-cell stage throughout the entire ectoderm [39] ) . As expected , the epidermis marker Ap2-like2 was strongly down-regulated throughout the ectoderm ( data not shown ) . The posterior neural markers Nodal , Msxb and Delta2 were ectopically expressed throughout the posterior ectoderm ( b4 . 2 lineage or b-line ectoderm ) , and the anterior neural marker Dmrt1 was activated throughout the anterior ectoderm ( a4 . 2 lineage or a-line ectoderm ) ( Figures 3A-H and S2 ) . Chordin , which is normally expressed in the progeny of b6 . 5 as well as in a8 . 26 and a8 . 28 blastomere pairs ( Figure 3C ) , was expressed throughout the posterior ectoderm and in part of the anterior ectoderm in response to ectopic FGF treatment ( Figures 3C and 3G ) . Nodal activation at the 32-cell stage was a likely direct consequence of FGF signaling . FGF treatment activated Nodal ectopic expression in the presence of protein synthesis inhibitor ( Figures S2 ) , suggesting the absence of a transcriptional relay . In addition , a previously identified b6 . 5-specific Nodal enhancer has the same regulatory logic as the FGF-responsive enhancer of the direct FGF target gene Otx [30] . Msxb , Delta2 and Chordin are more likely to be indirect targets of FGF as they are activated later at the 64-cell stage . In the following sections , we will precisely define the regulatory interactions between FGF , Nodal , Otx , Msxb , Delta2 and Chordin in the b6 . 5 lineage . To determine the function of Nodal during b6 . 5 fate acquisition , we blocked the function of its receptor with the pharmacological inhibitor SB431542 or overexpressed the Nodal antagonist Lefty in the ectoderm using electroporation . Both perturbations led to a loss of expression of Msxb , Delta2 and Chordin in b-line neural lineage at gastrula stages ( Figures 2B , 3I-K and S1 ) . At later stages , expression of the dorsal tail nerve cord marker KH . C7 . 391 was lost , as was the dorsal expression of the tail midline marker Klf1/2/4 ( Figure 2B ) . This altered genetic program was similar to that obtained in response to FGF inhibition , suggesting that Nodal acts downstream of Fgf9/16/20 in b-line neural specification ( Figure 2C ) . Consistent with this , FGF-induced ectopic activation of Msxb , Delta2 and Chordin was suppressed by Lefty overexpression ( Figure 3M-O ) . Nodal was however not the sole mediator of FGF action , as its inhibition was not sufficient to convert the b6 . 5 progeny into epidermis , marked by Ap2-like2 expression ( Figure S1 ) . We next overexpressed Nodal throughout the ectoderm using the pFOG driver and analyzed marker expression in the a- and b-line ectoderm . Ectopic expression of Chordin was observed throughout the ectoderm ( Figure 3S ) , independently of the FGF induction status of the cells . Ectopic Chordin expression was stronger in a-line ectoderm , possibly reflecting the stronger levels detected in a8 . 26 and a8 . 28 blastomeres compared to b6 . 5 progeny in control embryos ( Figure 3C ) . By contrast , we did not detect ectopic activation of Msxb and Delta2 in posterior ( b-line ) ectoderm ( Figure 3Q , R ) . However , anterior neural tissue precursors ( a6 . 5 lineage ) ectopically expressed these two genes ( Figure 3Q , R ) and had reduced Dmrt1 expression ( Figure 3T ) . These data indicate that anterior neural precursors adopted a posterior identity in response to Nodal expression . Consistent with these observations , co-electroporation of pFOG-FGF9/16/20 and pFOG-Nodal , led to the induction of posterior neural tissue in anterior ectoderm , demarcated by the ectopic activation of both Msxb and Delta2 and by the repression of Dmrt1 ( Figure 3U , V and X ) . The results of this section indicate that Nodal alone is required , though not sufficient , to induce neural tissue and that it can posteriorize FGF-induced neural tissue . Interestingly , expansion of the anterior neural marker Dmrt1 to posterior b-line territories was not observed following Nodal signaling inhibition in either wild type or FGF-induced contexts ( Figure 3L , P ) . These results are consistent with the presence of a Nodal-independent factor necessary for Dmrt1 expression and anterior neural fate acquisition in a-line ectoderm [40] , [41] ( see discussion ) . In summary , three genes expressed downstream of FGF in the b6 . 5 progeny show different requirements regarding Nodal signaling: Chordin can be activated in the entire ectoderm while Msxb and Delta2 are positive targets of Nodal solely in FGF-induced neural cells . The conversion of a6 . 5 anterior neural precursors into posterior neural fate upon ectopic activation of Nodal signaling ( Figure 3 Q , R ) suggests that posterior neural fates may result from the cooperation of Nodal with another FGF-target . Otx is a conspicuous candidate since it is expressed in all neural precursors downstream of FGF signaling ( Figure S2 ) and is coexpressed with Nodal in posterior neural precursors marked by Msxb and Delta2 expression [11] , [27] ( Figures 2 , 3 and S2 ) . We first tested the requirement of Otx in b6 . 5 fate acquisition by injecting a specific translation-blocking morpholino antisense oligonucleotide ( MO ) . Otx morpholino injection led a full loss of Msxb and Delta2 expression at stage 10 ( Figure 4C , F ) . The resulting embryos displayed gastrulation and neurulation defects reminiscent of FGF or Nodal signaling inhibition . The tail midline marker Klf1/2/4 was strongly affected ( Figure 4I ) . Dorsal tail epidermis midline staining originating from b6 . 5 was abolished while posterior-most staining ( originating from b6 . 6 lineage ) was maintained . Ventral midline expression was also kept but the domain of expression appeared reduced in size . Dorsal tail nerve cord did not form either as revealed by the loss of the marker KH . C7 . 391 ( Figure 4J ) . We obtained similar results by overexpressing a dominant negative form of Otx , OtxHDenR ( a fusion protein between the Otx homeodomain and the repressor domain of Engrailed ) [42] in the ectoderm ( Figure S4 ) . The phenotypes appeared milder probably because OtxHDenR was only targeted to the ectoderm and because of the mosaic inheritance of the transgene introduced by electroporation . In addition , we observed that expression of the epidermal marker Ap2-like2 was unchanged following overexpression of OtxHDenR ( Figure S4 ) . Similarly to what has been observed for Nodal inhibition , b-line neural lineage did not form neural tissue upon Otx loss-of-function but did not form epidermis either . We next tested the effect of Otx overexpression using the pFOG driver . Although we expected that Otx would need to cooperate with Nodal to activate Msxb and Delta2 , Otx overexpression was sufficient to activate both of these latter genes throughout the ectoderm ( Figure 4B , E ) . When we overexpressed simultaneously Otx and Nodal throughout the ectoderm , we simply observed an addition of each molecule effect with no increase in the number of embryos ectopically expressing Msxb and Delta2 in the ectoderm ( data not shown ) . To better understand these results , we further explored possible transcriptional interactions between Nodal and Otx that may control maintenance of their expression following the initial induction by FGF ( Figure S2 ) . We detected robust activation of Nodal expression at the 64-cell stage when Otx was ectopically expressed ( Figure S3Aii ) . Accordingly , Nodal expression was repressed by the overexpression of OtxHDenR ( Figure S3Aiii ) . This interaction between Otx and Nodal was not reciprocal , since Otx expression was not changed upon modulation of Nodal signaling ( Figure S3Avi , vii ) . Nodal signaling inhibition also prevented Nodal expression ( Figure S3Aiv ) , suggesting the existence of an autoregulatory loop on Nodal similarly to what has been described in vertebrates [43] . The ectopic activation of Msxb and Delta2 in the ectoderm by Otx overexpression did not require the activation of Nodal , as overexpression of Lefty did not significantly block Otx effect ( Figure S3B ) . By contrast , Nodal-mediated ectopic expression of Msxb and Delta2 in anterior neural precursors was inhibited by OtxHDenR overexpression ( Figure S3C ) . These data demonstrate that Otx is an essential regulator of b6 . 5 lineage derived posterior neural tissue formation . Figure 4K provides a schematic representation of the gene regulatory network acting downstream of FGF in b-line ectoderm . We next used the above functional evidence to isolate cis-regulatory DNA regions responsible for neural marker expression in the b6 . 5 lineage . We reasoned that the enhancer responsible for b6 . 5 lineage expression should integrate both Otx and Nodal inputs . Nodal is a ligand which controls gene expression through the activation of the Smad2/3 nuclear effector . A Smad2/3/Smad4 complex can directly bind DNA with low affinity through poorly defined GC rich regions or through ( C ) AGAC Smad Binding Element ( SBE ) consensus sequences [44] . However , high affinity binding is usually achieved through association with a DNA binding cofactor . In several instances , Fox transcription factors have been shown to fulfill this function [44]–[46] . We consequently searched the Msxb locus for the co-occurrence of Otx and Fox/Smad binding sites . We selected the core consensus sequences GGATTA for Otx , TGTTT for Fox from the Jaspar database [47] , and AGAC for Smad [44] . We searched for regions enriched in Otx- , Fox- and Smad- core binding site motifs by first scanning , in Ciona intestinalis type A [48] , the 50 kb genomic region that includes Msxb up to its two flanking genes . We arbitrarily chose a 300 bp window and found 15 regions that contained at least one of each motif . To reduce the number of candidates we increased the stringency by increasing the number of the least frequent site , which is Otx . We chose a more degenerate site for this additional motif , GATTA , as in [42] . Adding one or two GATTA motifs yielded 7 and 4 candidate regions , respectively . We focused on the latter 4 regions and searched whether the Ciona savignyi orthologous regions harbored a similar combination of binding sites using Vista suite [49] . A single region matched this criterion and was named “msxb-b6 . 5 line” according to its enhancer activity ( see below ) ( Figure 5 ) . This region is located just upstream of Msxb on a peak of conservation and contains 6 putative Otx , 5 putative Fox binding sites and 6 putative SBEs ( Figures 5A , B and S5 ) . This region falls within a region bound in vivo by Otx at early gastrula stages as revealed by ChIP-on-Chip experiment ( Figure 5B ) [50] . We amplified this 707 bp fragment from C . intestinalis type B genomic DNA . The sequence obtained is very similar to the reference type A sequence but contains only 4 Fox binding sites and 5 SBEs ( Figure S5 ) . Placed upstream of the minimal promoter of Fog and the reporter gene LacZ [39] , [51] , this fragment drove transcription throughout b6 . 5 derivatives from the early gastrula stage ( Figure 5C-E and Table S1 ) . Thus , searching for enrichment in Otx , Fox and Smad putative binding sites in conserved non-coding genomic DNA was sufficient to isolate a region , which binds Otx in vivo at the early gastrula stage and is transcriptionally active in posterior neural precursors . The same logic led to the identification of a Delta2 enhancer active in the b6 . 5 lineage . A single genomic region at the Delta2 locus harbored a combination of Otx , Fox and SBE sites within 300 bp in both C . intestinalis and C . savignyi and was named “delta2-b6 . 5 line” ( Figure 5 ) . This 392 bp long region is located within 2 kb upstream of Delta2 , harbors a strong level of conservation , contains 5 Otx sites , 3 Fox sites and 3 SBEs; and is bound in vivo by Otx ( Figures 5F-G and S6 ) . When electroporated in C . intestinalis embryos it drove expression in b6 . 5 derivatives from early gastrula stages ( Figure 5H-J and Table S1 ) . Overall , these results indicate that Msxb and Delta2 share similar regulatory motifs in their enhancers . We next assayed the relative contribution of Otx , Fox and Smad binding motifs to enhancer activity in the b6 . 5 lineage , focusing on the “msxb-b6 . 5 line” enhancer . Progressive shortening of this region on both sides ( Figure S7 and Table S1 ) identified an active 273 bp long fragment ( msxb-B ) containing 3 Otx binding motifs , 2 overlapping Fox binding motifs and 4 Smad motifs ( Figure 6A-B ) . This fragment was still active in inverted orientation ( Msxb-B-inv ) , as expected from an enhancer ( Figure S8B ) . Msxb-B enhancer activity was abolished when the Otx morpholino was injected and when Lefty was overexpressed ( Figure 6B-D ) . Simultaneous mutation of the 3 Otx sites through a single nucleotide modification in the core ( GATTA = >GcTTA ) ( construct Msxb-D ) led to a partial loss of activity ( Figure 6E ) . Since activity was not completely suppressed , we looked for potential Otx binding motifs with altered core sequence . Interestingly , we found a GAATTA motif that corresponds to a canonical GGATTA sequence in Ciona savignyi ( Figure 6A ) . Simultaneous mutation of this and the 3 canonical Otx sites ( GNATTA = >GNcgTA ) ( construct Msxb-I ) led to a complete loss of activity . We next mutated the 4 conserved Smad Binding Elements ( AGAC = >ctAC ) and found these sites to be essential for Msxb-B activity ( Msxb-L construct; Figure 6A , E ) . We finally mutated the Fox sites . Two AAACA sites overlap in the AAACAAACA sequence ( Figure 6A ) . We generated either a single nucleotide change that matches in the core of each Fox site ( AAACgAACA , Msxb-E ) or a single nucleotide change in each core ( AAgCAAgCA , Msxb-H ) ( Figures 6E and S8 ) . These mutations did not affect enhancer activity . Additional mutation ( AACA = >AgCA , Msxb-G ) of the three more degenerate AACA consensus found in the sequence , but not conserved in C . savignyi , also had no effect ( Figure S8 ) . We then tested the effect of mutating Fox sites in the sensitized context of the Msxb-D element where 3 Otx sites are mutated and where activity is decreased . The Msxb-F fragment ( 3 Otx sites mutated , 2 canonical Fox sites mutated ) displayed a further reduction in activity ( Figure 6E ) , suggesting that Otx and Fox sites may work together to control Msxb-B activity . Mutational analysis indicates that Msxb regulation through the Msxb-B enhancer may involve putative Fox binding sites and requires the presence of putative Otx and Smad binding sites to be transcriptionally active in b6 . 5 derived cells . We tested the transcriptional activity of the Ciona Msxb and Delta2 enhancers that we identified in a distantly related and genomically divergent ascidian , Phallusia mammillata . When each construct was electroporated in P . mammillata embryos , we detected LacZ activity in dorsal tail epidermis midline , dorsal nerve cord and secondary muscle , the same territories that are stained in C . intestinalis ( Figure 7B , D and Table S2 ) . These results suggest that the regulatory logic of these enhancers is interpreted in the same way in C . intestinalis and P . mammillata embryos . The similar enhancer activity between these two species possibly reflects conservation of the combination of transcription factors , the trans-regulatory logic , acting upstream of Msxb and Delta2 . We further tested this possibility by determining the expression patterns of Msxb and Delta2 in P . mammillata by in situ hybridization ( Figure S9 ) . We observed that both genes are activated in the b6 . 5 lineage at the 64-cell stage ( b7 . 9 and b7 . 19 blastomeres ) like the C . intestinalis orthologous genes . This expression was abolished when inhibitors of the FGF/MEK ( U0126 ) and Nodal ( SB431542 ) signaling pathways were applied to the embryos ( Figure 7G-L ) . These results led us to search for enhancers regulating Msxb expression in P . mammillata . Employing the same strategy we used for C . intestinalis genes , we searched the Pm-Msxb locus for regions enriched in Otx , Fox and Smad binding motifs and conserved in the sister species Phallusia fumigata . We isolated a 587 bp fragment containing 6 Otx , 7 Fox binding motifs and 7 SBEs and located just upstream of Pm-Msxb ( Figure S10 ) . This fragment , “Pm-msxb-b6 . 5 line” , whose sequence could not be aligned with that of “Ci-msxb-b6 . 5 line” , was active in b6 . 5 derivatives when electroporated in P . mammillata ( Figure 7F ) or C . intestinalis ( Figure 7E ) embryos ( Tables S1 and S2 ) . Therefore , the functional knowledge acquired in C . intestinalis was sufficient to isolate an active enhancer with expected activity in another species , P . mammillata .
FGF-triggered neural induction in Ciona appears , at first glance , to be a simple inductive process whereby two blastomeres ( a6 . 5 and b6 . 5 ) receive a signal from the vegetal hemisphere and adopt a neural fate instead of an epidermal fate ( Figures 1 and 2A ) . However , this event is tightly controlled: ectodermal cell competence is regulated [39] , [40] , embryo geometry [52] and various signaling pathways [41] also control the response of the ectoderm to the inducer . We have shown that three FGF-dependent genes expressed in the b6 . 5 progeny from the 64-cell stage show differential regulation by Nodal signaling . Chordin is probably directly regulated by Nodal while Msxb and Delta2 need additional inputs from Otx . Our data provide additional connections and genomic hardwiring to a previously described network [35] . The network of genes regulating posterior neural fate is not linear and includes several regulatory loops ( Figure 4K ) . FGF activates at least two direct target genes , Otx and Nodal , at the 32-cell stage , which collectively regulate secondary targets ( i . e . Msxb and Delta2 at the 64-cell stage ) . Moreover , the regulation that we have uncovered involves a transcription factor and a signaling molecule that are expressed in the same cells . It is possible that this configuration allows very tight transcriptional control in a lineage-restricted manner using autocrine signaling . Finally , we have uncovered additional interactions that most likely maintain gene expression in a lineage-restricted manner following initial activation . For example , maintaining Nodal expression in the b6 . 5 progeny following FGF induction is apparently controlled both by Otx and Nodal itself ( Figures S3 and 4K ) . The actual mode of concerted regulation of Msxb and Delta2 by Otx and Nodal at the molecular level will need further investigation . We have proposed that the signaling molecule Nodal uses a Fox factor as a nuclear effector [44] , [53] . This hypothesis led us to isolate three enhancers active in the b6 . 5 lineage . However , it is very likely that omitting Fox sites in our enhancer search would have led to the same outcome since Fox consensus sites ( AAACA ) are probably very abundant in the AT-rich ascidian genomes . Nevertheless , we observed that two overlapping Fox sites ( AAACAAACA ) are present in Msxb enhancers from both C . intestinalis and P . mammillata ( Figures S5 and S10 ) . However , mutation of these sites in “Ci-msxb b6 . 5 line” enhancer was silent unless some Otx sites were also mutated ( Figure 6 ) . The C . intestinalis genome encodes 29 predicted Fox factors whose expression pattern during early development has been determined [37] , [54] , but the number of candidate Fox factors ( expressed in the b6 . 5 lineage or maternally provided ) is beyond the scope of the current study . Although we cannot exclude the involvement of Fox factors in Msxb and Delta2 regulation , we would favor an alternative scenario explaining the concerted action of Otx and Nodal . We have shown that Smad Binding Elements ( SBEs ) are essential for msxb-B enhancer activity , and the active enhancers that we have isolated contain at least three SBEs . We could thus conceive that Otx itself serves as a co-factor for Nodal signaling and that it would interact directly with activated Smad2/3 on the enhancer to promote transcriptional activation . Besides activating secondary FGF targets , the function of direct FGF targets is an opened question . Epidermal versus neural fate decision is primarily controlled by FGF signaling . We have shown that inhibition of FGF , Nodal or Otx function abolishes b-line neural fate . However , contrary to the inhibition of FGF , blocking Nodal or Otx function does not lead neural precursors to adopt the alternative epidermal fate ( Figures S1 and S4 ) . These observations can be explained by two non-exclusive hypotheses: epidermis fate inhibition is achieved directly upon reception of FGF signaling or several direct FGF targets contribute to epidermis repression . In particular , in addition to Otx and Nodal , genes such as Elk and Erf are expressed in neural progenitors and are likely direct FGF targets [30] , but their function has not been determined . Following their activation at the 64-cell stage in the b7 . 9/10 blastomeres , Msxb and Chordin remain expressed in all daughter cells ( until mid-gastrula stages ) but Delta2 expression becomes restricted in b8 . 18/20 blastomeres , precursors of the dorsal tail midline epidermis ( Figure 2 ) . This change in expression correlates with and may be involved in the fate restriction that occurs at early gastrula stages . This event is crucial since it separates central nervous system ( dorsal nerve cord ) and peripheral nervous system ( dorsal tail midline epidermis ) precursors . A similar CNS versus PNS segregation occurs at the same time in the anterior part of the embryo and involves FGF signaling [55] . While Msxb is essential for the formation of both dorsal tail epidermis midline and dorsal nerve cord [23] , [35] , the role of the two other genes remains to be investigated . Otx is a transcription factor expressed in the anterior nervous system , and which participates to anterior neural patterning in many bilaterians [56] , [57] . In ascidians , a similar role has previously been ascribed to this gene in two distantly related species Ciona intestinalis and Halocynthia roretzi [21] , [27] , [33] , [35] , [42] , [58] . The additional involvement of Otx in posterior neural tissue formation that we describe in the present study is rather unexpected . However , the function of Otx that we have addressed corresponds to a very early phase of its dynamic expression . Otx has been shown to be a direct target of FGF signaling at the 32-cell stage [11] . The expression is transient ( from the 32-cell stage to the 112-cell stage ) in both anterior ( a6 . 5 lineage ) and posterior ( b6 . 5 lineage ) neural tissue precursors and precedes a new and massive expression only in the anterior neural plate ( from early gastrula stages ) . This early phase marks neural induction in both ascidian species studied [11] , [59] , [60] . While the onset of expression of Otx homologs in vertebrates may be broader than the prospective anterior central nervous system [61] , there is no report , to our knowledge , of participation of Otx genes in posterior nervous system formation . We consequently propose that Otx has been co-opted in ascidians for posterior neural tissue specification . Whether this co-option is unique to ascidians will await more functional data in invertebrate deuterostomes . We have shown that Nodal is required for posterior neural tissue formation and that Nodal can posteriorize FGF-induced neural tissue . Interestingly , Nodal signaling is also involved in posterior neural tissue formation in vertebrates [62]–[64] . However , this is most likely indirect through the control of mesoderm specification and patterning . Nodal signaling is rather thought to be an anti-neural pathway whose activity needs to be shut down for neural fate acquisition [65] , [66] . Our study shows that the function of Nodal signaling in ascidians is different from vertebrates: Nodal is not incompatible with neural fate and it can directly posteriorize neural tissue . In Ciona , Nodal expression in posterior neural precursors is the result of differential competence of animal blastomeres to respond to FGF . This competence is controlled by FoxA-a , expressed in anterior blastomeres [35] , [40] , [41] . When FoxA-a function is abolished , anterior neural ectoderm adopts a posterior identity and ectopically expresses Nodal and Delta2 . A phenotype similar to what we observed for Nodal ectopic misexpression . However , Nodal is not the only factor involved in posterior identity definition . When we blocked Nodal function , posterior neural precursors did not adopt an anterior identity . This result suggests that either expression of FoxA-a is necessary for anterior identity definition and/or that additional factor ( s ) control posterior identity redundantly with Nodal . It will be interesting to probe the involvement of other signaling pathways ( Wnt , FGF and retinoic acid ) that are also major regulators of posterior neurectoderm formation in vertebrates [4] . Based on the combined regulation by Otx and Nodal , we were able to isolate enhancers containing putative Otx , Fox and Smad binding sites that control expression in the posterior neural lineage for two co-expressed genes . Interestingly , the “Ci-msxb-b6 . 5 line” enhancer is also active in anterior neurectoderm at tailbud stages ( Figure S11 ) where several enhancers with an Otx signature have been described to be active [42] . This raises questions that will need further investigation . Are the same Otx-regulated enhancers re-used in different territories at different stages ? Does the fragment we tested contain two distinct abutting or partially overlapping enhancers ? These enhancers could consequently be the means for Otx co-option in posterior neural tissue . Finally , is Nodal signaling involved in later steps of anterior neurectoderm formation in C . intestinalis ? We have extended our study through cross-species transcriptional assay in two divergent ascidian species . Since Otx and Nodal display conserved expression in the b6 . 5 blastomeres in both C . intestinalis and H . roretzi [27] , [29] , [58] , [67] , it is very likely that they are also expressed in b6 . 5 in Phallusia mammillata , a species more closely related to C . intestinalis . This hypothesis can explain why we found conserved activity when C . intestinalis enhancers were tested in P . mammillata embryos . Importantly , we found that Msxb and Delta2 from P . mammillata are expressed under the control of FGF and Nodal signaling pathways in b-line neural precursors . Together with the isolation of an active enhancer for Pm-Msxb , these results strongly support that gene regulation is also conserved . We have tried to extend our comparison to Pm-Delta2 by testing several elements containing consensus Otx , Fox and Smad binding sites , but these elements were not active in posterior neural tissue precursors ( data not shown ) . This can be explained by subtle changes in gene regulation or most likely by an incomplete understanding of the regulatory logic to be able to predict a functional enhancer ( for example the tested elements had fewer Otx sites compared to the three active enhancers previously isolated ) . Interestingly , the Msxb enhancers that we isolated from each species do not show sequence conservation , they are not alignable . This is a general trend that has been observed by comparing ascidian genomes [21] , [22]; mainly coding sequences retain sequence conservation and there is poor synteny conservation . This indicates that these genomes have largely diverged and underwent extensive reshuffling . This offers an excellent situation to probe enhancer evolution and transcription factor binding site turnover in genomes that control development of very similar embryos [20] .
Ciona intestinalis type B were provided by the Centre de Ressources Biologiques Marines in Roscoff . Phallusia mammillata were collected by diving in the Port-Vendres and Sète harbors , or collected from fishermen trawling in the Banyuls-sur-mer area . C . intestinalis embryology was performed as described in [32] . Staging was described according to [68] . P . mammillata embryos were handled the same way as Ciona except dechorionation was performed on unfertilized eggs for around 40 min with 0 . 1% trypsin and 0 . 5% sodium thioglycolate acid raised to basic pH by NaOH addition . Electroporation was performed as described [32] with the following modification: a single pulse of 25V for 32 ms ( C . intestinalis ) or 25 to 37V for 32 ms ( P . mammillata ) . Recombinant protein and inhibitor treatments were conducted as previously described [11] , [27]–[29] , [32]: bFGF ( 100 ng/ml ) from the 16-cell stage , the protein synthesis inhibitor puromycin ( 200 µg/ml ) from the 8-cell stage , the MEK inhibitor U0126 ( 4 µM ) from the 8-cell stage and the TGFβ type 1 receptor inhibitor SB431542 ( 5 to 10 µM ) from the 16-cell stage . Standard control-MO ( 5'-CCTCTTACCTCAGTTACAATTTATA 3' ) and otx-MO ( 5′-ACATGTTAGGAATTGAACCCGTGGT-3′ ) were purchased from GeneTools LLC and were injected at 0 . 25 to 0 . 50 mM . The genes described in this study are represented by the following gene models in the KH2012 Ciona intestinalis assembly: Fgf9/16/20 ( KH . C2 . 125 ) , Otx ( KH . C4 . 84 ) , Nodal ( KH . L106 . 16 ) , Msxb ( KH . C2 . 957 ) , Delta2 ( KH . L50 . 6 ) , Chordin ( KH . C6 . 145 ) , Klf1/2/4 ( KH . C5 . 154 ) , KH . C7 . 391 ( KH . C7 . 391 ) , Dmrt1 ( KH . S544 . 3 ) , Lefty ( KH . C3 . 411 ) , Fog ( KH . C10 . 574 ) and Ap2-like2 ( KH . C7 . 43 ) . Whole mount in situ hybridization and X-gal staining were performed as previously described [11] . Dig-labeled probes were synthesized from the following cDNAs for C . intestinalis: Msxb ( cign067l18 ) , Delta2 ( cieg005o22 ) , Chordin ( cign055j01 ) , Nodal ( cicl090l02 ) , Dmrt1 ( ciad017d15 ) , Klf1/2/4 ( citb012d14 ) , KH . C7 . 391 ( cilv038e26 ) [69] , Otx [27] and Ap2-like2 ( cien223529 ) ( Rothbächer et al . , in preparation ) . For P . mammillata: Msxb ( AHC0AAA214YL10RM1 ) and Delta2 ( AHC0AAA62YG24RM1 ) . While Msxb , Delta2 and Chordin expression in the b6 . 5 lineage starts at the 64-cell stage ( st . 8 ) , we analyzed early gastrula stages ( st . 10/11 ) because expression is much stronger and more readily detectable by in situ hybridization . Electroporation constructs for overexpression were generated using Gateway technology [51] with the promoter of Fiend of Gata ( Fog ) driving expression throughout ectoderm from the 16-cell stage [32] , [39] . Constructs for Fgf9/16/20 , Nodal and Lefty have already been described [32] . pFOG-Otx was generated by U . Rothbächer using the pENTRY clone cien28442 ( Rothbächer et al . , in preparation ) . A construct corresponding to the homeodomain of Otx fused to the Engrailed repressor domain has already been used [42] and was converted into a pENTRY clone using the following primers: attB1-OTXHD-Fw ( 5′-AAAAAGCAGGCTCAGAAAAAATGGTATACAGTTCGTCTAGAAAAC-3′ ) and attB2-EnR-Rev ( 5′-AGAAAGCTGGGTGAATTCTATACGTTCAGGTCCT-3′ ) . For transcriptional assay , genomic fragments were PCR amplified from sperm genomic DNA using AccuPrime Taq HiFi DNA polymerase ( Invitrogen ) and converted into pENTRY clones by a BP clonase reaction or TA cloning using the PCR8/GW/TOPO TA cloning kit ( Invitrogen ) . The LR clonase reaction was performed to produce an expression clone with the genomic region in front of the minimal promoter of Fog and of nls-LacZ [51] . A detailed list of primers and vectors is described in Table S3 . Enhancers msxb-A to -M ( Figures 6 , S7 and S8 ) were designed based on the msxb-OtxUP type B sequence and were synthesized as G-blocks Gene Fragments ( Integrated DNA Technologies ) flanked with AttB sequences ( sequences listed in File S1 ) . G-Blocks were shuffled into pDONR221 through BP reaction and through LR reaction into Rfa-bpFOG-nlsLacZ [51] .
|
The Chordate phylum groups vertebrates , tunicates ( including ascidians ) and cephalochordates ( amphioxus ) . These animals share a typical body plan characterized by the presence during embryonic life of a notochord and a dorsal neural tube . Ascidians , however , took a significantly different evolutionary path from other chordates resulting in divergent morphological , embryological and genomic features . Their development is fast and stereotyped with very few cells and ascidian genomes have undergone compaction and extensive rearrangements when compared to vertebrates , but also between ascidian species . This raises the question of whether developmental mechanisms controlling typical chordate structure formation are conserved between ascidians and vertebrates . Here , we have studied the set of ascidian genes which control the formation of the posterior part of the nervous system . We uncovered original usages of the signaling molecule Nodal and the transcription factor Otx . For example , Otx , which is a specific determinant of anterior identity in most metazoans , has been co-opted for the formation of the ascidian posterior nervous system . These two factors define a regulatory signature found in enhancers of posterior neural genes in two genomically divergent ascidian species .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
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"genome",
"expression",
"analysis",
"functional",
"genomics",
"genome",
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"embryology",
"gene",
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"comparative",
"genomics",
"ciona",
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"gene",
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"life",
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"biology",
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] |
2014
|
An Otx/Nodal Regulatory Signature for Posterior Neural Development in Ascidians
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A CpG island ( CGI ) lies at the 5′ end of the Airn macro non-protein-coding ( nc ) RNA that represses the flanking Igf2r promoter in cis on paternally inherited chromosomes . In addition to being modified on maternally inherited chromosomes by a DNA methylation imprint , the Airn CGI shows two unusual organization features: its position immediately downstream of the Airn promoter and transcription start site and a series of tandem direct repeats ( TDRs ) occupying its second half . The physical separation of the Airn promoter from the CGI provides a model to investigate if the CGI plays distinct transcriptional and epigenetic roles . We used homologous recombination to generate embryonic stem cells carrying deletions at the endogenous locus of the entire CGI or just the TDRs . The deleted Airn alleles were analyzed by using an ES cell imprinting model that recapitulates the onset of Igf2r imprinted expression in embryonic development or by using knock-out mice . The results show that the CGI is required for efficient Airn initiation and to maintain the unmethylated state of the Airn promoter , which are both necessary for Igf2r repression on the paternal chromosome . The TDRs occupying the second half of the CGI play a minor role in Airn transcriptional elongation or processivity , but are essential for methylation on the maternal Airn promoter that is necessary for Igf2r to be expressed from this chromosome . Together the data indicate the existence of a class of regulatory CGIs in the mammalian genome that act downstream of the promoter and transcription start .
Atypical CpG-rich regions known as CpG islands ( CGIs ) overlap 60–70% of mammalian transcription start sites [1] . Although most CGIs extend downstream of the transcription start and are therefore partly transcribed , they are considered to have promoter regulatory functions and are often described as ‘CGI promoters’ . A recent study used a biochemical purification strategy to identify a large number of novel CGIs not associated with annotated promoters , in the body of coding genes or in intergenic regions [2] . While this could indicate the mammalian genome has many transcripts still to be identified , it is also possible that CGIs have additional functions in addition to promoter regulation . The best examples of CGIs with additional regulatory functions are those that lie inside imprint control elements ( ICE , also known as an imprint control region ) [3] . An ICE is a genetically defined region whose epigenetic state controls parental-specific expression of small clusters of genes [4]–[6] . CGIs within an ICE are similar to classic promoter-associated CGIs as they show a CpG density higher than the genome average and lack sequence conservation , even between homologous mouse and human elements [7] . However they differ in several ways [8] , [9] . First , their CpG density is less than that of classic promoter-associated CGIs . Second , whereas most promoter-associated CGIs are free of DNA methylation [1] , CGIs within ICEs gain DNA methylation during gametogenesis but only in one of the two parental gametes . These modified regions are also known as gametic ‘differentially-methylated-regions’ ( gDMRs ) , since once established in a gamete they are maintained in all somatic cells on the same parental chromosome , while the other parental allele remains methylation-free . In six imprinted clusters the ICE has been shown by deletion experiments that include the CGI , to control repression of all imprinted genes [10]–[15] . Thus , the third distinguishing feature is that an unmethylated ICE can act as a cis-acting long-range repressor of multiple flanking genes . This indicates that CGIs residing in an ICE may be a prototype for a class of cis-regulatory CGIs that may differ from classic promoter-associated CGIs . Remarkably , the silencing ability of the unmethylated ICE correlates with its action as a promoter or cis-activator of a macro non-protein-coding ( nc ) RNA ( provisionally defined as a ncRNA >200 bp whose function does not depend on processing to smaller RNAs ) [16] , [17] . Three imprinted macro ncRNAs that play a direct role in imprinted gene silencing i . e . , Airn , Kcnq1ot1 , and Nespas , have their promoter in the ICE [18]–[20] . These cis-repressor macro ncRNAs therefore contain CGI sequences at their 5′ end that could contribute to their repressor function . The Airn , Kcnq1ot1 and Nespas macro ncRNAs are expressed only from the paternal chromosome and induce paternal-specific silencing of flanking protein-coding genes . Imprinted expression of the flanking protein-coding genes arises because these repressor macro ncRNAs are repressed on the maternal chromosome by an ICE gametic methylation imprint [9] , [21] , [22] . Maternal gametic methylation imprints depend on expression in growing oocytes of the DNMT3A/B de novo methyltransferases and the DNMT3L cofactor [23] , [24] . It has also been shown that transcription across the ICE controlling Nespas ncRNA expression is required for methylation in oocytes [25] . In addition , recent high-throughput analyses show a general link between overlapping transcription and CGI methylation in oocytes [26] . However , there is little information on the relative contribution of DNA elements within the ICE for the methylation state . Tandem direct repeats ( TDRs ) that show organizational but not sequence conservation , are frequently found in or adjacent to the ICE and have been suggested to guide epigenetic modifications [9] , [27] , [28] . The TDRs are present on both parental chromosomes but methylation of the ICE restricts expression of the macro ncRNA to one parental chromosome . Thus , it is possible that the TDRs play a role in ICE methylation on one parental chromosome and in the repressor function of the macro ncRNA expressed from the other parental chromosome . However , to date various experiments analysing their function either at the endogenous locus or in a transgene context , have not yet identified a general function for TDRs in imprinted clusters [28] . The Airn macro ncRNA promoter that is embedded in the ICE , lies in intron 2 of the Igf2r gene . Airn overlaps and represses the Igf2r promoter ( Figure 1A ) ; in extra-embryonic lineages Airn also represses the non-overlapped Slc22a2 and Slc22a3 genes that lie more than 100 kb upstream of the Airn promoter [19] , [29] . Airn is an unusually long 118 kb ncRNA that is transcribed by RNA polymerase II ( RNAPII ) . The majority of nascent Airn transcripts are unspliced and nuclear-localized while the minority that are spliced are exported to the cytoplasm [21] . Splicing suppression , unusual length and gene silencing ability are also features shared with the Kcnq1ot1 macro ncRNA [30] . Previously we have established an ES cell imprinting model that recapitulates the onset of imprinted Igf2r expression in early mouse embryonic development [31] ( Figure 1A ) . Undifferentiated ES cells show bi-allelic but low-level Igf2r expression and Airn is not expressed [31] . Airn expression is initiated during ES cell differentiation and induces imprinted Igf2r expression by blocking up-regulation of the overlapped paternal promoter between days 3–5 . The Igf2r promoter , which is also associated with a CGI , gains DNA methylation on the paternal allele after the onset of imprinted expression between days 5–14 . However , this somatic methylation mark is not required for repression , as Airn silences Igf2r in mouse embryos lacking DNA methylation [21] , [22] . We previously used a deletion/replacement approach in this ES cell imprinting model to identify a 959 bp promoter region immediately upstream of the Airn main transcription start [32] . These experiments demonstrated not only that the promoter lies upstream of the annotated CGI ( Figure 1B ) , but also , that the endogenous Airn promoter does not control the unusual features of the macro ncRNA , as Airn driven by the mouse Pgk1 promoter is indistinguishable from Airn driven by its endogenous promoter [32] . Thus control of the unusual biology of the Airn macro ncRNA lies outside its promoter . Here we use the ES cell imprinting model and also mouse models , to test if the Airn downstream CGI plays a role either on the paternal allele in regulating Airn expression and function and the unmethylated state of the ICE or , on the maternal allele in regulating ICE methylation ( Figure 1A , 1B ) . The Airn downstream CGI contains in its distal half two classes of imperfect TDRs that are each repeated three times , one with a 172–180 bp monomer length and one with a 30–32 bp monomer length ( Figure 1B ) . We used homologous recombination in ES cells to delete a 1129 bp fragment containing the entire CGI and also to delete a 692 bp fragment containing just the TDRs . Both deletions left the Airn promoter and transcription start site ( TSS ) intact . Analysis of the effects of the deletion on the paternal chromosome that expresses Airn shows that the CGI deletion decreased Airn transcription initiation and strongly reduced transcript elongation , which as predicted from previous analyses [19] , led to a loss of its ability to repress Igf2r in cis . The TDR deletion on the paternal chromosome led to a minor defect in transcript processivity that progressively affected the 3′ end of Airn , combined with a minor effect on its repressor function in differentiated ES cells and in mouse tissues . In contrast to the minor role on the paternal chromosome , analysis in mouse embryos of maternal chromosomes carrying the TDR deleted allele shows this element is essential for the DNA methylation imprint . Together these data show the Airn CGI has a dual parental-specific function and is necessary both for Airn biology and function as well as the critical epigenetic modifications that control its imprinted expression .
We first examined if the position of the Airn CGI that lies downstream of the TSS , represents a rare exception or a common occurrence in the mouse genome ( Figure 1C ) . We asked , for each CGI annotated by the UCSC genome browser ( http://genome . ucsc . edu/ ) , if a known protein-coding or non-protein-coding gene taken from the NCBI RNA reference sequences collection ( RefSeq ) , has its TSS within the CGI or in the DNA region representing half the length of the CGI up- or downstream . 57% of all RefSeq genes were associated with a CGI , of which 88 . 5% including Igf2r , have their TSSs within the body of the CGI ( grey shaded area Figure 1C ) , with the majority lying in the first half . 11 . 5% of CGI-associated RefSeq genes have their TSS located outside of the annotated CGI , 8 . 7% of these have their TSS upstream and 2 . 8% have a TSS downstream of the CGI . Airn represents one of those with their TSS located upstream of the CGI while the Kcnq1ot1 macro ncRNA has its TSS on the 5′ border of the CGI . As the Airn macro ncRNA has its TSS located upstream of the CGI it is possible to distinguish separate functions for the promoter ( previously mapped to a 959 bp fragment lying upstream of the Airn-TSS [32] ) and the CGI . We first generated a deletion of the TDRs that occupy the second half of the Airn downstream CGI ( Figure 1B ) . We used feeder-dependent D3 ES cells previously modified to contain a single nucleotide polymorphism ( SNP ) in Igf2r exon 12 that is used to distinguish maternal and paternal Igf2r expression [31] . The SNP modifies the maternal allele , thus this cell line is called S12/+ ( note the maternal allele is always written on the left i . e . , Mat/Pat ) . S12/+ cells were used as the wildtype control for all differentiation experiments . We targeted S12/+ cells by homologous recombination to delete a 692 bp region containing all TDRs starting 614 bp downstream of the major Airn-TSS ( T1 ) . The selection cassette was inserted 2 kb upstream of the deletion to avoid leaving a loxP site at the deletion , which has been reported to attract DNA methylation [33] and also to minimise potential effects on the Airn promoter region from the transient presence of a selection cassette ( Figure S1A ) . Two independent homologously-targeted clones ( named S12/TDRΔ+cas-1 and -2 ) were verified by Southern blot . The selection cassette was removed by transient transfection with a CRE-recombinase expressing plasmid ( Figure S1A , S1B ) and cells were subcloned to obtain four cell lines ( named S12/TDRΔ-1A/-1B/-2A/-2B ) . Southern blot analysis showed that all cells were targeted on the paternal allele that carries the unmethylated active ICE and expresses the Airn ncRNA ( Figure S1C , S1D ) . Preferential paternal targeting of the region between the Airn and Igf2r promoters is a feature of this cluster ( data not shown ) . Initial analysis of the deletion was performed in the ES cell imprinting model that we have shown recapitulates the onset of imprinted Igf2r expression in the early embryo [31] , [32] . To further validate the ES cell imprinting model and to observe a possible role of the TDRs in ICE methylation on the maternal allele , we deleted the same region in intraspecies 129/B6 A9-ES cells to generate knock-out mice ( Figure S2A ) . The homologously-targeted A9 clone ( +/TDRΔ+cas ) was injected into blastocysts and the selection cassette removed by mating to a MORE CRE-deleter strain ( Figure S2B , S2C ) [34] . It is technically challenging to determine the exact length of macro ncRNAs as they are too long to be resolved on RNA blots . Therefore to test if the TDR deletion changed the length of the Airn transcript , we performed an RNA hybridisation to a genome tiling array . Genome tiling arrays allow the detection of changes in transcript length using approximately 50 bp long probes spaced every 100 bp of single copy genomic DNA . As Airn is only expressed upon differentiation , we differentiated wildtype ( S12/+ ) and two independently derived TDRΔ ES cells ( S12/TDRΔ-2A and +/TDRΔ ) . cDNA prepared from total RNA labelled with one fluorochrome and sonicated genomic DNA labelled with a second fluorochrome , were cohybridized to the tiling array and the relative signal intensity plotted ( Figure 2 ) . The displayed 180 kb region contains the overlapping Igf2r and Airn transcripts and can be divided into three parts: the first part is specific to the 3′ end of the Igf2r transcript , the second part is the region of Igf2r/Airn sense/antisense transcriptional overlap and the third part is specific to the 3′ end of the Airn transcript from 28–118 kb . In the ‘Igf2r-specific’ region , comparison of signal intensities does not indicate a difference in Igf2r levels between the wildtype and two TDRΔ ES cell lines , as the overlapping error bars show the technical variation is larger than the biological difference . All three relative signal intensities then show an abrupt increase in the ‘overlap’ region due to the combined signals of Igf2r and Airn . In the ‘Airn-specific’ region , all three signal intensities decline from left to right as previously shown for wildtype Airn [32] . However , in both TDRΔ cell lines compared to wildtype cells , although Airn relative intensity was unchanged from 28–68 kb downstream of the Airn-TSS , it was reduced after 68 kb and absent from 90 kb onwards while wildtype Airn extends 20 kb further ( Figure 2 single and double dashed arrows ) . Thus the TDR deletion on the paternal chromosome has no effect on the first half of Airn but reduces its overall length progressively towards the 3′ end . While 95% of Airn transcripts are unspliced , spliced transcripts comprise 23–44% of the steady-state population due to their increased stability [21] . To confirm the shortening of Airn on unspliced and spliced transcripts , we quantified steady-state levels of spliced and unspliced Airn in undifferentiated and differentiated TDRΔ and control ES cells using qPCR assays spread throughout the Airn transcript ( Figure 2 map ) . These qPCR assays allowed us to specifically test if splicing suppression of Airn is affected by the TDR deletion . As previously reported , neither spliced nor unspliced Airn is expressed in undifferentiated ( d0 ) wildtype ( S12/+ ) ES cells but Airn is strongly upregulated during differentiation ( d5–d14 ) [31] ( Figure 3A , 3B ) . Similar kinetic behaviour with some biological variation was found for all four S12/TDRΔ ES cells using four qPCR assays spaced over the first 73 kb of Airn . These were an assay within the first exon of Airn that detects both unspliced and spliced Airn ( START , Figure 3A black bars ) , two assays that detect only unspliced Airn ( RP11 at 154 bp and Airn-middle at 53 kb , Figure 3A light and dark grey bars ) and , two assays that only detect spliced Airn ( RP6 detecting the SV1a splice variant at 38 kb and RP21 detecting the SV1 variant at 73 kb that are the most abundant spliced products , Figure 3B black and light grey bars ) . At d14 , RP5 detecting the SV2 splice variant at 88 kb showed a significant reduction in 2 of 4 TDRΔ clones compared to wildtype ( Figure 3B dark grey bars ) . Two qPCR assays in the 3′ part of Airn showed a significant reduction in all four S12/TDRΔ cells compared to wildtype cells . These were one assay that detects unspliced Airn ( Airn-end at 99 kb , Figure 3A white bars ) and one assay that detects spliced Airn ( RP4 detecting the SV3 splice variant at 118 kb; Figure 3B white bars ) . Although splice variants that used an exon 2 located after 73 kb were reduced , the TDR deletion did not induce a major shift in spliced versus unspliced Airn transcripts for SV1a and SV1 that end before 73 kb . Thus , the inefficient splicing of Airn is not dependent on sequences in the TDRs . In addition , neither the absence of Airn transcription in undifferentiated ES cells , nor its ability to be upregulated during differentiation depends on TDR sequences . We also analysed steady-state levels of Airn with or without the TDRs in 12 . 5–13 . 5 dpc mouse embryos . We used +/+ and +/TDRΔ embryos and as additional controls , Thp/+ and Thp/TDRΔ embryos . Thp is a hemizygous deletion of the Igf2r cluster allowing the specific analysis of one parental allele [35] . We analysed unspliced Airn with three different qPCR assays at the beginning , middle and end of Airn and found that the mouse data largely recapitulate those from the ES cell imprinting model by showing a progressive length reduction towards the 3′ end of Airn ( Figure 3C ) . The 5′ assay ( RP11 at 154 bp , grey triangles ) detected similar amounts of Airn from the wildtype and the TDRΔ allele . In contrast to the results obtained from the ES cell imprinting model , one assay in the middle of Airn ( Airn-middle at 53 kb , black rectangles ) showed a significant reduction to approximately 55% of wildtype levels of Airn from the TDRΔ allele . The end assay , in agreement with the ES cell imprinting model ( Airn-end at 99 kb , black circles ) , detected significant reduction to approximately 5% from the TDRΔ allele compared to the wildtype allele . A similar progressive loss of Airn towards the 3′ end was detected in the extra-embryonic visceral yolk sac ( VYS ) ( Figure S3A ) . Together , the quantitative analysis of Airn expression supports the conclusion drawn from the genome tiling array , that sequences in the TDR deletion are necessary for full-length Airn . In addition to the unusual biology that results in a very long unspliced RNA , the other key property of the Airn ncRNA is its cis-silencing ability . We have previously shown that shortening Airn from 118 kb to 3 kb by targeted insertion of a polyA-signal , leads to a loss of repression of the overlapped Igf2r gene and the upstream flanking Slc22a2 and Slc22a3 genes [19] . As TDR deletion led to a 3′ shortening of Airn we first asked if this affected its ability to repress Igf2r . This can be monitored indirectly by the gain of DNA methylation on the paternal Igf2r promoter-associated CGI or , directly by assaying allelic Igf2r expression . We first analysed gain of DNA methylation by methyl-sensitive restriction digestion of genomic DNA from undifferentiated and differentiated wildtype ( S12/+ ) and TDRΔ ( S12/TDRΔ ) ES cells ( Figure 4A ) . In undifferentiated wildtype ES cells both Igf2r parental alleles are methylation-free as demonstrated by the single 4 kb band . Upon differentiation a 5 kb band indicating a gain of DNA methylation on the paternal allele appears , which increases in strength during differentiation . At d14 , quantification from three independent differentiation sets ( Figure 4A , Figure S4A ) revealed methylated∶unmethylated ratios of 0 . 75∶1 in wildtype ES cells and a similar ratio in four TDRΔ ES cells . This indicates the gain of DNA methylation on the repressed paternal Igf2r promoter is not dependent on the TDRs . We next directly analysed Igf2r imprinted expression using the SNP that lies in Igf2r exon 12 , 20 kb upstream to the ICE , which can be distinguished by PstI digestion ( Figure 4B , Figure S4B ) . In undifferentiated ( d0 ) wildtype ES cells , PstI digested cDNA results in an undigested maternal ( Mat ) fragment and two restriction fragments representing the paternal ( Pat ) allele , indicating biallelic Igf2r expression . As previously described , the paternal-specific fragments are gradually lost during differentiation , which indicates maternally-biased Igf2r imprinted expression [31] . Undifferentiated ES cells with a paternal TDRΔ allele ( S12/TDRΔ ) also express Igf2r biallelically . However , they differ from wildtype cells by showing reduced paternal Igf2r repression during differentiation , as the two paternal-specific restriction fragments are more visible at d5 and in some cases , remain visible at d14 . We quantified this effect on paternal Igf2r repression using a qPCR assay that uses forward primers specific for the two SNP alleles in combination with a common reverse primer . The ratio of the maternal to the paternal allele in undifferentiated wildtype ES cells was set to 1 ( Figure 4C ) , as they were shown previously to express Igf2r biallelically [31] . Wildtype ES cells show a consistent increase in the maternal to paternal Igf2r ratio during differentiation , representing specific upregulation of the maternal allele , with constant low-level paternal expression . Undifferentiated ES cells with a paternal TDRΔ allele ( S12/TDRΔ ) also show ratios close to 1 , indicating biallelic Igf2r expression . S12/TDRΔ cells show an increased maternal∶paternal Igf2r ratio during differentiation , however , this only reached 44–55% at d5 and 49–65% at d14 of the ratio seen in wildtype cells , representing an approximate 2-fold upregulation of the paternal Igf2r allele in TDRΔ cells compared to wildtype ( Figure 4C ) . However , neither the maternal∶paternal Igf2r ratio , nor total Igf2r levels ( data not shown ) were statistically different in TDRΔ cells compared to wildtype cells . Total Igf2r levels were then analyzed in 12 . 5–13 . 5 dpc mouse embryos carrying a paternal TDRΔ ( +/TDRΔ , Thp/TDRΔ ) or paternal wildtype ( +/+ , Thp/+ ) chromosome ( Figure 4D ) . Mean total Igf2r levels in +/+ embryos were set to 100% and +/TDRΔ embryos showed an average of 106% . As the majority of Igf2r transcripts are produced from the maternal wildtype allele that could mask changes on the paternal allele , we analysed embryos carrying a maternal Thp deletion allele that only have the paternal Igf2r allele . The wildtype chromosome in Thp/+ embryos showed 4 . 5% of levels in +/+ embryos while the TDRΔ chromosome in Thp/TDRΔ embryos showed 7 . 6% of wild type levels , representing a 1 . 7-fold upregulation of Igf2r from the paternal TDRΔ allele that was however , not statistically significant . In extra-embryonic tissues , in addition to Igf2r , the Slc22a2 and Slc22a3 genes show Airn-dependent imprinted expression [19] . Analysis of all three genes in VYS shows a similar trend for a modest but not consistently significant loss , of paternal repression upon paternal transmission of the TDRΔ allele ( Figure S3B–S3D ) . Together this indicates a similar trend for a minor loss of imprinted repression of protein-coding genes in both ES cells and mid-late gestation embryos and extra-embryonic tissues , indicating that deletion of the TDRs slightly reduces the repressor efficiency of Airn . The Airn CGI is contained within the ICE that carries a gametic DNA methylation imprint on the maternal allele while the paternal allele is free of methylation . This gametic methylation imprint is present in undifferentiated ES cells as they are derived from the inner cell mass of the 3 . 5 dpc blastocyst [31] . To test if TDR deletion from the paternal allele compromised the methylation-free state of the paternal ICE , we analysed genomic DNA from undifferentiated ( d0 ) and differentiated ( d5 and/or d14 ) S12/+ and S12/TDRΔ ES cells by methyl-sensitive restriction digestion of genomic DNA ( Figure 5A ) . Wildtype ( S12/+ ) undifferentiated and differentiated ES cells both show a 6 . 2 kb band originating from the methylated maternal allele and a 5 . 0 kb band from the unmethylated paternal allele . In S12/TDRΔ ES cells , two fragments were similarly present , the 6 . 2 kb fragment from the wildtype maternal allele and a 4 . 3 kb fragment from the unmethylated paternal allele that is shortened by the TDR deletion . In addition , a faint but reproducible 5 . 5 kb band that must originate from a methylated TDRΔ paternal allele ( Figure S1 ) was detected in undifferentiated but not in differentiated S12/TDRΔ ES cells ( *Figure 5A ) . This indicates a transient gain of DNA methylation in undifferentiated S12/TDRΔ ES cells that is lost during differentiation . Figure S5A shows two additional differentiation sets with similar behaviour . To test if low-level DNA methylation on the paternal ICE represents a property of undifferentiated ES cells , rather than a consequence of the TDR deletion , we performed bisulfite sequencing , specifically analysing the paternal allele , in undifferentiated S12/TDRΔ-1A and -2A ES cells and a control ES cell line with the ICE deleted from the maternal allele ( R2Δ/+ ) [36] . Figure 5B , 5C and Figure S5C–S5E show that low-level DNA methylation from 11–14% with extremes ranging from 0–64% , is a general feature of the paternal ICE in undifferentiated ES cells and not a consequence of the TDR deletion . This low-level DNA methylation is however transient as the ICE becomes methylation-free in differentiated ES cells and in differentiated primary embryonic cells ( Figure 5A , Figure S5A , S5D , S5E ) . A possible effect of the TDR deletion on the methylated state of the maternal allele cannot be analysed in the ES cell imprinting model , as homologous recombination with the targeting vector replaces the CpG methylated genomic DNA with unmethylated DNA grown in bacteria . We therefore used TDRΔ embryos to analyse DNA methylation of the ICE by methyl-sensitive restriction digestion of genomic DNA . Paternal transmission of the TDRΔ did not affect the maintenance of the unmethylated state , confirming the data obtained from the ES cell imprinting model ( data not shown ) . In contrast , maternal transmission of the TDRΔ led to almost complete loss of the methylated 5 . 5 kb fragment ( detected as 6 . 2 kb in wildtype mice , Figure 5D ) . Bisulfite sequencing of mouse embryos carrying a maternal TDRΔ ( TDRΔ/+ and TDRΔ/Thp ) or a wildtype maternal allele ( +/+ ) confirms that maternal transmission of the TDRΔ allele led to near complete loss of the maternal methylation imprint ( Figure 5E , 5F , Figure S5F ) . The TDRΔ allele showed mean methylation levels of only 4% with extremes ranging from 0–29% . Together these results demonstrate that the TDRs do not play a role in the maintenance of the methylation-free state of the paternal ICE but are essential for methylation on the maternal ICE . Whether the TDRs play a role in the acquisition of the maternal ICE methylation mark in oocytes or its maintenance at later developmental stages was not determined . The loss of maternal ICE methylation in TDRΔ/+ embryos and VYS , resulted in maternal expression of the same progressively shorter Airn transcript with similar ability to repress Igf2r , Slc22a2 and Slc22a3 ( Figure S3E–S3J ) , as shown above for a paternal TDRΔ allele . Neither paternal nor maternal TDR inheritance has an effect on viability or fertility , examining respectively 29 and 65 offspring . In addition TDR homozygotes are obtained in the expected ratio from double heterozygote crosses ( i . e . , 19 wildtype , 34 heterozygotes and 12 homozygotes were found in 65 offspring ) . However , although male TDR homozygotes are fertile and produce viable young ( 16 offspring from 4 litters ) , female TDR homozygotes show reduced fertility and do not produce viable offspring ( 10 offspring were obtained from 4 litters but all died within 2 days , indicating a role for Igf2r in the female reproductive tract as noted earlier [37] . We have previously shown that low levels of Igf2r that continue to be expressed from the paternal allele in wildtype embryos ( approximately 5% , see Thp/+ in Figure 4D ) are not sufficient for viability in the absence of a maternal Igf2r allele [37] . Live born fertile TDRΔ offspring are obtained with Igf2r levels that average 16% ( ranging from 11–21% ) of wildtype at 12 . 5–13 . 5 dpc ( see Figure S3F ) . These crosses contain 129Sv and C57BL/6J genotypes and additional contribution to the survival of TDRΔ/+ mice could come from a mixed genetic background , which was previously shown to influence viability upon loss of maternal Igf2r contribution [37] , [38] . The above data shows that the TDRs that lie in the 3′ half of the CGI ( Figure 1B ) , act on the paternal chromosome to control the full-length of Airn and on the maternal chromosome to regulate ICE DNA methylation . To determine if the CGI contains additional elements regulating Airn expression we next removed the entire CGI . The same wildtype parental ( S12/+ ) ES cells were used to delete a 1129 bp fragment , starting 177 bp downstream of the Airn-TSS and ending at the same position as for the TDR deletion ( Figure S6A ) . This deletion left behind 106 bp of the 5′ part of the CGI including the diagnostic MluI site , however the remnant is too small to fit conventional CGI definition criteria [39] . The selection cassette was inserted at the same position as for the TDR deletion to avoid leaving a loxP site at the deletion site . We obtained two homologously-targeted clones that were both targeted on the paternal allele ( S12/CGIΔ+cas-1 , -2 ) ( Figure S6B , S6C ) . The selection cassette was removed by transient CRE expression and four ES cell subclones ( S12/CGIΔ-1A/-1B/-2A/-2B ) were used for analysis in the ES cell imprinting model ( Figure 1A , Figure S6B ) . To test if the CGI deletion enhanced the shortening of the Airn transcript observed after the TDR deletion , we differentiated S12/+ and S12/CGIΔ-1A ES cells and analysed them by RNA hybridization to genome tiling arrays ( Figure 6A ) . In contrast to the S12/+ cells ( and S12/TDRΔ cells in Figure 2 ) , the relative signal intensities from S12/CGIΔ cells did not increase at the transition from the ‘Igf2r-specific’ to the ‘overlap’ region but instead were similar . Since signals in the overlap region are derived from both Igf2r and Airn , this indicated an absence of Airn transcription in this region or a shortening of Airn not resolved on the array . In addition , S12/CGIΔ cells showed a sharp drop in signal intensity at the start of the ‘Airn-specific’ region and signals were not detected after 73 kb downstream of the Airn transcription start ( respectively single and double dashed arrows , Figure 6A ) . This was in contrast to Airn in TDRΔ cells that showed a drop in signal intensity from 68 kb onwards and no signal only after 90 kb ( Figure 2 ) . Lastly , higher signal intensities in the Igf2r-specific region in S12/CGIΔ cells compared to wildtype , indicate a gain of bi-allelic Igf2r expression . We also performed strand-specific RNA-Seq and plotted the log2 ratio of the number of reads originating from the forward and reverse strand to obtain an estimate for strand-specific expression in the analysed region ( Figure 6A , dotted lines ) . In S12/+ cells , reads in the ‘Igf2r-specific’ region show specific expression of Igf2r . In the ‘overlap’ region , the ratio then shifts towards the Airn-expressing forward strand , which is even more pronounced in the ‘Airn-specific’ region . In S12/CGIΔ cells , a similar albeit less pronounced shift in the ratio is seen upon transition from the ‘Igf2r-specific’ to the ‘overlap’ region , with a further shift occurring at the transition from the ‘overlap’ to the ‘Airn-specific’ region that is not detected after 73 kb and is reduced compared to S12/+ cells . This confirms in a strand-specific manner , a low level but persistent Airn-expression upon deletion of the CGI . Together , these data indicate that high Airn expression and production of full-length Airn transcripts and as a consequence , the ability to repress Igf2r in cis , are dependent on the CGI . To validate the genome tiling array data we analysed cDNA from undifferentiated and differentiated wildtype ( S12/+ ) and CGIΔ ( S12/CGIΔ ) ES cells using five qPCR assays spaced along the length of Airn . In undifferentiated ES cells with and without the CGI , Airn expression was mostly absent consistent with the previously observed lack of Airn expression in undifferentiated ES cells [31] . The low level of Airn expression seen in undifferentiated ES cells in Figure 6B represents a small amount of spontaneous differentiation that was similar in wildtype and CGIΔ ES cells . During differentiation Airn was upregulated in wildtype ES cells using all five qPCR assays . In contrast , the CGIΔ ES cells showed consistently less Airn at all analysed positions ( Figure 6B ) . However , the extent of the loss of steady-state levels differed along the length of the transcript . The START assay showed that total unspliced and spliced Airn is reduced to an average of 24–30% . Unspliced Airn detected with RP11 at 154 bp downstream of the TSS showed an average reduction to 50–65% , which was statistically significant in 3 of 4 clones at d14 . This difference between total and unspliced Airn is explained by the major loss of the Airn splice variants that require transcription elongation to at least 72 kb ( Figure 2 ) , and represent up to 44% of steady-state Airn levels [21] . Unspliced Airn detected with the AirnT3 assay at 2 . 8 kb from the TSS on the CGIΔ allele showed an average reduction to 8–14% and detection by the Airn-middle assay at 52 kb from the TSS showed reduction to 6–8% ( note that distances from the TSS on the CGIΔ allele are reduced by 1129 bp ) . Finally at Airn-end ( 98 kb from the Airn-TSS ) the reduction was to 0–0 . 4% of wildtype levels . This indicates that the CGI deletion induced successive loss of Airn with increasing distance from the 5′ end . To further map the observed shortening of Airn we used two more assays at the 5′ end ( Figure 6C ) . In CGIΔ cells , the Airn-124 assay at 570 bp from the TSS showed an average reduction of Airn steady-state levels to 17–21% , while the Airn-117 assay at 7 . 3 kb from the TSS showed an average reduction to 13–14% . We also analysed steady-state levels of two Airn splice variants to see if the splicing suppression was altered by the CGI deletion ( Figure 6C ) . The RP6 assay showed the SV1a splice variant is reduced on average to 5–7% of wildtype levels , while the RP21 assay showed the SV1 splice variant is reduced on average to 4–7% . Both these splice variants require transcription elongation to 72 kb ( Figure 2 ) . Splicing suppression was therefore not altered after the CGI deletion as the abundance of splice variants decreased in a similar manner as unspliced Airn . Furthermore , the abundance of splice variants from the CGIΔ allele is at most 7% of wildtype levels . As splice variants represent up to 44% of the Airn steady-state population , this indicates that the 24–30% of steady-state levels observed with the START assay ( Figure 2B ) represent more than 60% of initiating transcripts , confirming the result obtained for the RP11 assay . Together the data shows that Airn full-length elongation is significantly affected by deletion of the CGI with a successive loss of Airn with increasing distance from the 5′ end . However , by analysing RNA steady-state levels by qPCR a more moderate change is seen in transcription initiation such that 50–65% of Airn transcripts elongate at least to 154 bp . To test if the observed decrease of Airn ncRNA expression was reflected by altered recruitment of RNA polymerase II ( RNAPII ) we performed chromatin immunoprecipitation using antibodies specifically recognising initiating RNAPII phosphorylated at the Serine 5 ( Ser5P ) residue of its carboxy-terminal domain ( CTD ) and elongating RNAPII phosphorylated at Serine 2 ( Ser2P ) of its CTD . RNAPII occupancy in differentiated S12/+ , S12/TDRΔ-1A and S12/CGIΔ-1A ES cells was analysed at five positions along the gene body of Airn as well as in intron 5 of Igf2r to control for overlapping Igf2r transcription ( Figure 6D map ) . Whereas equal amounts of RNAPII Ser5P were found in S12/+ and S12/TDRΔ-1A cells , it was strongly reduced at the Airn 5′ region in S12/CGIΔ-1A cells , indicating reduced Airn transcriptional initiation on the CGIΔ allele ( Figure 6D left ) . For RNAPII Ser2P , S12/+ and S12/TDRΔ-1A showed similar enrichment except for Airn-end , where S12/TDRΔ-1A showed reduced levels . In S12/CGIΔ-1A cells , RNAPII Ser2P levels were increased in intron5 of Igf2r consistent with the increase in Igf2r levels observed in Figure 6A . RNAPII Ser2P levels within the Igf2r/Airn transcriptional overlap were lower compared to the other two cell lines indicating that Airn transcriptional elongation on the CGIΔ allele is strongly reduced ( Figure 6D right ) . An independent RNAPII ChIP experiment showed a similar result ( data not shown ) . Together , the analysis of RNA levels and RNAPII occupancy indicate that the CGI which is localised downstream of the Airn promoter , controls Airn initiation and elongation . As the majority of Airn transcripts were only between 154–570 bp long ( Figure 6C ) we tested if Airn produced from the CGIΔ allele was unable to silence Igf2r , as expected from previous experiments that truncated Airn to 3 kb from the TSS [19] . We first analysed the DNA methylation status on the paternal Igf2r promoter-associated CGI , as described in Figure 4A for the TDRΔ . Figure 6E and Figure S7 show that in contrast to wildtype ES cells , all four CGIΔ ES cell lines fail to gain DNA methylation on the paternal Igf2r promoter during differentiation , indicative of biallelic Igf2r expression in these cells . Next , we performed allelic expression analysis of Igf2r using the qPCR assay described above for the TDR deletion in Figure 4C . The results ( Figure 6F ) show that all differentiated CGIΔ cells displayed an unchanging maternal∶paternal expression ratio during differentiation indicative of biallelic Igf2r expression . This contrasts to wildtype cells that show an increasing ratio of maternal∶paternal Igf2r expression during differentiation indicative of maternally-biased imprinted expression . An absence of Igf2r imprinted expression can also be inferred from the tiling array analysis in Figure 6A where increased Igf2r hybridization signals are seen in the Igf2r-specific region and from the increased RNAPII occupancy in Igf2r intron 5 in Figure 6D . Thus , these results show that Airn transcripts in S12/CGIΔ ES cells , the majority of which had a length of between 154–570 bp are as expected , defective in their ability to silence Igf2r . Finally , we tested if deletion of the CGI affected the methylation-free state of the Airn promoter region on the normally unmethylated paternal allele . The CGI deletion left behind 106 bp of the 5′ part of the CGI including the diagnostic MluI site analysed for the TDR deletion in Figure 5A . Undifferentiated ES cells with a wildtype paternal allele ( S12/+ ) showed a 6 . 2 kb maternally methylated and an equally strong 1 . 1 kb paternally unmethylated band ( Figure 7A ) . Cells with a paternal CGI deletion ( S12/CGIΔ ) showed a wildtype 6 . 2 kb maternally-methylated fragment and a 5 kb paternally-methylated band . The size of the paternal fragment is reduced to 1 . 1 kb when unmethylated . In Figure 7A we also examined CGIΔ cells with ( S12/CGIΔ+cas ) and without ( S12/CGIΔ ) the selection cassette , to obtain information from cells that had experienced a short and long culture period since the loss of the CGI . Compared to S12/CGIΔ+cas cells , S12/CGIΔ cells that lack the selection cassette have been an additional 8 passages in culture and show an increased intensity of the 5 . 0 kb band indicating that DNA methylation increases with passage number . However , in contrast to the TDR deletion shown in Figure 5A , DNA methylation was not lost upon differentiation as indicated by the similar intensity of the 5 kb band in d0 and d14 S12/CGIΔ cells ( Figure 7B , S8 ) . Bisulfite sequencing was used to determine the extent of DNA methylation on the CGIΔ allele in undifferentiated ES cells using primers spanning the deletion that specifically amplify the paternal CGIΔ allele ( Figure S5B ) . In two S12/CGIΔ ES cell lines the results show a high level of DNA methylation ( Figure 7C left ) . The % methylation levels were 69–76% , with extremes ranging from 9–100% ( Figure 7C right ) . Taken together , this analysis shows that deletion of the CGI leads to a strong non-reversible gain of DNA methylation in cis , indicating that one major function of the CGI on the paternal allele is to block DNA methylation on the paternal ICE .
The 1129 bp deletion of the complete Airn downstream CGI had a moderate effect on the most 5′ RNA levels such that two qPCR assays within the first 154 bp downstream of the Airn-TSS detected approximately 50–65% of wildtype levels of the normally 118 kb long Airn ncRNA . Airn transcripts were reduced to ∼14% between 1 . 7–8 . 5 kb and to ∼6% between 53–73 kb , while no transcripts were detected at 99 kb . In addition , both initiating and elongating forms of RNAPII were reduced compared to control cells . In contrast the TDR deletion had a minor effect on the length of Airn with transcripts at normal levels for the first two-thirds , but progressively reduced after 68 kb and absent at 99 kb , with the elongating form of RNAPII reduced at the 3′ end of Airn . This indicates that the efficiency of RNAPII to elongate Airn over 118 kb is regulated by the CGI and at least in part , also by the TDRs . Notably , in view of the splicing suppression of wildtype Airn that results in splicing of only 5% of transcripts [21] , the production of all four splice variants was decreased in proportion to the unspliced transcripts indicating that neither the CGI nor TDRs cause splicing suppression . We have previously shown that Airn must be longer than 3 kb and be expressed from a strong promoter , to induce silencing of the overlapped Igf2r promoter [19] , [32] . A loss of paternal Igf2r repression after the CGI deletion that shortened the majority of Airn transcripts to less than 0 . 5% of its normal length is therefore expected , and this deletion was only analysed in the ES cell imprinting model . The TDR deletion although producing normal levels of the Airn transcript that overlap the 28 kb distant Igf2r promoter and elongate up to 90 kb from the Airn-TSS , nevertheless showed a minor loss of paternal Igf2r repression . This was seen as a 1 . 7–2 . 0 fold upregulation of the paternal Igf2r allele in differentiated ES cells and in mid-late gestation embryos and VYS , which was not statistically significantly different from paternal repression on wildtype chromosomes . A similar minor increase in paternal steady-state levels was observed for Slc22a2 and Slc22a3 in the VYS . We lack an explanation for this minor effect . It appears not to arise from changed developmental kinetics of Airn expression that were similar in wildtype and TDRΔ differentiating ES cells , but it may reflect changes in RNAPII post-translational modification not detected with current antibodies . Currently it is unknown if the Airn ncRNA or the act of its transcription induce imprinted expression of Igf2r [16] , [40] . For the Slc22a3 gene that lies 275 kb downstream of Igf2r and is repressed by Airn only in placenta , Airn was shown to localize to the Slc22a3 promoter and to induce imprinted expression by interacting with the G9A histone methyltransferase . However , imprinted expression of Igf2r was not affected in these studies [41] or in studies eliminating PRC2 activity [42] . The results obtained here do not distinguish between a role for the Airn ncRNA or its transcription , but are in agreement with previous analyses that demonstrated a role for high Airn expression and a length longer than 3 kb to repress Igf2r in cis [21] , [32] . The Xist macro ncRNA that induces whole chromosome silencing in female XX mammals has been suggested to share similarities with imprinted repressor ncRNAs such as Airn and Kcnq1ot1 [43] , [44] . Notably Xist contains a set of 5′ direct ‘A’ repeats that are essential for Xist to induce chromosome silencing [45] . The Airn TDRs may have served a similar purpose . Here we show that the TDRs are not required for Airn to repress its target genes as despite the minor loss of paternal repression , imprinted expression is present in TDRΔ cells and mice . However , since the TDRs are required for maternal ICE methylation , they are necessary to ensure expression of the maternal Igf2r allele as it has been previously shown that mouse embryos lacking maintenance DNA methylation , repress both parental Igf2r alleles [22] . The imprinted Kcnq1ot1 macro ncRNA shares many features with the Airn ncRNA and its TSS lies on the 5′ border of the CGI ( Figure 1C ) , which contains a series of TDRs that lack sequence conservation with those in Airn [30] , [46] . Two overlapping deletions have been used to test the function of this region in the Kcnq1ot1 ncRNA . The first is a 657 bp deletion starting just downstream of the Kcnq1ot1-TSS [18] , while the second deletion removed 890 bp and overlapped 40% of the 657 bp deletion [47] . The ability of the deleted Kcnq1ot1 ncRNA to repress flanking genes on the paternal chromosome was found to be unchanged in midgestation embryos for the 657 bp deletion . However , the 890 bp deleted Kcnq1ot1 allele showed a failure to repress some genes in this cluster in a lineage-specific manner that correlated with failure to gain DNA methylation on the derepressed genes . Although the failure to repress flanking genes was attributed to a failure in recruiting DNMT1 due to the lack of the 890 bp region in the ncRNA [47] , both Kcnq1ot1 and Airn are able to repress genes in mouse embryos lacking the Dnmt1 gene that are deficient in genomic methylation [29] , [48] . The TDR deletion described here resulted in loss of the 3′ part of Airn and a minor loss of paternal repression of protein-coding genes with the paternal Igf2r promoter showing a normal gain of DNA methylation ( the Slc22a2 and Slc22a3 genes are repressed in the absence of promoter methylation [49] ) . A direct comparison between the two imprinted clusters is not possible since although Kcnq1ot1 steady-state levels were unchanged in both the deletion experiments [18] , [47] , measurements were only made in the first half of the transcript and it is not known if these deletions affected the full-length of Kcnq1ot1 . Classic mammalian promoter-associated CGIs extend upstream and downstream of the transcription start of the majority of mouse and human genes and these CGIs are considered to have promoter regulatory functions [1] . The promoter region of a CGI is perceived as the region between the 5′ boundary of the CGI and the TSS [50] , although none have been subject to deletion at the endogenous locus and analyzed as described here . Recently , evidence has been accumulating that gene regulation acts not only at the step of RNAPII recruitment by the promoter , but also at later steps of transcription elongation and processing [51]–[53] . The data here show that elements located downstream of the transcription start site are required for RNAPII transcription initiation and elongation and also indicate that CGIs can play a different role to that of the upstream promoter . Reduced Airn transcript length could be explained by alternative polyadenylation site choice that is often seen in mammalian genes [54] . The Airn ncRNA produces four splice variants , three of which have alternative polyadenylation sites spread over 45 kb ( Figure 2 ) [21] . Although premature polyadenylation could explain progressive Airn shortening in TDRΔ and CGIΔ alleles , we think this unlikely for two reasons . First , the genome tiling array analysis shows Airn shortening is gradual and not stepwise , which would be expected from use of alternative polyadenylation sites . Second , the RT-qPCR data indicate that Airn shortening on CGIΔ alleles occurs within the first 570 bp , which does not contain a known polyadenylation site ( http://rulai . cshl . edu/tools/polyadq/polyadq_form . html ) . Cells with a paternal TDRΔ allele showed similar occupancy of the initiating and elongating forms of RNAPII to wildtype cells , except for the 3′ end of Airn where elongating RNAPII was reduced . As Airn transcription initiation is unchanged in TDRΔ cells with the majority of transcripts longer than 68 kb , this indicates the length of Airn is subject to regulation after the switch between paused and elongated transcription . In cells with a CGIΔ allele however , both initiating and elongating RNAPII were decreased compared to wildtype and TDRΔ cells , although ∼60% of wildtype RNA levels were found at the very 5′ end . This indicates that the deletion of the whole CGI affected the ability of the upstream promoter region not only to elongate but also to efficiently initiate Airn transcription . The finding that both the TDR and CGI deletions induced progressive Airn shortening indicates that cumulative elements distributed throughout the CGI play distinct roles in regulating Airn transcription elongation and processivity . An obvious feature involved in regulating expression of a CGI associated gene is DNA methylation . Gain of methylation was not seen on the paternal TDRΔ allele , but up to 70% of DNA methylation was gained on the flanking sequences after paternal CGI deletion . However , Airn 5′ levels only showed a moderate change on the CGIΔ allele . As methylation levels showed a high variability between different alleles , ranging from 9–100% , it could be possible that hypomethylated alleles are still able to initiate Airn transcription as detected by RT-qPCR which specifically analyses Airn transcripts , but not by the RNAPII ChIP which might be relatively less sensitive and also suffers from background problems due to increased Igf2r levels in the overlap region . We therefore suggest that this gain of methylation and not loss of the CGI , explains the reduction in Airn transcription initiation shown by the CGIΔ allele . Since most CpG dinucleotides including those in the body of genes , are methylated when they lie outside CGIs [55] , it is clear that DNA methylation downstream of promoters does not block transcript elongation of endogenous mammalian genes . Furthermore , as no increase in DNA methylation was observed upon TDR deletion on the paternal allele that also induced shortening of Airn , we can exclude DNA methylation as the cause of the length phenotype . Thus , loss of sequences within the CGI and not gain of DNA methylation correlate with loss of full-length Airn . Deletion of the TDRs removed the 3′ half of the CGI from the paternal ICE but did not change its unmethylated status . Notably , controls used in these experiments allowed us to observe for the first time , a low level of DNA methylation on the wildtype paternal ICE in two different undifferentiated ES cell lines , that was fully reversible upon differentiation and was also absent in differentiated primary embryonic fibroblasts . Although Airn is not expressed in undifferentiated ES cells , the paternal ICE is marked by H3K4me3 [31] , [56] , which has been shown to block DNMT3L , an essential cofactor for the de novo methylation complex , from binding histone H3 [57] . The existence of low-level DNA methylation at the Airn promoter in undifferentiated cells despite the presence of H3K4me3 indicates either , that high Airn expression induced during differentiation is required in addition to H3K4me3 to fully block DNA methylation or , that DNA methylation modifies a small number of chromosomes in the population that lack H3K4me3 [58] . Deletion of the whole CGI led to a substantial gain of DNA methylation on the paternal allele that was not reversible upon differentiation but was enhanced after removal of the selection cassette . We attribute this enhancement to the longer period in cell culture required to remove the cassette . Thus the CGI deletion shows that one role located in the first half of the island , is to block DNA methylation on the paternal Airn promoter that is 177 bp upstream from the deleted sequences . Transgene reporter experiments have been used to show that SP1 transcription factor binding sites protect a CGI from DNA methylation [59]–[62] . Furthermore a high CpG density also correlates with protection from DNA methylation by recruitment of the CpG-binding protein CFP1 , which in turn leads to H3K4me3 via recruitment of the SETD1 histone methyltransferase [58] . As the CGI deletion reduces CpG density considerably and removes three predicted SP1 binding sites [21] , this may explain the gain of DNA methylation upon deletion of the CGI . Deletion of the 3′ half of the CGI that included the TDRs , led to loss of ICE methylation following maternal transmission of the deleted allele . The Airn-TDRs are conserved in human and mouse at an organizational level and in their ability to be methylated on the maternal chromosome only [27] , [63] . The conservation of TDRs in the ICE may be explained by the preference of the DNMT3A de novo methyltransferase for an 8–10 bp periodicity in CpG frequency , that is seen in the 12 known maternally-methylated ICE [64] . Previous experiments using multicopy transgenes randomly inserted in the genome have also identified the Airn-TDRs , in particular the three long 172–180 bp monomer repeats , as important for maternal-specific methylation of a hybrid RSVIgmyc imprinted transgene [65] . These experiments also demonstrated a role for the TDRs in maintaining the unmethylated state on paternal transmission . The data reported here that deleted the TDRs from the endogenous Airn CGI , confirm a role for the TDRs in the methylation of the maternal ICE , but do not demonstrate a role in maintaining the unmethylated state of the paternal ICE . The two overlapping 657 and 890 bp deletions cited above for the Kcnq1ot1 downstream CGI [18] , [47] , were not directly tested for their role in the methylation of the maternal ICE . Indirect evidence that indicates no role for these deleted regions comes from the finding that the maternal transmission of the 890 bp deletion did not lead to derepression of Kcnq1ot1 . Together this would indicate that the Airn-TDRs but not the Kcnq1ot1 TDRs , have a function in methylation of the maternal ICE . However two minor caveats could be considered . First , the two overlapping deletions reported from the Kcnq1ot1 downstream CGI might not have removed all necessary sequences and second , these two overlapping deletions left a single loxP site at the site of the deletion , which has been reported to attract DNA methylation [33] . In contrast , the Airn-TDR deletions reported here placed the remaining single loxP site 2 kb upstream from the deletion and we are now able to assign a specific role for the Airn-TDRs in the methylation of the maternal ICE at the endogenous locus . Together the data presented here show that the CGI lying immediately downstream of the Airn transcription start regulates both the epigenetic and transcription state of its upstream promoter . Classically , with the exception of retrotransposons , RNA polymerase II promoters are viewed as lying upstream of the transcription start [66] , [67] . In contrast , the majority of CGIs can be seen in Figure 1C to extend downstream of the transcription start , with some located entirely downstream of the transcription start . The importance of CGIs as regulators of gene expression has been emphasised with the advent of genome-wide studies showing CGIs are not only associated with genes showing tissue-specific and inducible expression but are also present in large numbers as orphan CGIs not associated with annotated promoters [1] , [2] , [68] . The data here identify a role for the downstream Airn CGI to regulate its epigenetic state and the production of transcripts expressed at sufficiently high levels and of sufficient length to silence flanking target genes . Future work will determine how this regulation is achieved and if these features are shared by CGIs regulating non-imprinted gene expression .
Mice were bred and housed at the Forschungsinstitut für Molekulare Pathologie GmbH , Dr . Bohr-Gasse 7 , 1030 Vienna , Austria in strict accordance with national recommendations described in the “IMP/IMBA Common Institutional policy concerning the care and use of live animals” with the permission of the national authorities under Laboratory Animal Facility Permit MA58-0375/2007/4 . Blastocyst injections and chimeric mice were prepared under the permit M58/003079/2009/8: Production of Chimeras , Examination of Germline , Examination of Gene Effects in Parents and Successor Generations ( Model B ) . Mouse embryos were obtained after humane killing of pregnant female mice by cervical dislocation by skilled qualified personnel . All targeting vectors were generated from a plasmid with a 6 . 4 kb 129Sv homology region ( chr17:12931344–12937792/NCBI37-mm9 ) . In the TDRΔ construct a 692 bp SacII-NsiI fragment ( chr17:12934848–12935543 ) was deleted . The selection cassette ( loxP ) - ( HSVTk-Neomyocin-SV40polyA ) - ( HSVTk-ThymidineKinase+polyA ) - ( loxP ) for the ES cell imprinting model and ( loxP ) - ( Pgk1-Neomycin-Pgk1polyA ) - ( loxP ) for blastocyst injection was subcloned into the NheI site at chr17:12932836 . In the CGIΔ construct the 1129 bp deletion ( chr17: 12934414–12935543 ) was created by PCR ( primers: TGGAACCCTTCCTTTGCGGAATC - TGCATGAGGGTGCCACACTCCT ) . The selection cassette: ( loxP ) - ( Pgk1-Neomycin-Pgk1polyA ) - ( loxP ) was inserted at the same position as for TDRΔ . Electroporation and neomycin-selection were performed using standard conditions into S12/+ cells ( a D3 feeder-dependent 129 ES line previously modified to carry a SNP in Igf2r exon 12 [31] ) for the ES cell imprinting model experiments and into the feeder-dependent BL6/129 intraspecies A9 ES cell line for blastocyst injection . The selection cassette in the ES cells used for the ES cell imprinting model was removed by electroporation of the pMC-Cre plasmid leaving a single loxP site 2 kb upstream of each deletion . One A9 ES cell clone carrying the TDRΔ+cas allele was injected into C57BL/6J blastocysts and transferred into pseudo-pregnant recipient mice and one chimeric male mouse was obtained who transmitted the TDRΔ+cas allele . The selection cassette was removed by crossing TDRΔ+cas males with MORE-Cre females . Heterozygous TDRΔ mice were mated with wildtype FVB or FVB with a Thp allele and embryos were isolated at 12 . 5 dpc or 13 . 5 dpc . Visceral yolk sacs were isolated as described in [29] . ES cells were grown on irradiated primary mouse embryonic fibroblasts using standard conditions and differentiation induced by feeder-depletion , LIF-withdrawal and 0 . 27 µM retinoic acid . RNA was isolated using TRIreagent and was DNaseI treated prior to reverse transcription . Realtime qPCR for Taqman assays was as described [21] . SybrGreen assays used 100 nM primers and cycling conditions: 5 min 95°C , 40 cycles: 15 sec 95°C+1 min 60–65°C . Allele-specific qPCR was as described [31] with 5 mM MgCl2 . The assay specificity was improved by a mismatch in the primer body . See for primers and probes . All assays were normalised to CyclophilinA . DNA isolation and blots were performed using standard techniques and ImageJ quantified signal intensities . For some blots the contrast was linearly enhanced with Adobe Photoshop . RNA hybridization to genome tiling array was performed by Source BioScience LifeSciences , Berlin , as described [69] . The data were Tukey bi-weight normalised before analysis . Relative signal intensities ( normalised to the average signal in the region ) of overlapping windows of 9 tiles were averaged and each displayed data point is the average of 20 windows , the standard deviation is displayed as error bars . Two pseudogenes in the region , Au76 and LA41 , were removed from the analysis . RNA sequencing:1 µg of total RNA was treated with the RiboZero kit ( Epicentre ) and two strand-specific RNA-Seq libraries prepared using the ScripSeq kit and two compatible barcodes ( Epicentre ) according to the manufacturer's protocol . Sequencing and read alignment to the mouse genome ( mm9 ) was as described [70] . The region shown in Figure 6A was divided into non-overlapping 3 . 2 kb windows , reads mapping to the forward or reverse strand in these windows were counted and the log2 ratio of these counts was calculated and plotted . Windows overlapping Igf2r exons and the Au76 and the LA41 pseudogenes were removed from the plot . Preparation of soluble chromatin and chromatin immunoprecipitation assays were carried out as described [71] . 25 µg of sonicated chromatin were diluted 10-fold and precipitated overnight with the following antibodies: Phospho RNA Pol II ( S5 ) ( Bethyl Laboratories A300-655A ) , Phospho RNA Pol II ( S2 ) ( Bethyl Laboratories A300-654A ) or rabbit IgG ( Invitrogen 10500C ) as control . Chromatin antibody complexes were isolated using Protein A magnetic beads ( Dynabeads ) . The extracted DNA was then used for qPCR as described above . A 1∶20 dilution of input DNA was assayed . 1 µg genomic DNA from undifferentiated ES cells grown on feeder cells carrying a homozygous ICE deletion or 1 µg genomic DNA from 12 . 5–13 . 5 dpc embryos was RNaseA treated , EcoRI digested and Bisulfite converted using the EpiTect Bisulfite Kit ( Qiagen ) . PCR amplification used JumpStart Taq DNA Polymerase ( Sigma ) , primers: DMR2-F4 ( GGGGAATTGAGGTAAGTTAGGGTTTT ) with DMR2-R4 ( TCTTATAACCCAAAAATCTTCACCCTAAC ) for wt alleles or DMR2-R9 ( AACACCTTCATATACCCCTAAACAC ) for TDRΔ and CGIΔ alleles [8] , cycle conditions: 94°C 1 min , 40 cycles of 94°C 1 min , 60°C 1 min , 72°C 1 min then 72°C 5 min . PCR fragments were gel-purified , subcloned and plasmid DNA from single colonies sequenced using standard primers . Analysis and sequence quality control used BiQAnalyzer and standard settings [72] . Each CGI ( UCSC Genome Browser , mm9 ) with flanking regions ( 50% of the CGI length upstream and downstream ) was divided into 100 equal-sized bins ( i . e . , parts ) and the number of RefSeq genes ( USCS mm9 ) was calculated with a transcription start site in each bin . Bins were summed for all CGIs and plotted using Microsoft Excel . For qPCRs an unpaired t-test was performed using www . graphpad . com/quickcalcs/ .
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CpG islands are CG-rich regions associated with the majority of mammalian promoters . Although widely considered to be necessary for promoter activity , their exact function is unknown . CpG islands are mostly unmethylated during development and differentiate with notable exceptions including imprinted genes and genes on the inactive X chromosome . Here we analysed how the imprinted Airn ncRNA CpG island , that is normally methylated on the maternal and unmethylated on the paternal chromosome , regulates its associated upstream promoter . We used embryonic stem cells or mice carrying a deletion of either the whole CpG island or of the second half that contains an unusual series of tandem direct repeats . Our results show that the CpG island is needed for efficient transcription of the Airn promoter on the paternal chromosome and to keep it free from DNA methylation . The series of tandem direct repeats plays a minor role in regulating the length of the Airn transcript on the paternal chromosome but is essential for DNA methylation of the Airn promoter on the maternal chromosome . These results show that CpG islands do not only function as classical promoters to bind RNA polymerase II and initiate transcription , but can also play other roles in regulating transcription processivity as well as the epigenetic state of their associated promoter .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"chromosome",
"biology",
"gene",
"expression",
"genetics",
"epigenetics",
"biology",
"genomics",
"genetics",
"and",
"genomics"
] |
2012
|
A Downstream CpG Island Controls Transcript Initiation and Elongation and the Methylation State of the Imprinted Airn Macro ncRNA Promoter
|
Dengue viruses ( DENVs ) are mosquito-borne flaviviruses and the causative agents of dengue fever and dengue hemorrhagic fever . As there are four serotypes of DENV ( DENV1-4 ) , people can be infected multiple times , each time with a new serotype . Primary infections stimulate antibodies that mainly neutralize the serotype of infection ( type-specific ) , whereas secondary infections stimulate responses that cross-neutralize 2 or more serotypes . Previous studies have demonstrated that neutralizing antibodies induced by primary infections recognize tertiary and quaternary structure epitopes on the viral envelope ( E ) protein that are unique to each serotype . The goal of the current study was to determine the properties of neutralizing antibodies induced after secondary infection with a different ( heterotypic ) DENV serotypes . We evaluated whether polyclonal neutralizing antibody responses after secondary infections consist of distinct populations of type-specific antibodies to each serotype encountered or a new population of broadly cross-neutralizing antibodies . We observed two types of responses: in some individuals exposed to secondary infections , DENV neutralization was dominated by cross-reactive antibodies , whereas in other individuals both type-specific and cross-reactive antibodies contributed to neutralization . To better understand the origins of type-specific and cross-reactive neutralizing antibodies , we analyzed sera from individuals with well-documented sequential infections with two DENV serotypes only . These individuals had both type-specific and cross-reactive neutralizing antibodies to the 2 serotypes responsible for infection and only cross-reactive neutralizing antibodies to other serotypes . Collectively , the results demonstrate that the quality of neutralizing ( and presumably protective ) antibodies are different in individuals depending on the number of previous exposures to different DENV serotypes . We propose a model in which low affinity , cross-reactive antibody secreting B-cell clones induced by primary exposure evolve during each secondary infection to secrete higher affinity and more broadly neutralizing antibodies .
Dengue Virus ( DENV ) is a mosquito-borne flavivirus and the causative agent of dengue fever and dengue hemorrhagic fever ( DHF ) [1] . Several hundred million people are estimated to acquire DENV infections each year[2] . Dengue infections can be clinically inapparent or lead to symptoms that range from an undifferentiated fever to severe DHF and dengue shock syndrome ( reviewed in [3 , 4] ) . The DENV complex consists of 4 viruses designated as serotypes ( DENV1-4 ) [1] . Primary infection by DENV leads to long-term protection against the serotype of infection ( homologous serotype ) but not other serotypes ( heterologous serotypes ) [5–7] . Subsequent secondary infection with a new serotype results in serotype cross-neutralizing antibodies that correlate with durable protection against 2 or more serotypes [5 , 7] . Recent studies have defined the properties of human antibodies responsible for serotype-specific neutralization after primary infection [8–12] . In the present study we investigated the properties of serum neutralizing antibodies produced after recovery from secondary DENV infections . The DENV envelope ( E ) protein , which binds to cellular receptors and mediates viral entry and fusion , is the main target of neutralizing and protective antibodies [13] . The ectodomain of E protein is composed of three domains: I , II and III ( EDI , EDII and EDIII ) [14] . Each DENV particle has 180 monomers of E that are organized into 90 dimers that cover the entire surface of the virus . The E proteins are arranged with icosahedral symmetry with each asymmetric unit containing portions of three homodimers[15] . After primary DENV infection , people develop a mix of DENV serotype cross-reactive and type-specific antibodies . The cross-reactive antibodies are weakly neutralizing and have been implicated in antibody dependent enhanement of DENVs during secondary infections [8 , 16–18] . The serotype-specific antibodies are strongly neutralizing and , presumably , responsible for long-term protection aganst re-infection with the same serotype . Quaternary epitopes formed after assembly of E molecules into higher order structures required for virion assembly are major targets of type-specific neutralizing antibodies [8–12] . Here we report on the properties of serum neutralizing antibodies in people exposed to 2 or more DENV infections . The studies were designed to test if people exposed to secondary infections have neutralizing antibodies that mainly recognize epitopes that are conserved between serotypes or epitopes that are unique to each serotype previously encountered . Unlike primary DENV infections that result in predominantly serotype-specific polyclonal neutralizing antibody responses , some people exposed to secondary infections had neutralizing antibodies that mainly recognized epitopes conserved between serotypes , while others had antibodies that targeted both type-specific and conserved epitopes .
Blood donations were obtained from individuals who had traveled to dengue-endemic regions and experienced a primary DENV infection . These human samples were obtained with informed consent approximately 2 to 10 years after DENV infection . All blood donations were collected in compliance with the Institutional Review Board of the University of North Carolina at Chapel Hill . Written informed consent was obtained from all subjects before participation in the study . Blood donations were obtained from individuals who had traveled to dengue-endemic regions or people living in endemic areas . All blood donations were collected in compliance with the Institutional Review Board of the University of North Carolina at Chapel Hill and the University of California at Berkeley . Written informed consent was obtained from all subjects before participation in the study . WHO reference strains , DENV1 ( West Pac 74 ) , DENV2 ( S-16803 ) , DENV3 ( CH-53489 ) and DENV4 ( TVP-376 ) were used in the present study . The strains were grown in C6/36 mosquito cells to generate infectious stocks and Vero-81 mammalian cells to generate purified antigen . DENVs from culture media were purified by density gradient and ultracentrifugation as previously described [23] . DENV2 ( NGC ) purified live virion antigen was purchased from Microbix Biosystems , Inc . ( Mississauga , Ontario , Canada ) . DENV2 envelope protein was produced as a soluble recombinant protein ( amino acids 1–397 of S-16803 ) using the Bac-to-Bac Baculovirus Expression System ( Invitrogen Life Technologies ) and Sf9 insect cells . The protein was purified from cell culture supernatant using an antibody column coated with 4G2 , a serotype-cross reactive mouse monoclonal that targets a conserved region on the envelope protein . Structure of the recombinant protein was verified by ELISA using monoclonal antibodies that bind to well-defined epitopes on all three domains of E protein . DENV neutralizing activity of human immune sera was assessed using a flow cytometry-based assay with U937 human monocytic cells stably transfected with DC-SIGN as previously described [24] . In brief , serially diluted human sera were incubated with virus for 45 min at 37°C followed by the addition of U937 DC-SIGN cells . Cells were incubated with virus for 2 hours at 37°C , washed with media to remove immune sera and unbound virus and incubated for 24 hours at 37°C . Cells were fixed , permeabilized and stained with 2H2-Alexa Flour 488 , a mouse monoclonal that binds to DENV pre-membrane protein . Infected cells were quantified using a Guava flow-cytometer ( Milipore ) . Stained cells were analyzed using GraphPad Prism version 6 . 00 ( La Jolla California USA , www . graphpad . com ) to calculate 50% neutralization titers as previously described [25] . We determined if depletion of specific antibodies resulted in statistically significant ( P<0 . 05 ) changes in Neut50 values by comparing neutralization curves using the Extra Sum of Squares F test ( Non-linear regression: Compare function in GraphPad Prism version 6 . 00 ) . Each control and antigen depleted serum sample was tested in a single neutralization assay in duplicate using all 4 DENV serotypes . ELISAs were conducted for both confirmation of depletion and the assessment of binding activity to all four DENV serotypes following depletion of human DENV immune sera . Plates were coated with either 50 ng of purified DENV or 100 ng of DENV rE in carbonate buffer at pH 9 . 6 for 2 hours at room temperature . The plates were blocked with Tris-buffered Saline containing 0 . 05% Tween 20 with 3% Normal Goat Serum followed by an incubation with a 1:40 dilution of control or DENV depleted human sera for 1 hour at 37°C . Alkaline phosphatase conjugated Goat anti-human IgG ( Sigma ) was added to the plates for 1 hour at 37°C . The plates were washed and then developed by adding p-nitrophenyl phosphate substrate . The optical density at 405nm was recorded using a spectrophotometer . Purified DENV was absorbed onto 4 . 5-μm Polybead polystyrene microspheres ( Polysciences , Inc . ) at a bead ( ul ) to ligand ( ug ) ratio of 5:2 . Beads were washed three times with 0 . 1M Borate buffer ( pH 8 . 5 ) followed by an overnight incubation with purified DENV and 0 . 1M Borate buffer ( pH 8 . 5 ) at room temperature ( RT ) . Control beads were incubated with an equivalent amount of BSA . The control and virus absorbed beads were then blocked with a 10 mg/ml BSA solution for 30 minutes at RT followed by four washes with PBS . DENV-specific antibodies were depleted from human sera by incubating virus absorbed beads with human sera diluted 1:10 in PBS for 45 minutes at 37°C . Typically three cycles were performed to remove dengue-specific antibodies in each serum sample . Successful depletion of DENV-specific antibodies was confirmed via an ELISA with purified DENV coated plates . Purified DENV2 rE protein was conjugated to magnetic dynabeads M-270 Epoxy ( Invitrogen by Life Technologies ) with a bead ( mg ) to ligand ( ug ) ratio of 5:1 . Beads were washed three times with 0 . 1M Sodium Phosphate ( 7 . 4 ) followed by an overnight incubation with equal volumes of purified rE protein , 0 . 1M Borate buffer ( pH 9 . 5 ) and 3M Ammonium sulfate at 37°C . Control beads were incubated with the equivalent amount of BSA . Control and DENV2 rE conjugated beads were then blocked by incubating with a 10 mg/ml BSA solution followed by four washes with PBS . DENV2 rE-specific antibodies were depleted from human sera by incubating rE conjugated beads with human sera diluted 1:10 in PBS for 45 minutes at 37°C three times with end-over-end mixing . Successful depletion of DENV2 rE-specific antibodies was confirmed via an ELISA with purified DENV2 rE coated plates . After depletion of sera using DENV2 rE antigen , the percentage of rE binding DENV2 neutralizing Abs were calculated using the following formula .
Depletion of two primary DENV2-immune sera ( DT001 and DT110 ) using beads coated with DENV2 resulted in a reduction of binding to DENV2 as well as DENV1 , 3 and 4 , demonstrating removal of cross-reactive and type-specific antibodies in the sample ( S1 Fig ) . Antibody depletion with the homologous ( serotype of infection ) DENV2 antigen led to a significant ( P<0 . 05; Extra Sum of Squares F test to compare neutralization curves ) drop in DENV2 neutralization ( S1 Fig ) . Following depletion with DENV1 , 3 and 4 coated beads , ELISA confirmed removal of cross-reactive antibodies , while still retaining DENV2 type-specific antibodies in the sample ( S1 Fig ) . After removal of cross-reactive antibodies , the samples retained most of the DENV2 neutralizing activity and the neutralization titers of the control and depleted samples were not significantly different ( P>0 . 05; Extra Sum of Squares F test to compare neutralization curves ) ( S1 Fig ) . Fig 1A and 1B ) . These results are in agreement with previous reports that following primary DENV infections neutralization of the homologous serotype is mainly mediated by type-specific antibodies [8] . Following secondary DENV infections , individuals are , typically , protected from two or more serotypes . We tested whether individuals exposed to secondary infections generate individual type-specific neutralizing populations of antibodies against each serotype or a broadly neutralizing cross-reactive population against 2 or more serotypes . We depleted secondary infection sera using beads coated with DENV2 or a mixture of DENV1 , 3 and 4 and performed neutralization assays ( S2 Fig ) . In some individuals DENVs were almost exclusively neutralized by cross-reactive antibodies , while others had a mixture of cross-reactive and type-specific neutralizing antibodies ( Fig 1C , 1D , 1E , 1F and 1G ) . For example , subject DT000 had high levels of neutralizing antibodies to all 4 serotypes ( Table 1 ) . When DENV2 binding antibodies were depleted from the DT000 sample , there was a significant loss ( P<0 . 05; Extra Sum of Squares F test ) of DENV2 neutralization as expected ( S2 Fig ) . However , there was also a significant loss ( P<0 . 05; Extra Sum of Squares F test ) of DENV1 , 3 and 4 neutralization ( S2 Fig ) indicating that these serotypes were neutralized by cross-reactive antibodies ( Fig 1C ) . Reciprocal depletion of DT000 immune sera with DENV1 , 3 and 4 antigens resulted in a significant loss ( P<0 . 05; Extra Sum of Squares F test ) of DENV2 neutralization , indicating that cross-reactive antibodies were responsible for neutralization of DENV2 as well ( Fig 1C and S2 Fig ) . Subject DT121 , another subject with neutralizing antibodies to all 4 serotypes , also had a response that was dominated by cross reactive neutralizing antibodies ( Fig 1E and S2 Fig ) . DT130 , a subject that strongly neutralized DENV1 and 2 but not 3 and 4 ( Table 1 ) , had a mixture of type-specific and cross-reactive neutralizing antibodies ( Fig 1D ) . Following removal of DENV2 binding antibodies , we observed a major loss ( P<0 . 05; Extra Sum of Squares F test ) of DENV2 neutralization and only a partial loss of DENV1 neutralization ( S2 Fig ) . This result indicates that both type-specific and cross reactive antibodies are responsible for the high DENV1 neutralizing activity in this individual ( Fig 1D ) . Reciprocal depletion with DENV1 , 3 and 4 antigens removed all the DENV2 neutralizing activity demonstrating that cross-reactive antibodies were responsible for neutralization ( Fig 1D and S2 Fig ) . Samples DT025 and DT027 also exhibited a similar pattern in which both type-specific and cross-reactive antibodies contributed to DENV neutralization ( Fig 1F and 1G , and S2 Fig ) . Unlike primary DENV infections that stimulate durable serotype-specific neutralizing antibody responses , we conclude that secondary infections result in more complex mixtures of neutralizing antibodies that recognize serotype-specific and cross-reactive epitopes . The proportions of these two classes of antibodies varied between individuals exposed to secondary infections . To better understand different patterns of type-specific and cross reactive neutralizing antibodies in people exposed to secondary DENV infections , we analyzed serum samples from 3 individuals with well documented histories of two sequential infections with different serotypes of DENV ( Table 2 ) . These samples were obtained from a long-term prospective pediatric DENV cohort study in Nicaragua [22] . Two of the subjects had been exposed to a first DENV2 infection followed by a second DENV3 infection ( Subjects 985 . 8 and 3428 . 8 ) . One subject had been exposed to a DENV1 infection followed by a DENV3 infection ( Subject 2934 . 7 ) . In sera collected several months after the second infection , all three subjects had varying levels of neutralizing antibodies to at least 3 different serotypes ( Table 2 ) . We depleted each post-second infection sample using beads coated with the DENV serotype of the 1st or 2nd infection and performed neutralization assays to estimate levels of cross-reactive and type-specific neutralizing antibodies against each DENV serotype . All three subjects had a mixture of type-specific and cross-reactive neutralizing antibodies directed to the serotypes of known 1st and 2nd infection ( Fig 2 and S3 Fig ) . For serotypes not “seen” by the individual , neutralization was driven by cross-reactive antibodies only ( Fig 2 and S3 Fig ) . Our previous studies with primary DENV immune sera revealed that type-specific neutralizing antibodies mostly targeted quaternary epitopes expressed on E protein dimers or higher order structures but not recombinant E protein ( rE ) , which is mainly a monomer in solution[8] . We , next , addressed if neutralizing antibodies induced by secondary DENV infections targeted simple or quaternary epitopes on E protein . When convalescent immune sera from individuals exposed to primary DENV2 infections were depleted of DENV2 rE binding antibodies , we observed no significant loss of DENV2 neutralization ( P>0 . 05; Extra Sum of Squares F test ) , confirming the importance of quaternary structure neutralizing antibody epitopes after primary infection ( Fig 3 and S4 Fig ) . When convalescent immune sera from individuals exposed to secondary DENV infections ( Table 1 ) were depleted of DENV2 rE binding antibodies and tested for DENV neutralization , we observed a significant decrease in DENV2 neutralizing antibody titers ( P<0 . 05; Extra Sum of Squares F test ) ( Fig 3 and S5 Fig ) . Our results indicate that epitopes displayed on rE protein are a major target of DENV2 neutralizing antibodies after secondary infections but not primary DENV2 infections .
Recently several studies have described the properties of neutralizing antibodies generated following primary DENV infections and the epitopes targeted by these antibodies [8–10 , 18 , 26] . Although primary infections stimulate both DENV serotype-specific and cross reactive binding antibodies , only the type-specific antibodies have been linked to durable neutralizing and protective responses . These type-specific antibodies target tertiary and quaternary structure E protein epitopes displayed on the surface of the virus [8–10 , 12] . In the current study we observed that the majority of neutralizing antibodies that develop after secondary DENV infections recognize serotype cross reactive epitopes . The cross-reactive neutralizing antibodies bound to simple epitopes on soluble E protein as well as more complex epitopes displayed on the intact DENV particle . Indeed , serotype cross-reactive and neutralizing human monoclonal antibodies isolated from people exposed to secondary DENV infections bind with high affinity to simple epitopes on domain II as well as quaternary epitopes that span across two E proteins forming a single dimer [16 , 27–29] . Following secondary infections , some people had a single population of DENV serotype cross-reactive and cross-neutralizing antibodies , whereas others had a mixture of type-specific and cross-reactive neutralizing antibodies . We suspect that subjects like DT000 and 121 who mainly had cross-reactive neutralizing antibodies are likely to have had multiple DENV exposures resulting in broad neutralization of all 4 serotypes . Subjects like DT27 and 130 , who had a mixture of type-specific and cross-reactive neutralizing antibodies are likely to represent people who have been exposed to sequential infections with 2 serotypes only . Indeed , when we tested samples from three subjects enrolled in a long-term prospective pediatric cohort study in Nicaragua with know exposure to just two sequential DENV serotype infections , all three subjects had a mixture of type-specific and cross reactive neutralizing antibodies to the serotypes responsible for the first and second infections . Tsai and colleagues recently characterized DENV neutralizing antibodies in volunteers infected with a single serotype monovalent live attenuated DENV vaccine or sequentially infected with two monovalent DENV vaccines representing different serotypes [30] . They also observed type-specific neutralizing antibodies in volunteers who received the single serotype vaccine and mixture of type-specific and cross reactive neutralizing antibodies after sequential infection with 2 different serotypes . We propose that low affinity DENV cross reactive memory B-cells derived from primary infections undergo antibody somatic hyper mutation and each subsequent DENV exposure selects and expands rare affinity matured clones with greater neutralization breadth and potency ( Fig 4 ) . This model is supported by studies comparing the avidity and neutralization of both monoclonal antibodies and polyclonal sera from people after primary and secondary DENV infections . Analysis of polyclonal human sera following DENV infections revealed that the avidity of DENV antibodies following secondary infection was higher than that of antibodies generated following a primary infection [31] . In agreement with this , studies focusing on group-reactive MAbs derived from primary and secondary DENV infected patients found that the group-reactive MAbs from patients with secondary infection had stronger neutralization potencies and higher binding avidities than those derived from patients with primary infection [29 , 32 , 33] . Additional studies have identified a class of broadly neutralizing human antibodies produced by plasmablasts in hospitalized cases of secondary DENV infections . Structural analysis of these broadly neutralizing antibodies in complex with rE revealed that these antibodies recognize serotype invariant sites at the E dimer interface . Collectively , these studies support the idea that low affinity , weakly neutralizing antibody clones generated followed primary DENV infections give rise to antibodies of increasing breadth and neutralization potency with each subsequent exposure ( Fig 4 ) . In this study we analyzed in-depth convalescent blood samples from 10 individuals exposed to DENV infections . While the small sample size is a weakness , it is challenging to perform antibody depletion studies on larger panels because of the complexity of the studies and the volume of blood required . Another limitation of our study is that the infection history of some of the study subjects was inferred by the neutralizing antibody profile and travel history , therefore , definite conclusions relating antibody population characteristics to the number of secondary infections cannot be made . However , three subjects with known sequences of two DENV infections support our conclusion that sequential infections with two serotypes result in a mixture of cross-reactive and type-specific neutralizing antibodies to serotypes responsible for infections , while inducing cross-reactive neutralizing antibodies only to the serotypes not “seen” by the host . Studies are currently in progress using additional samples from human dengue cohort studies with well-defined infection histories to further test our model about the roles of sequential infections and antibody somatic hypermutations in cross-protective immunity . Our studies show that the prior DENV immune status of an individual has a profound effect on the quality of neutralizing antibodies that develop after an infection . These findings are relevant to the development of live attenuated dengue vaccines that strive to provide simultaneous protection against all four serotypes . In DENV naïve individuals who are vaccinated with a single dose of live attenuated DENV vaccines , protection is likely to require type-specific protective antibody responses to each serotype . In the case of tetravalent live DENV vaccine formulations , these responses may not be balanced towards each serotype [34 , 35] . On the other hand , DENV-immune individuals receiving a tetravalent vaccine are likely to generate broadly neutralizing and protective responses even if individual components in the vaccine perform poorly . Indeed , a recent live attenuated tetravalent dengue vaccine efficacy trial demonstrated higher efficacy in dengue pre-immune individuals compared to DENV naïve individuals [36 , 37] . Overall , it is clear that a better understanding of the antibody response transition from primary to secondary infection is needed to understand and improve the performance of dengue vaccine in the current pipeline .
|
The four dengue virus serotypes are emerging mosquito-borne flaviviruses and the causative agents of dengue fever and dengue hemorrhagic fever/dengue shock syndrome . Infected people develop protective immunity to the infecting serotype but remain susceptible to secondary infections with new serotypes . Both antibodies and T-cells are responsible for protection against re-infection by the same serotype . The goal of the current study was to analyze the properties of antibodies in people who have been exposed to a single or secondary dengue virus infections . We found that people exposed to a single infection have neutralizing antibodies that mainly bind sites that are unique to the infecting serotype . In contrast , secondary infections induced more complex mixtures of neutralizing antibodies that target regions that unique to each serotype and regions that are conserved between serotypes . Our results have implications for understanding protective responses after natural infections as well as responses induced by dengue vaccines .
|
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2017
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Dissecting the human serum antibody response to secondary dengue virus infections
|
Scrub typhus is endemic in the Asia-Pacific region including China , and the number of reported cases has increased dramatically in the past decade . However , the spatial-temporal dynamics and the potential risk factors in transmission of scrub typhus in mainland China have yet to be characterized . This study aims to explore the spatiotemporal dynamics of reported scrub typhus cases in mainland China between January 2006 and December 2014 , to detect the location of high risk spatiotemporal clusters of scrub typhus cases , and identify the potential risk factors affecting the re-emergence of the disease . Monthly cases of scrub typhus reported at the county level between 2006 and 2014 were obtained from the Chinese Center for Diseases Control and Prevention . Time-series analyses , spatiotemporal cluster analyses , and spatial scan statistics were used to explore the characteristics of the scrub typhus incidence . To explore the association between scrub typhus incidence and environmental variables panel Poisson regression analysis was conducted . During the time period between 2006 and 2014 a total of 54 , 558 scrub typhus cases were reported in mainland China , which grew exponentially . The majority of cases were reported each year between July and November , with peak incidence during October every year . The spatiotemporal dynamics of scrub typhus varied over the study period with high-risk clusters identified in southwest , southern , and middle-eastern part of China . Scrub typhus incidence was positively correlated with the percentage of shrub and meteorological variables including temperature and precipitation . The results of this study demonstrate areas in China that could be targeted with public health interventions to mitigate the growing threat of scrub typhus in the country .
Scrub typhus , also known as tsutsugamushi disease , is endemic in the so-called “tsutsugamushi triangle” area that includes Pakistan and Afghanistan in the west , far-eastern Russia and Japan in the north , and northern Australia in the south [1] . The causative bacterium of this disease , Orientia tsutsugamushi ( O . tsutsugamushi ) , is spread to humans bitten by infected species of trombiculid mites [2 , 3] . The clinical presentation of scrub typhus is characterized by high fever and rash or typical eschar at the location of the bite , which can progress to multiple organ failure and even death in some cases [4–6] . It is estimated that over one billion people are currently living in at-risk areas and approximately one million cases occur around the world annually [2 , 7] . In recent years , there has been a drastic increase in both the frequency and geographic distribution of scrub typhus cases , which could signal the re-emergence of this neglected tropical disease [8–11] . The first reported case of a human infected with scrub typhus in China was identified in the southern province of Guangdong in 1948 [12] . Until the 1980s , scrub typhus cases primarily occurred in the regions south of Yangtze River with established natural foci including Zhejiang in the east and Yunnan in the west part of China [13 , 14] . However , with rapid societal development , changing environment , climate change , population movement , better recognition by health care professionals and ever-improving detection techniques , both sporadic cases and disease outbreaks began to be identified in the northern provinces of Shandong , Jiangsu , Tianjin and Beijing , as well as the emergence of new natural foci in the past three decades [13 , 15–17] . Currently , the disease is widespread in most of the provinces in mainland China , where the incidence has increased rapidly in recent years . Despite the recent resurgence of illness , scrub typhus remains a neglected tropical disease that is able to simultaneously impact tourism and military activities in China [9 , 18] , with the potential to cause a significant burden on both public health and economy . In the past decades , spatiotemporal analysis techniques have been widely applied in the surveillance of infectious disease and outbreak investigations [19–21] . Previous studies have identified clusters of reported cases of scrub typhus in different provinces of China and reported that the geographic distribution of the disease varied by year [22 , 23] . Some studies have revealed that environmental variables were important drivers in the transmission of scrub typhus [7 , 24] . However , there are few studies that examine both the spatiotemporal dynamics and potential risk factors in scrub typhus transmission across China . Thus , the objectives of this study were to describe the temporal trends in scrub typhus incidence , to detect spatiotemporal clusters of scrub typhus cases at the county level , and to identify the physical environmental variables associated with scrub typhus incidence , which would be helpful for the health administration officers and public health workers to the implementation of effective intervention measures targeted toward high-risk areas and populations .
This study was approved by the Ethics Committee of Beijing Institute of Disease Control and Prevention . All the data analyzed in this study were de-identified to protect patient confidentiality . In China , scrub typhus is a vector-borne notifiable disease; attending physicians are required by law to report to the China Center for Disease Control and Prevention through the China Information System for Disease Control and Prevention ( CISDCP ) . Scrub typhus case reports include basic demographic and clinical data including gender , age , occupation , residential address , date of onset of symptoms , laboratory diagnosis , and clinical outcome for each case . Data from January 2006 through December 2014 were obtained from CISDCP . All scrub typhus cases were confirmed according to the diagnostic criteria issued by the Ministry of Health of the People’s Republic of China . The criteria for a confirmed case of scrub typhus include epidemiological exposure histories ( travelling to an endemic area and contact with chiggers or rodents within 3 weeks before the onset of illness ) , clinical manifestations ( such as high fever , lymphadenopathy , skin rash and eschar or ulcers ) , and also at least one of the laboratory diagnosis: a 4-fold or greater rise in serum IgG antibody titers between acute and convalescent sera by using indirect immunofluorescence antibody assay ( IFA ) , or detection of O . tsutsugamushi by polymerase chain reaction ( PCR ) in clinical specimens , or isolation of O . tsutsugamushi from clinical specimens [24–26] . Demographic data for each county was obtained from the National Bureau of Statistics of China . Environmental and meteorological data from 2006 to 2014 were collected . Land cover variables such as the percentage coverage of cropland , forest , shrub , grassland , built-up land and water bodies were collected from the data on land cover 2005 and 2009 released by European space agency ( http://www . esa . int ) . Meteorological variables including temperature , relative humidity and precipitation were obtained from the Chinese Academy of Meteorological Sciences ( www . cams . cma . gov . cn ) . In order to perform spatial analysis , the data set of cases was aggregated at the county level as the spatial unit for analysis . In mainland China , there are 31 provinces ( or municipalities ) comprised of 2 , 922 counties , with population sizes ranging from 7 , 123 to 5 , 044 , 430 , with geographic areas ranging in size from 5 . 4 to 197 , 346 square kilometres . All cases were geocoded and matched to the county-level administrative boundaries using the ArcGIS software ( version 9 . 3 , ESRI , Redlands , CA ) . The cases of scrub typhus reported at the county-level were aggregated to provide a national data set of monthly cases for time-series analyses . The monthly incidence as well as the cumulative number of cases was tabulated for visualization , along with the graphical assessment of the cumulative annual cases with various trends including linear , polynomial , and exponential growth curves using Excel ( Microsoft , Redmond , WA ) . For the assessment of the seasonal trend in scrub typhus incidence , both the annual and long-term trends were assessed . The average monthly incidence for every calendar month ( January to December ) was compared to the average incidence in January ( the lowest monthly incidence and beginning of each year ) using a categorical Poisson regression model to generate incident rate ratios ( IRR ) and 95% confidence intervals ( CI ) for the IRR . Temporal autocorrelation between the monthly reported cases of scrub typhus and seasonal trend in incidence was assessed using time lags between 0 and 60 months . Seasonal trends were classified by maximum autocorrelations of 12 months and minimum autocorrelations observed every 6 months , that also demonstrated a sinusoidal oscillation with respect to time . Local Indicators of Spatial Association ( LISA ) were used to assess the spatial pattern of scrub typhus incidence at the county level during the study period . LISA was used to identify significant hotspots ( High-High ) , coldspots ( Low-Low ) , and outliers ( High-Low and Low-High ) by calculating local Moran’s I index between a given county and the neighbouring values in the surrounding counties [27] . The significance level of clusters was determined using a Z score generated by comparison of the Local Moran’s I statistic for the average incidence in each county . A high positive Z score indicated that the surroundings had spatial clusters ( High-High: high-value spatial clusters or Low-Low: low-value spatial clusters ) and a low negative Z score indicated the presence of spatial outliers ( High-Low: high values surrounded with low values or Low-High: Low values surrounded with high values ) [27] . Kulldorff’s space-time scan statistic ( SaTScan software , version 9 . 1 . 1 ) was used to explore the location of high-risk space-time clusters . The space-time scan statistic was defined by a cylindrical window with a circular ( or elliptic ) geographic base and with height corresponding to time [28] . The base was defined exactly as for the purely spatial scan statistic , while the height reflected the time period of the potential clusters [28] . In this study , circular scan windows were selected and fit discrete Poisson models . The maximum spatial cluster size was set to 5% of the population at risk in the spatial window and a maximum temporal cluster size of 10% of the study period in the temporal window . Likelihood ratio tests were evaluated to determine the significance of identified clusters and P-values were obtained through Monte Carlo simulation after 999 replications . The null hypothesis of a spatiotemporally random distribution was rejected when the P-value was < 0 . 05 . We conducted panel Poisson regression analysis to examine the association between yearly scrub typhus incidence and potential environment risk factors . An autocorrelation term was included to account for spatial and temporal dependency in scrub typhus incidence . The autocorrelation term was calculated using the minimum distance from each county-center to the nearest cluster . After aggregating the yearly incidence into a panel dataset , the association between incidence and environmental factors was examined , with IRR and their corresponding 95% confidence intervals and p values estimated using maximum likelihood methods . The temporal analysis and the panel Poisson regression analysis were conducted in STATA software ( Stata Crop Lp , College Station , TX , USA ) .
A total of 54 , 558 scrub typhus cases were reported from 1 , 031 counties during the period between 2006 and 2014 . The monthly variations in the number of scrub typhus cases presented in Fig 1A suggested a seasonal relationship , whereas the rapid increase in the total number of annual cases in Fig 1B was explained by an exponential growth function ( R2 = 0 . 98 ) . Scrub typhus cases occurred throughout the year , however began to increase dramatically in April through September , before reaching a peak in October and returning to low , constant levels of transmission in the winter months of December through March . The average numbers of reported cases are presented by month in Fig 2A , along with the incidence rate ratios comparing each month to January of each year . It was estimated that the month of October , which consistently had the largest number of reported cases , had an incidence of scrub typhus that was approximately 25 times higher than the winter months of January , February , or March ( P <0 . 001 ) ; IRR = 25 . 2; 95% CI: ( 13 . 1 , 27 . 4 ) . Given the seasonal patterns observed in Fig 1A , the autocorrelation of the monthly scrub typhus cases was compared using a time-series analysis . The pattern of autocorrelation in Fig 2B not only demonstrated that the monthly reported incidence in one month was significantly correlated with the previous months’ incidence , but also exhibited maximum correlations in every 12 months , minimum correlations in every 6 months , and followed a sinusoidal pattern of oscillation with an increasing correlation between 2006 and 2014 . Though aggregated for the temporal analyses , the annual incidence rate of scrub typhus was highly variable at the county level , which ranged from zero reported cases to 66 . 21 cases per 100 , 000 residents . Spatial analysis of scrub typhus incidence at the county level demonstrated the spatial autocorrelation was positive , indicating clustering of reported cases during the study period . The values of Moran’s I ranged from 0 . 02 to 0 . 08 with all P values < 0 . 05 ( Table 1 ) , indicating the presence of clusters of scrub typhus incidence in each year . The hotspots ( High-High ) and outliers of scrub typhus transmission in mainland China were identified through LISA analysis . Hotspots were primarily distributed in the southern and southwestern provinces of China , however variation of the location was observed . In 2006 , the hotspots were distributed sporadically in Yunnan , Guangdong and Fujian . Later hotspots in those provinces expanded to include larger geographic areas over the next eight years ( Fig 3 ) . Hotspots also occurred and expanded in Guangxi and Hainan province between 2008 and 2014 , with a short appearance in the northern provinces of Anhui and Jiangsu in 2011 . High-Low outliers were sporadically distributed in the middle-eastern provinces of China including Shandong , Jiangsu and Anhui , while Low-High outliers were mainly concentrated in Yunnan province ( Fig 3 ) . During the study period , the proportion of counties and populations within High-High clusters increased from 1 . 51% to 6 . 19% and 1 . 44% to 5 . 95% respectively . Additionally , hotspot counties were responsible for between 48 . 96% of all reported cases in 2006 to 67 . 57% in 2013 ( Table 2 ) . Fig 4 shows the distribution of annual average scrub typhus incidence and the location of spatial clusters identified by using Kulldorff’s space-time scan statistic for each year from 2006 to 2014 . As can be seen , both the number of counties with increased scrub typhus incidence expanded persistently between 2006 and 2014 , which resulted in the formation of a large , continuous geographic area of scrub typhus incidence in southern mainland China . The primary cluster of scrub typhus cases was originally located in Shandong and Jiangsu province , after which the area expanded between 2006 and 2008 . Since that time , the primary cluster of increased scrub typhus incidence shifted to southwest , except for 2011 , where the primary cluster was identified in the northwestern region that included Anhui province . Secondary clusters of scrub typhus cases were also identified in southern and southeastern China as well as in Shaanxi and Beijing , with five to eight clusters identified every year . Additionally , spatiotemporal clusters across the entire study period between 2006 and 2014 were identified by using Kulldorff’s spatiotemporal scan statistic ( Fig 5 ) . The primary cluster was located in southwest China , including 103 counties in Yunnan , 11 counties in Sichuan , and even a county in Tibet , with a radius of 491 . 64 km . The time frame of the primary cluster was from July to October in 2014 , which coincided with the largest annual scrub typhus outbreak identified by the time-series analyses and carried a RR of 64 . 88 and log likelihood ratio ( LLR ) of 10 , 460 ( Table 3 ) . Most importantly , the primary cluster accounted for only 2 . 63% of the total population , but included 29 . 28% of the total cases during that time ( Table 4 ) . In addition , there were eleven significant secondary clusters identified , also primarily located in southern and middle-eastern China , with the RR and LLR ranging from 2 . 94 to 800 . 71 and 32 to 7433 , respectively ( Table 3 ) . The results of panel Poisson regression analysis revealed that scrub typhus incidence was positively correlated with the percentage of forest ( IRR = 1 . 17; 95% CI: 1 . 15 , 1 . 10 ) and shrub ( IRR = 7 . 52; 95% CI: 7 . 18 , 7 . 87 ) as well as temperature ( IRR = 1 . 06; 95% CI: 1 . 05 , 1 . 07 ) and precipitation ( IRR = 1 . 01; 95% CI: 1 . 00 , 1 . 01 ) . Our results also indicate that lower incidence of scrub typhus is associated with the percentage of cropland ( IRR = 0 . 61; 95% CI: 0 . 60 , 0 . 62 ) , grassland ( IRR = 0 . 23; 95% CI: 0 . 19 , 0 . 28 ) , built-up land ( IRR = 0 . 64; 95% CI: 0 . 61 , 0 . 67 ) , water bodies ( IRR = 0 . 25; 95% CI: 0 . 23 , 0 . 27 ) , and relative humidity ( IRR = 0 . 90; 95% CI: 0 . 90 , 0 . 91 ) . Except for percentage of forest , which was positively correlated in the univariate analysis but negatively correlated in the multivariate analysis ( Table 5 ) , the IRR were similar , with the largest differences observed with the percentage of shrub ( IRR: 7 . 52 vs 1 . 29 ) and the IRR for temperature ( IRR: 1 . 06 vs 1 . 35 ) in the multivariate analysis .
The results of our study indicate that the spatiotemporal transmission of scrub typhus has increased exponentially between 2006 and 2014 and spread throughout much of mainland China . LISA and spatial scan statistics analyses identified significant clusters with respect to both space and time that indicated outbreaks of scrub typhus were primarily located in southwestern and southern China . Given the ability of spatiotemporal analyses based on geographic information systems to assist in the identification of counties with the highest risk of contracting scrub typhus , we suggest that these methods could have further application in both future disease surveillance and planning of mitigation strategies . In a previous study , we identified the most significant cluster of scrub typhus in the southeastern provinces of Guangdong , Fujian , Jiangxi , and Guangxi [25] . In the current study , the counties at the highest risk were located in the southwestern provinces of Yunnan and Sichuan and accounted for nearly a quarter of total cases during the 2014 outbreak . More importantly , by analyzing annual spatiotemporal clustering , the transmission dynamics appeared to shift between middle-east , southeast , and southwest China . Therefore , we suggest that each of these three high-risk regions be considered for the implementation of targeted interventions such as environmental management , controlling and killing rodent and mites , strengthening personal protection . Given that there was a low incidence of scrub typhus in 2007 in north eastern Xinjiang Uygur Autonomous Region contiguous to Gansu province , we suggest that more investigations be performed to determine if novel cases are as yet unreported in those outlying provinces . Notably , high-high spots detected by LISA analysis were primarily concentrated in southern China and rarely identified in the middle-eastern regions . The high-low outliers that were identified in this region , suggest that scrub typhus incidence in this region was concentrated in a few counties , which could indicate that natural foci of scrub typhus are still forming in this region as they expand into northern China . In addition , our study identified clusters in provinces such as Beijing , Shaanxi , and Anhui , which further confirmed disease outbreaks in these provinces reported by other studies [14 , 29 , 30] . Thus , our findings will further assist health authorities and public health practitioners through the identification of established foci in southern China as well as the documentation of the emergence of new foci in the north . The increasing number of reported cases and geographic expansion of scrub typhus in China is partly due to increasing quality of the surveillance system and availability of detection facilities as the increasing investment of health resources . Moreover , environment change and human activities could be important factors contributed to this increasing trend [31 , 32] . In this study , our findings demonstrated the percentage of shrub , temperature and precipitation were risk factors associated to the spatiotemporal heterogeneity of scrub typhus notifications in China . A possible explanation is that temperature , precipitation , and shrub may affect the population dynamics and activity levels of chigger mites [7 , 33] . Previous studies also suggested the migration of infested rodents or chiggers may have led to the formation new natural foci in provinces of Shandong , Henan , and Beijing since the meteorological and vegetation cover conditions are similar in these areas [34] . Additionally , socio-economic factors could have also served as important drivers for the transmission of scrub typhus in recent years . For instance , the urbanization and change of land use may contribute to the spread of scrub typhus into urban areas by providing suitable habitats such as clearings , grasslands , and riverbanks for vectors and small rodents [35] . Presently , we only explored the association between environmental variables ( land cover , weather ) and the incidence of scrub typhus . In future , a more well-coordinated and interdisciplinary approach is imperative and urgently needed to explore the relative effects of environmental and socio-economic factors on scrub typhus transmission in mainland China . While this study brings the important new knowledge on the epidemiology of scrub typhus in China , there are also some limitations . Since the case data were obtained from a passive surveillance system , the reporting system might miss some cases due to lack of diagnostic facilities and/or misdiagnosed due to the co-occurrence of other febrile diseases such as leptospirosis , typhoid fever , or hemorrhagic fever in the absence of the characteristic eschar [36 , 37] . Additionally , in our analysis we chose to use a circular scan window in space-time scan statistics . While the circular scan has been documented to perform better at detecting larger clusters compared to the elliptic window scan , it may also include insignificant zones and has been shown to have reduced performance when used with irregular shapes [38 , 39] . In conclusion , our results show that the incidences of scrub typhus vary in different spatial settings , and the geographic distribution of scrub typhus appeared to have expanded over recent years , indicating the disease is emerging or re-emerging and remains an important public health problem in China . Meanwhile , the study also prove environmental factors such as temperature , precipitation and vegetation type are important drivers in the dynamics of scrub typhus . To the best of our knowledge , this is the most detailed study on spatiotemporal epidemiology of scrub typhus across the entire country , which provides a sound evidence base for future prevention and control programs and also lays a foundation for further investigation into the social and environmental factors responsible for changing disease patterns . Given the exponential growth and spatiotemporal features observed in this study , it is likely that the incidence of scrub typhus will increase in the future , and the disease may be spreading even to non-traditional foci where cases had rarely been reported . Moreover , based on the results of this study , it is recommended that immediate measures be taken in high-risk areas to increase health education and awareness of scrub typhus , enhance the availability of diagnostic and treatment practices , as well as continue surveillance of this emerging infectious disease .
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Scrub typhus is a vector-borne disease carried by the chigger mite and is endemic in the Asia-Pacific region . Now scrub typhus causes a considerable burden on public health and the economy in China . We explored the spatiotemporal dynamics of scrub typhus cases in China between January 2006 and December 2014 , and explored the potential risk factors affecting the spatial distribution of the disease . The majority of cases were reported between July and November , with peak incidence during October every year . Several high-risk clusters were identified in southwest , southern , and middle-east China . Scrub typhus incidence was positively correlated with the percentage of shrub , and temporal variation in temperature and precipitation in China .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
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2016
|
Spatiotemporal Dynamics of Scrub Typhus Transmission in Mainland China, 2006-2014
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Some symbiotic bacteria cause remarkable reproductive phenotypes like cytoplasmic incompatibility and male-killing in their host insects . Molecular and cellular mechanisms underlying these symbiont-induced reproductive pathologies are of great interest but poorly understood . In this study , Drosophila melanogaster and its native Spiroplasma symbiont strain MSRO were investigated as to how the host's molecular , cellular and morphogenetic pathways are involved in the symbiont-induced male-killing during embryogenesis . TUNEL ( terminal deoxynucleotidyl transferase dUTP nick end labeling ) staining , anti-cleaved-Caspase-3 antibody staining , and apoptosis-deficient mutant analysis unequivocally demonstrated that the host's apoptotic pathway is involved in Spiroplasma-induced male-specific embryonic cell death . Double-staining with TUNEL and an antibody recognizing epidermal marker showed that embryonic epithelium is the main target of Spiroplasma-induced male-specific apoptosis . Immunostaining with antibodies against markers of differentiated and precursor neural cells visualized severe neural defects specifically in Spiroplasma-infected male embryos as reported in previous studies . However , few TUNEL signals were detected in the degenerate nervous tissues of male embryos , and the Spiroplasma-induced neural defects in male embryos were not suppressed in an apoptosis-deficient host mutant . These results suggest the possibility that the apoptosis-dependent epidermal cell death and the apoptosis-independent neural malformation may represent different mechanisms underlying the Spiroplasma-induced male-killing . Despite the male-specific progressive embryonic abnormality , Spiroplasma titers remained almost constant throughout the observed stages of embryonic development and across male and female embryos . Strikingly , a few Spiroplasma-infected embryos exhibited gynandromorphism , wherein apoptotic cell death was restricted to male cells . These observations suggest that neither quantity nor proliferation of Spiroplasma cells but some Spiroplasma-derived factor ( s ) may be responsible for the expression of the male-killing phenotype .
Symbiotic microorganisms are ubiquitously associated with diverse insects , and affect their host biology in a variety of ways [1] , [2] . Some symbionts play important biological roles such as provisioning of essential nutrients to their hosts [3] , helping food digestion for their hosts [4] , or improving the fitness of their hosts under specific ecological conditions [5] . Other symbionts like Wolbachia , Cardinium and Spiroplasma are generally parasitic rather than beneficial to their hosts , often causing negative fitness effects and also inducing reproductive phenotypes like cytoplasmic incompatibility , male-killing , parthenogenesis or feminization , by which these symbionts are able to spread their own infections into the host populations in a selfish manner [6]–[8] . Members of the genus Spiroplasma , belonging to the class Mollicutes , are wall-less bacteria associated with diverse arthropods and plants [9] . Some Spiroplasma species and strains are known to cause male-killing phenotypes in fruit flies , ladybird beetles and butterflies , wherein infected females produce all-female or female-biased offspring due to male-specific mortality during embryogenesis and/or larval development [10] , [11] . Male-killing symbiotic bacteria belonging to Spiroplasma poulsonii [12] have been identified from fruit flies of the genus Drosophila , which are represented by the strains WSRO from D . willistoni , NSRO from D . nebulosa , MSRO from D . melanogaster and others [13] , [14] . While the Drosophila-Wolbachia symbiosis represents one of the best-studied model symbiotic systems [8] , [15] , the Drosophila-Spiroplasma symbiosis has also been well-studied as another model system of infection dynamics [16]–[18] , immune regulation [19]–[21] , vertical transmission [22] , [23] and male-killing expression [24]–[27] . However , molecular and cellular mechanisms underlying the Spiroplasma-induced male-specific embryonic pathology are still not well understood . Histological observations , mosaic analysis and in vitro culturing have suggested that nervous system is among the major target sites of Spiroplasma-induced male-killing in Drosophila embryos [28]–[32] . In D . melanogaster , Spiroplasma-infected mutants deficient in dosage compensation complex genes fail to show male-killing phenotype , indicating that a functional dosage compensation complex is required for expression of the Spiroplasma-induced make-killing [24] . In D . nebulosa infected with its native Spiroplasma strain NSRO , dying male embryos exhibit widespread TUNEL ( terminal deoxynucleotidyl transferase dUTP nick end labeling ) signals , suggesting possible involvement of host's pathway of programmed cell death or apoptosis [25] . In this study , we performed detailed investigation of the male-killing process during embryogenesis of D . melanogaster infected with its native Spiroplasma strain MSRO . In particular , we focused on host's molecular , cellular and morphogenetic pathways that may potentially be involved in the male-killing phenotype by utilizing the wealth of genetic resources available in D . melanogaster . Our observations unveiled several previously unknown aspects of Spiroplasma-induced male-killing , which include unequivocal demonstration of male-specific up-regulation of apoptotic pathway , identification of embryonic epithelium as the main target of male-specific apoptosis , male-specific malformation of embryonic nervous system independent of apoptosis , and specific killing of male cells in gynandromorphic embryos .
In Spiroplasma-infected female embryos ( sexed according to Sex-lethal [Sxl] expression , see Materials and Methods and Fig . S1A and B ) , TUNEL-positive cells were scarcely found by stage 9 ( Fig . 1A ) , and first appeared at stage 10 in the cephalic region ( Fig . 1B , arrowhead ) . Subsequently , TUNEL-labeled cells spread to the other regions ( Fig . 1C and D ) and reached a peak level at stages 12–13 ( Fig . 1K ) . These patterns are typical of normal programmed cell death in Drosophila development [33] . Actually , the Spiroplasma-infected female embryos exhibited no substantial differences in the spatiotemporal appearance of TUNEL-positive cells in comparison with uninfected male and female embryos ( Fig . 1C; Fig . S1C–E ) . The Spiroplasma-infected female embryos developed normally ( Fig . 1E ) , and finally emerged as first instar larvae . By contrast , in Spiroplasma-infected male embryos , ectopic TUNEL signals were observed at stage 10: in addition to the dense signals at the cephalic region ( Fig . 1G , arrowhead ) , TUNEL-positive cells were detected throughout the embryonic body ( Fig . 1G ) . Subsequently , the excessive TUNEL signals became more prominent and progressively increased during germ band retraction ( Fig . 1H , I and K ) . From stage 13 and on , the Spiroplasma-infected male embryos started to disintegrate with massive cell death , wherein segmentation and other morphological traits of the embryos became difficult to recognize ( Fig . 1J ) , and finally died . These results indicate that Spiroplasma-infected Drosophila males exhibit ectopic programmed cell death from the early stage of embryonic development . A previous study reported that , in D . nebulosa and its natural Spiroplasma strain NSRO , Spiroplasma-infected male embryos exhibit developmental arrest between stages 12 and 13 with segmentation failure , disintegrated embryonic morphology , and widespread apoptosis as testified by TUNEL staining [25] . Our observations with D . melanogaster and its natural Spiroplasma strain MSRO are highly concordant with the previous observations , suggesting that the same molecular and cellular processes are operating under the symbiont-induced male-specific cell death in the different host species . Previous studies have demonstrated that normal programmed cell death in Drosophila development requires the activity of Caspase-9-like initiator caspase Dronc ( Drosophila Nedd2-like caspase ) [34]–[38] , and an antibody against cleaved-Caspase-3 can detect the Dronc activity [39] . When probed with the anti-cleaved-Caspase-3 antibody , Spiroplasma-infected male embryos exhibited more immunopositive signals than Spiroplasma-infected female embryos as well as uninfected male and female embryos , and the spatiotemporal patterns of the signals looked similar to those of the TUNEL signals ( Fig . 1L ) . These results suggest that Spiroplasma-infected Drosophila males exhibit ectopic programmed cell death during embryonic development , at least in part by activating the caspase-dependent apoptotic pathway . Spiroplasma-infected male embryos and female embryos were individually subjected to quantitative PCR targeting Spiroplasma dnaA gene copies . Throughout the embryonic stages examined ( from 10 to 13 ) , Spiroplasma titers per embryo remained almost constant , exhibiting no significant differences between male embryos and female embryos ( Fig . 1M ) . By contrast , Spiroplasma titers per host elongation factor 1α 100E ( EF1a ) gene copy exhibited higher values in male embryos than in female embryos ( Fig . S1F ) , which was attributable to lower EF1α gene titers in male embryos presumably because of male-killing phenotype ( Fig . S1G ) . These results strongly suggest that the ectopic programmed cell death specific to male embryos entails no Spiroplasma proliferation during embryogenesis . Meanwhile , it should be noted that , since quantitative PCR detects not live bacterial cells but DNA molecules , the possibility cannot be ruled out that titers of live Spiroplasma cells may change during embryogenesis and/or between male embryos and female embryos . Previous studies have demonstrated that programmed cell death in normal Drosophila development requires proapoptotic genes reaper ( rpr ) , head involution defective ( hid ) and grim , which are collectively termed RHG genes [40]–[42] . In Drosophila mutant H99 deficient in all these genes , apoptotic cell death is almost completely blocked during embryogenesis [40] . RHG proteins bind to Drosophila inhibitor of apoptosis protein 1 ( DIAP1 ) and disrupt its ability to inhibit caspase activity , by which apoptosis is triggered [43]–[46] . When Spiroplasma-infected H99 mutant embryos were subjected to the TUNEL assay , TUNEL-positive cells were observed neither in female embryos nor in male embryos at stages 11 and 12 ( Fig . 2A and C–F ) . These results strongly suggest that the majority of the ectopic programmed cell death observed in Spiroplasma-infected male embryos is induced via host's apoptotic pathway . In later stages of embryogenesis ( stages 13 and 14 ) , some TUNEL-positive cells were detected specifically in Spiroplasma-infected H99 male embryos , although the level of the signals was significantly lower than that in Spiroplasma-infected control male embryos ( Fig . 2B and G–J ) . The residual TUNEL-positive cells may implicate the presence of a minor pathway of Spiroplasma-induced male-specific cell death independent of RHG proteins . Alternatively , the residual TUNEL-positive cells may be due to attenuated clearance of dead cells in the infected embryos . One of the major mechanisms of apoptosis regulation in Drosophila development is the expression control of RHG genes [44] , [47] . Notably , it was reported that several Hox proteins , such as Deformed ( Dfd ) and Abdominal B ( Abd-B ) , may directly activate the expression of rpr by binding to its enhancer elements [48] . It was also reported that some segment polarity genes may regulate cell survival and death to establish morphological patterns during embryogenesis [49] . However , when we performed immunohistochemical visualization of Hox proteins Antennapedia ( Antp ) , Ultrabithorax ( Ubx ) and Abd-B , and several segment polarity proteins Wingless ( Wg ) and Engrailed ( En ) in Spiroplasma-infected male embryos and female embryos , no sex-related differences were observed in their localization patterns ( Fig . S2 ) . These results refute the possibility that Spiroplasma infection may induce ectopic cell death by affecting such developmental signals as Hox genes and segment polarity genes in male embryos . In Drosophila , atypical protein kinase C ( aPKC ) localizes to the subapical region ( SAR ) in the epithelial junctions , thereby establishing apical-basal cell polarity ( Fig . 3A ) [50] , [51] . We visualized embryonic epithelial cells by immunostaining with anti-aPKC antibody . In Spiroplasma-infected female embryos , aPKC showed normal junctional localization and subapical localization in epithelial cells ( Fig . 3B–D ) . In Spiroplasma-infected male embryos , by contrast , junctional localization of aPKC was significantly impaired ( Fig . 3E and F ) , and concentrated TUNEL signals were observed at the level of the subapical region of epithelial cells ( Fig . 3G ) . Notably , intersegmental furrows , which were normally formed in Spiroplasma-infected female embryos ( Fig . 3B–D , brackets ) , became obscure in Spiroplasma-infected male embryos ( Fig . 3E–G ) . These results indicate that embryonic epithelium is among the target tissues wherein Spiroplasma-associated male-specific programmed cell death is induced . Spiroplasma-infected male embryos show ambiguous segments after germ band retraction and die ( Fig . 1I ) [25] . The loss of segmentation may be relevant to the epithelial damage due to the male-specific apoptosis . Previous histological observations , mosaic analysis and in vitro culturing have suggested that nervous system is among the major target sites of Spiroplasma-induced male-killing in Drosophila embryos [28]–[32] . In Drosophila embryos , Elav ( embryonic lethal abnormal vision ) protein is specifically expressed in differentiated neural cells [52] , [53] . We performed immunostaining of developing embryos with anti-Elav antibody , which clearly visualized neurons in central nervous system ( CNS ) and also neurons in peripheral nervous system ( PNS ) ( Fig . 4A ) . In Spiroplasma-infected male embryos , both CNS and PNS were disorganized ( Fig . 4E and F ) , whereas these neural structures were intact in Spiroplasma-infected female embryos ( Fig . 4C and D ) . It is noteworthy that , despite the remarkable structural disorder , the regions of concentrated TUNEL-positive cells in Spiroplasma-infected male embryos did not agree with the locations of CNS and PNS ( Fig . 4E and F ) . These results suggest that Spiroplasma infection certainly disrupts the formation of normal nervous system specifically in male embryos , but the neural defects are not due to apoptosis of already differentiated neural cells . In Drosophila embryos , precursor neural cells , or neuroblasts , delaminate basally from neuroectoderm and undergo asymmetric cell division to generate neuroblast itself and ganglion mother cells ( GMC ) . GMCs divide once to give rise to two neurons ( Fig . 4B ) . Neuroblasts express several transcription factors sequentially to generate diverse populations of neurons , one of which is the transcription factor Krüppel ( Kr ) [54] . Spiroplasma-infected embryos at stage 10 were immunostained with anti-Kr antibody to observe neuroblasts at the beginning of male-specific ectopic cell death . Numerous Kr signals were identified in the neuroblast layer of both Spiroplasma-infected female embryos and male embryos , but they did not co-localize with the TUNEL signals ( Fig . 4G and H ) . These results suggest that Spiroplasma infection disrupts the formation of normal nervous system specifically in male embryos , but the neural defects are unlikely due to direct killing of precursor neural cells via apoptosis . In the light of these results , it is of focal interest how the epithelial apoptosis and the neural malformation in the Spiroplasma-infected male embryos are interconnected to each other . Are there any causal relationships between them , or do they represent independent processes leading to the embryonic male lethality ? To address this question , we observed the development of nervous system in Spiroplasma-infected H99 mutant embryos , in which apoptotic cell death is almost completely blocked [40] , by immunostaining with anti-Elav antibody . In Spiroplasma-infected H99 female embryos , CNS and PNS developed normally ( Fig . 5A and C ) , while in Spiroplasma-infected H99 male embryos , remarkable neural malformation was observed ( Fig . 5B and D ) . These results strongly suggest that host's apoptotic pathway is not required for expression of the neural malformation in Spiroplasma-infected male embryos . Plausibly , the male-specific neural malformation may occur independently of the male-specific epithelial apoptosis in the Spiroplasma-infected embryos . If so , the Spiroplasma-induced male-killing entails at least two independent mechanisms: one targets male epithelial cells via host's apoptotic pathway and another targets male nervous system via unknown pathway ( s ) . Alternatively , the defects in neural tissues may somehow influence the organization of adjacent epithelial cells , thereby causing the male-specific epithelial apoptosis secondarily , or vise versa . In the survey of Spiroplasma-infected embryos , we occasionally identified gynandromorphic embryos with mosaic expression of Sxl ( Fig . 6 ) . Sxl-mosaic embryos were observed at stage 12 and later ( 3/162; 1 . 9% ) , but not found at earlier stages ( stage 9 to 11; 0/142 ) . In D . melanogaster , spontaneous gynandromorphism has been reported to occur at frequencies between 0 . 02 to 0 . 1% in XX zygotes [55] . While symbiont-induced gynandromorphism has been reported from Wolbachia-infected moth , butterfly , planthopper , wasp and wood louse [56]–[60] , it requires further verification whether or not the infrequent occurrence of gynandromorphism in D . melanogaster is induced by Spiroplasma infection . Interestingly , apoptotic cells labeled with anti-cleaved-Caspase-3 antibody were restricted to Sxl-negative , presumable male areas in the gynandromorphic embryos ( Fig . 6B and C ) . These results provide strong evidence that Spiroplasma infection selectively acts on male cells but not on female cells , thereby causing male-killing phenotype . In conclusion , our study unveiled previously unknown molecular and cellular aspects underlying the Spiroplasma-induced male-killing in D . melanogaster . We demonstrated that in Spiroplasma-infected Drosophila embryos ( i ) host's apoptotic pathway is up-regulated in a male-specific manner , ( ii ) the male-specific apoptosis mainly targets embryonic epithelial cells , ( iii ) as previously reported , remarkable neural malformation is observed in male embryos , ( iv ) however , neither differentiated neural cells nor precursor neural cells exhibit apoptosis in male embryos , ( v ) the male-specific neural malformation occurs even when host's apoptotic pathway is disrupted , and ( vi ) therefore , the apoptosis-dependent epidermal cell death and the apoptosis-independent neural malformation may represent different mechanisms underlying the Spiroplasma-induced lethality in male embryos . We also found that ( vii ) Spiroplasma titers remain almost constant throughout the embryonic development and across male and female embryos , ( viii ) although at a low frequency ( ∼2% ) , gynandromorphic embryos are found in the Spiroplasma-infected embryos , ( ix ) in these embryos , apoptotic cell death is preferentially observed in male cells , and ( x ) therefore , neither quantity nor proliferation of Spiroplasma but some Spiroplasma-derived factor ( s ) selectively acting on host's male cells may be responsible for the expression of male-killing phenotype . These findings highlight complex molecular and cellular interactions in the Spiroplasma-Drosophila symbiosis , and provide invaluable clues to our deeper understanding of the symbiont-induced manipulation of host's development and reproduction .
The following laboratory strains of D . melanogaster were raised at 25°C on a standard cornmeal diet in plastic tubes unless otherwise indicated . Oregon-R ( wild-type strain ) was provided by Takehide Murata ( the Institute of Physical and Chemical Research , RIKEN ) . Sxl-Pe-EGFP G78b [61] and Df ( 3L ) H99 , kniri-1 , pp/TM3 , Sb1 [40] were obtained from the Bloomington Stock Center , USA , and the Drosophila Genetic Resource Center ( DGRC ) at Kyoto Institute of Technology , Japan , respectively . After tetracycline treatment for curing bacterial infections as described [62] , the fly strains were infected with the Spiroplasma strain MSRO by hemolymph injection as described [16] . The MSRO-containing hemolymph was collected from naturally infected D . melanogaster strain Ug-SR derived from Uganda [63] , which was gifted by John Jaenike ( University of Rochester , USA ) . Since the MSRO-infected fly strains produce all-female offspring , these strains were maintained by supplying males from corresponding uninfected fly stocks . H99 mutant strain was re-balanced with GFP-tagged balancer ( TM3 , ActGFP , Ser1 ) and homozygous mutant individuals were identified by immunostaining with anti-GFP antibody . Spiroplasma-infected female flies within three days after eclosion were allowed to mate with male flies for three days in plastic tubes . These insects were kept with grape juice agar plates for embryo collection . Embryos at different developmental stages were dechorionated , fixed in 4% formaldehyde and heptane for 20 min , and devitellinized by vigorously shaking in heptane and methanol . In this study , female-specific expression of Sxl , the master regulator of the sex determination system in Drosophila [64] , [65] , was used for sexing of embryos ( Fig . S1A and B ) . The following primary antibodies were used for immunohistochemical staining: mouse anti-Sex-lethal ( M18; 1∶20 dilution; Developmental Studies Hybridoma Bank [DSHB] ) , rabbit anti-cleaved-Caspase-3 ( 1∶100; Cell signaling Technology , Inc . ) , rat anti-Dα-Catenin ( DCAT-1; 1∶10; DSHB ) , rabbit anti-PKCζ ( C-20; 1∶100; Santa Cruz Biotechnology , Inc . ) , mouse anti-Elav ( 9F8A9; 1∶20; DSHB ) , rat anti-Elav ( 7E8A10; 1∶20; DSHB ) , chicken anti-GFP ( GFP-1020; 1∶400; Aves Labs , Inc . ) , mouse anti-Wingless ( 4D4; 1∶20; DSHB ) , mouse anti-Engrailed/Invected ( 4D9; 1∶20; DSHB ) , mouse anti-Antp ( 8C11; 1∶20; DSHB ) , mouse anti-Ubx ( FP3 . 38; 1∶20; DSHB ) , mouse anti-Abd-B ( 1A2E9; 1∶20; DSHB ) , and guinea pig anti-Krüppel ( #573; 1∶300; Asian Distribution Center for Segmentation Antibodies ) [66] . Fluorochrome-labeled secondary antibodies were purchased from Jackson ImmunoResearch Laboratories , Inc . and Molecular Probes . Nuclear DNA was stained with SYTOX Orange Nucleic Acid Stain ( S-11368; 1∶20 , 000; Molecular Probes ) . TUNEL staining was performed to detect DNA fragmentation associated with programmed cell death or apoptosis [67] , [68] by In Situ Cell Death Detection Kit , TMR red ( Roche Applied Science ) as described [69] . Images were taken on a confocal microscope ( Zeiss LSM 5 Pascal and LSM 510 META ) . Serial Z-sections of confocal images were compiled to create projection images ( maximum intensity projection ) unless otherwise described , using a custom macro in ImageJ software ( National Institutes of Health , USA ) . After dechorionization , developmental stages and sexes of Sxl-Pe-EGFP embryos were determined visually under a stereoscopic fluorescent microscope ( Leica M165 FC ) . Each of 12 embryos of both sexes , which were collected at stage 10 , 11 , 12 or 13 , was individually subjected to DNA extraction using QIAamp DNA mini kit ( Qiagen ) . The DNA samples were subjected to real-time quantitative PCR using SYBR Green ( Takara ) and Mx3000P qPCR system ( Stratagene ) essentially as described [16] . Spiroplasma titers in terms of dnaA gene copies were quantified using the primers 5′-TGA AAA AAA CAA ACA AAT TGT TAT TAC TTC-3′ and 5′-TTA AGA GCA GTT TCA AAA TCG GG-3′ . The copy numbers of the host EF1α gene were also quantified using the primers 5′-TTA ACA TTG TGG TCA TTG GCC A-3′ and 5′-CTT CTC AAT CGT ACG CTT GTC G-3′ . The reaction mixture consisted of 1× AmpliTaq Gold buffer , 1 . 5 mM MgCl2 , 0 . 2 mM each of dATP , dGTP , dCTP and dUTP , 0 . 3 µM each of the forward and reverse primers , 1/100 , 000 SYBR green , and 0 . 02 U/µl AmpliTaq Gold DNA polymerase ( Applied Biosystems ) . PCR was performed under a temperature profile of 95°C for 10 min followed by 38 cycles of 95°C for 1 min , 60°C for 1 min and 72°C for 1 min . The data were statistically analyzed using the software R version 2 . 15 . 0 ( R Foundation for Statistical Computing ) . Multiple comparison was performed using non-parametric Kruskal-Wallis test followed by Scheffe test . Quantitative analyses of TUNEL signals were performed by custom R scripts with EBImage package for image processing [70] . Briefly , maximum projections of confocal slices stained with TUNEL and Sxl antibody were obtained . Images showing lateral view of the embryos were selected for further processing . Embryonic regions were determined by binarization of projected images of Sxl . Processed images were visually checked and signals derived from other objects ( e . g . flanking embryos , backgrounds etc . ) were manually blacked out to obtain the area of each embryo precisely ( mask image ) . TUNEL signals were also binarized and signals inside the mask image were calculated by image integration . For normalization , TUNEL signals in each embryo were divided by the embryonic area calculated by mask image . Statistical test was performed using Wilcoxon rank sum test .
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Symbiotic bacteria are ubiquitously associated with diverse insects , and affect their host biology in a variety of ways . In Drosophila fruit flies , infection with Spiroplasma symbionts often causes male-specific embryonic mortality , resulting in the production of all-female offspring . This striking phenotype is called “male-killing” , whose underlying mechanisms are of great interest . Here we investigated Drosophila melanogaster and its native Spiroplasma symbiont strain to understand how the host's molecular , cellular and morphogenetic pathways are involved in the symbiont-induced male-killing . Specifically in Spiroplasma-infected male embryos , pathogenic phenotypes including massive cell death throughout the body and neural malformation were observed . We unequivocally identified that the male-specific cell death preferentially occurs in the embryonic epithelium via the host's apoptotic pathway . Meanwhile , we found that , unexpectedly , the male-specific neural defects occur independently of host's apoptosis , suggesting that at least two different mechanisms may be involved in the Spiroplasma-induced male-killing . Also unexpected was the finding that Spiroplasma titers are almost constant throughout embryogenesis irrespective of sex despite the male-specific severe apoptosis . We serendipitously found Spiroplasma-infected sexual mosaic embryos , wherein apoptosis was associated with male cells , which suggests that some Spiroplasma-derived factor ( s ) may selectively act on male cells and cause male-killing .
|
[
"Abstract",
"Introduction",
"Results",
"and",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"developmental",
"biology",
"biology",
"microbiology"
] |
2014
|
Male-Killing Spiroplasma Induces Sex-Specific Cell Death via Host Apoptotic Pathway
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Pulmonary infection is the most common risk factor for acute lung injury ( ALI ) . Innate immune responses induced by Microbe-Associated Molecular Pattern ( MAMP ) molecules are essential for lung defense but can lead to tissue injury . Little is known about how MAMP molecules are degraded in the lung or how MAMP degradation/inactivation helps prevent or ameliorate the harmful inflammation that produces ALI . Acyloxyacyl hydrolase ( AOAH ) is a host lipase that inactivates Gram-negative bacterial endotoxin ( lipopolysaccharide , or LPS ) . We report here that alveolar macrophages increase AOAH expression upon exposure to LPS and that Aoah+/+ mice recover more rapidly than do Aoah-/- mice from ALI induced by nasally instilled LPS or Klebsiella pneumoniae . Aoah-/- mouse lungs had more prolonged leukocyte infiltration , greater pro- and anti-inflammatory cytokine expression , and longer-lasting alveolar barrier damage . We also describe evidence that the persistently bioactive LPS in Aoah-/- alveoli can stimulate alveolar macrophages directly and epithelial cells indirectly to produce chemoattractants that recruit neutrophils to the lung and may prevent their clearance . Distinct from the prolonged tolerance observed in LPS-exposed Aoah-/- peritoneal macrophages , alveolar macrophages that lacked AOAH maintained or increased their responses to bioactive LPS and sustained inflammation . Inactivation of LPS by AOAH is a previously unappreciated mechanism for promoting resolution of pulmonary inflammation/injury induced by Gram-negative bacterial infection .
Acute lung injury ( ALI ) or its more severe form , acute respiratory distress syndrome , is a complex syndrome that may lead to acute respiratory failure and death . ALI has an average case-fatality rate of 38 . 5%; its incidence and mortality increase with age [1] . Although significant progress has been made toward understanding the pathophysiology of ALI , only supportive treatment , such as lung-protective ventilation , has reduced its mortality [2] . The most common risk factor for ALI is severe sepsis caused by bacterial or viral pneumonia [1] . During acute pulmonary infection , MAMPs or DAMPs ( Danger-Associated Molecular Pattern ) are detected by pattern recognition receptors ( PRRs ) on/in lung parenchymal and immune cells , eliciting innate immune responses that eliminate the invading microorganisms [3–6] . Uncontrolled inflammation and excessive accumulation/activation of leukocytes may lead to ALI and lung dysfunction . Neutrophil accumulation is a cardinal feature of both acute inflammation and ALI [7] . MAMPS stimulate alveolar macrophages and airway epithelial cells to produce chemokines [8] that recruit neutrophils to the lung . In mice , KC ( Keratinocyte chemoattractant , CXCL1 ) , MIP-2 ( macrophage inflammatory protein-2 , CXCL2/3 ) , the IL-23-IL17 axis and CXCL5 play this important role [9 , 10] . Neutrophils take up and kill bacteria , release proteases , produce reactive oxygen species and form neutrophil extracellular traps . Although these are critical actions for innate immune defense , excessive or prolonged accumulation of neutrophils may cause alveolar barrier damage and pulmonary edema , impairing gas exchange [2 , 9 , 11] . Sustained high neutrophil counts or IL-8 ( functional homologue of mouse KC ) levels in BALF have correlated with the severity of ALI while clearance of neutrophils from lung is a marker of ALI resolution and predicts a good outcome [9] . Since long-lasting inflammatory responses can lead to severe lung injury , timely resolution of the inflammation is critical for the host to minimize tissue damage and regain homeostasis [12–14] . Resolution of lung inflammation is an active and coordinated process that is believed to start with the degradation/inactivation of the inflammatory stimulus [15] . Perhaps surprisingly , how MAMPs are degraded/inactivated in the lung and how MAMP clearance affects the severity or duration of ALI have not been reported . Here we have studied the resolution of ALI induced by Klebsiella pneumoniae , an opportunistic Gram-negative pathogen that can cause severe pulmonary infection and ALI [16] . Much of the inflammatory response to these bacteria is induced by their lipopolysaccharides ( LPS or endotoxin ) , potent MAMPs that are detected mainly by the MD-2-TLR4 receptor complex [17] . LPS , often used as a surrogate for infectious exposure , induces experimental ALI when it is introduced into the airway [18] . We found previously that LPS can be degraded and inactivated by a host enzyme , acyloxyacyl hydrolase ( AOAH ) [19 , 20] . AOAH removes the secondary fatty acyl chains from the lipid A moiety of LPS; the partially deacylated LPS becomes inactive or even may act as a LPS antagonist [21] . Our previous studies have shown that AOAH limits LPS-induced innate antibody production [22 , 23] , prevents hepatosplenomegaly [24 , 25] , and shortens endotoxin tolerance [26–28]; overexpression of AOAH protects mice from E . coli infection [29] . We hypothesized that AOAH , by degrading LPS in the lung , would also promote the resolution of ALI induced by LPS or Gram-negative bacteria . We found that AOAH expression was highly inducible in alveolar macrophages after both in vitro and in vivo stimulation with LPS and other TLR agonists . AOAH promoted the resolution of ALI induced by LPS or Klebsiella , probably mainly by diminishing neutrophil chemoattractant production . Our results point to an important role for host inactivation of the bacterial cell wall lipopolysaccharide ( LPS ) in the timely resolution of pulmonary inflammation/injury induced by many Gram-negative bacteria .
AOAH is expressed by several phagocytic cell types [20] . To test whether alveolar macrophages ( AMs ) also make AOAH , we obtained AMs from bronchoalveolar lavage fluid ( BALF ) . Naïve AMs expressed much lower levels of AOAH mRNA than did naïve peritoneal macrophages ( PM ) , yet LPS stimulation in vitro increased AM AOAH mRNA by over 90-fold ( Fig 1A ) . TLR1/2 agonist Pam3CSK4 also enhanced AOAH mRNA expression , and the combination of Pam3CSK4 and TLR3 agonist Poly I:C was additive ( Fig 1A ) . In contrast , naïve PMs expressed relatively high levels of AOAH and LPS slightly augmented AOAH expression ( Fig 1A ) . When we introduced LPS intranasally ( i . n . ) , AOAH expression in AMs increased by about 60-fold ( Fig 1B ) . Although intranasally administered hydrochloric acid ( HCl ) can also induce lung inflammation , AOAH expression was not significantly increased ( Fig 1B ) , suggesting that AOAH expression in AMs may be specifically induced by LPS and certain other MAMPs . The enhanced AOAH expression in AMs was not due to LPS-induced monocyte recruitment to the alveolar spaces , as we found that LPS induced AOAH expression to a similar extent in wild type mice and in CCR2-deficient mice ( Fig 1B ) , which had diminished blood Ly6C+ monocytes [30] and recruited fewer CD11bhi monocytes to the lung post LPS i . n . [31] ( S1 Fig ) . To identify the cells that take up LPS after i . n . instillation , we used LPS conjugated with FITC . Unlike macrophages from other sites , AMs express high levels of CD11c but low levels of CD11b and they have high autofluorescence [32] . Eighteen hours after instillation , LPS-FITC was mainly associated with CD11c+CD11blo autofluorescencehi AMs , while CD11b+Ly6G+ neutrophils , CD11b+Ly6G- mono-macrophages and CD11b- Ly6G- lymphocytes bound low amounts of LPS-FITC ( Fig 1C and 1D ) . When we used trypan blue to quench extracellular FITC fluorescence , the FITC geometric florescence intensity was largely sustained , suggesting that the LPS-FITC had been internalized . Thus , after i . n . instillation , AMs took up more LPS than did the other cells in alveoli . Aoah+/+ and Aoah-/- AMs took up comparable amounts of LPS ( Fig 1D ) . Using the LPS-induced acute lung injury ( ALI ) model , we studied whether AOAH influences ALI mortality and morbidity . To induce significant morbidity and mortality , we used a high dose of LPS ( 200 μg ) . After LPS instillation i . n . , Aoah-/- mice had greater mortality than did Aoah+/+ mice ( Fig 2A ) . The Aoah-/- mice that survived had greater and more prolonged weight loss ( Fig 2B ) and more severe and persistent clinical signs of illness than did Aoah+/+ survivors ( Fig 2C ) , suggesting that AOAH-mediated LPS deactivation may play an important role in mitigating lung injury and promoting recovery . An important feature of lung injury is the increased lung vascular and epithelial permeability that leads to accumulation of protein-rich fluid in alveolar spaces [2] . We studied whether Aoah-/- mice had more alveolar barrier leakage than did Aoah+/+ mice after intranasal exposure to LPS . To induce significant yet non-lethal pulmonary injury , we instilled 150 μg LPS . One day after LPS i . n . , both Aoah+/+ and Aoah-/- mouse BALF had increased alveolar protein , suggesting the accumulation of extravascular protein in the alveolar space ( Fig 2D ) . On days 4 and 7 post LPS , i . n . , the protein concentrations in Aoah+/+ BALF had returned to normal levels , while Aoah-/- BALF protein remained high , suggesting prolonged alveolar damage or delayed tissue repair ( Fig 2D ) . We also measured alveolar barrier damage by injecting Evans blue i . v . and then measuring the extravascular dye in the lungs . ( Fig 2E ) . Aoah-/- mouse lungs had prolonged accumulation of Evans blue , suggesting prolonged alveolar leakage , in keeping with the results shown in Fig 2D . As neutrophil infiltration is known to be associated with alveolar damage [9] , we measured myeloperoxidase ( MPO ) activity , a marker for neutrophil abundance , and found that Aoah-/- mouse lungs had persistently high levels of MPO activity ( Fig 2F ) . Thus , after LPS instillation i . n . , Aoah-/- mice had prolonged lung injury and delayed neutrophil clearance . We compared the histological evidence of inflammation and injury in the lungs of Aoah+/+ and Aoah-/- mice that had been exposed to intranasal LPS . Aoah+/+ and Aoah-/- mice that had received PBS i . n . had thin alveolar walls with few neutrophils . One day after LPS i . n . , peri-bronchial and peri-vascular leukocyte infiltrates and alveolar edema were observed in the lungs of both Aoah+/+ and Aoah-/- mice ( Fig 2G ) . The lung tissues close to bronchi had more inflammation and edema than did distal tissues ( Fig 2G ) . On day 4 and 7 , most leukocytes and edema were cleared in Aoah+/+ lungs , while Aoah-/- lungs had persistent leukocyte infiltrates and severe edema ( Fig 2G ) . Thus , AOAH promoted clearance of LPS-induced leukocyte accumulation and alveolar repair in the lung . To further study the impact that AOAH has on the resolution of lung inflammation , we treated mice with a low dose of LPS ( 10 μg ) to induce significant inflammation without producing severe ALI . Almost all of the cells ( over 90% ) in BALF from PBS treated mice were alveolar macrophages ( Fig 3A , day 0 ) . One day after LPS i . n . , the total cell numbers dramatically increased in both strains of mice , mainly due to neutrophil recruitment to the alveolar spaces ( Fig 3A ) . Neutrophils in Aoah+/+ alveoli were largely cleared on day 4 ( Fig 3A and 3B ) and diminished on day 7 ( Fig 3A ) , while in Aoah-/- mice , alveolar neutrophils persisted on day 4 ( Fig 3A and 3C ) , indicating that inflammation resolved more slowly in Aoah-/- airspaces . We also observed increased numbers of lymphocytes in Aoah-/- BALF on day 4; the monocyte-macrophage numbers were not significantly different between the two strains of mice ( Fig 3A ) . We measured the immune cells in the lungs and found that , four and seven days after LPS exposure , Aoah-/- lungs had more neutrophils , AMs , DCs and lymphocytes than did Aoah+/+ lungs ( S2 Fig ) , consistent with the results found in airspaces . To determine the activation states of the AMs , we measured macrophage activation markers MHCII and CD86 on AMs . Four days after LPS i . n . , Aoah-/- AMs ( CD11chi , hi autoflorescence cells ) had significantly greater expression of MHCII ( Fig 3D ) and CD86 ( Fig 3E ) on the cell surface , suggesting that Aoah-/- AMs were highly activated . CD11c expression was also maintained at high levels in Aoah-/- AMs ( Fig 3F ) . Thus , when the LPS-inactivating enzyme was absent , alveolar inflammation persisted and AMs were greatly activated . LPS i . n . instillation induces pro-inflammatory and anti-inflammatory gene expression in the lung . To test whether AOAH influences LPS-induced cytokine gene expression , we measured cytokine mRNA abundance in lung homogenates . On day 1 after LPS exposure , LPS induced pro- and anti-inflammatory gene expression in both Aoah+/+ and Aoah-/- lungs . When cytokine expression in Aoah+/+ mouse lungs started to return to basal levels on day 4 , Aoah-/- mouse lungs had significantly elevated cytokine gene mRNA , suggesting that AOAH promotes the cessation of inflammatory cytokine expression ( Fig 3G ) . IRAK-M , a negative regulator of TLR signaling , also remained at higher levels on day 4 in Aoah-/- mouse lungs ( Fig 3G ) . The persistent expression of IRAK-M and anti-inflammatory cytokine IL-10 also suggests the presence of unresolved inflammation and these anti-inflammatory molecules may counteract inflammation to protect lung tissues . Taken together , these results strongly suggest that AOAH accelerates inflammation resolution in the lung . To test whether AOAH specifically ameliorates LPS-induced lung injury , we compared HCl-induced acute lung injury in Aoah+/+ and Aoah-/- mice . HCl elicited similar leukocyte infiltration in the alveolar space and the inflammation resolved with comparable kinetics in Aoah+/+ and Aoah-/- mice ( Fig 4 ) , suggesting that AOAH specifically modulates acute lung injury induced by its substrate , LPS . Neutrophil infiltration is a sign of inflammation and neutrophil clearance is the most important marker of recovery from inflammation [9] . In response to intranasal LPS , alveolar neutrophil clearance was delayed in Aoah-/- mouse lungs ( Figs 2F and 3A–3C ) . We then studied the possible mechanisms for delayed neutrophil clearance , hypothesizing that the persistence of neutrophils in Aoah-/- lung tissue is caused by increased recruitment , decreased apoptosis , or a combination of these factors . We tested neutrophil apoptosis 3 days after LPS i . n . using Annexin V and 7-AAD staining . Similar rates of neutrophil apoptosis were found in Aoah+/+ and Aoah-/- alveoli ( S3 Fig ) . We then compared the mRNA expression levels of neutrophil chemotactic chemokines/cytokines in Aoah+/+ and Aoah-/- lungs . We instilled 150 μg LPS , which induced more prolonged neutrophil infiltration in Aoah-/- lungs than did 10 μg LPS . Day 1 after LPS i . n . , both strains of mice had dramatically elevated CXCL-2/3 ( MIP-2 ) , CXCL-1 ( KC ) , IL-23 , IL-17 and CXCL5 levels , while on day 4 and day 7 , Aoah-/- lungs had significantly higher CXCL-2/3 ( MIP-2 ) , CXCL-1 ( KC ) and CXCL5 expression than did Aoah+/+ lungs ( Fig 5A ) , suggesting that persistent chemokine production sustained neutrophil recruitment and prevented neutrophil clearance . To identify a source of these chemokines/cytokines , on day 7 after 150 μg LPS instillation i . n . we separated lung parenchymal cells ( CD45- ) from immune cells ( CD45+ ) using MACS . CD45+ cells produced more MIP-2 and IL-17 than did CD45- cells; CD45- cells expressed more KC , IL-23 and CXCL5 than did CD45+ cells ( Fig 5B ) . The expression levels of MIP-2 , KC and CXCL5 were higher in Aoah-/- cells than in their Aoah+/+ counterparts ( Fig 5B ) . As we had found that AMs took up LPS ( Fig 1C and 1D ) and that AMs up-regulated AOAH expression upon exposure to LPS ( Fig 1A and 1B ) , we surmised that AMs deacylate the LPS they ingest . We then hypothesized that when AOAH is missing , AMs cannot deacylate/inactivate the LPS that they take up . Bioactive LPS could then be released [27] to stimulate AMs to produce MIP-2 or to induce AMs and lung epithelial cells to secrete KC . We instilled PBS or 150 μg LPS in PBS i . n . to Aoah+/+ and Aoah-/- mice and cultured their AMs ex vivo 7 days later . Explanted Aoah-/- AMs , which had been exposed to LPS i . n . , secreted significantly more MIP-2 than did Aoah+/+ AMs ( Fig 5C ) . Co-culture with MLE-12 , a commonly used murine lung epithelial line that preserves some features of alveolar epithelial cells [33] , only slightly increased MIP-2 production ( Fig 5C ) , consistent with the finding that CD45+ immune cells are the major source of MIP-2 ( Fig 5B ) . Single culture of explanted LPS-exposed Aoah+/+ or Aoah-/- AMs did not produce much KC in the media , while co-culture of AMs with MLE-12 dramatically enhanced KC secretion ( Fig 5D ) ; in the co-culture , Aoah-/- AMs induced significantly higher levels of KC secretion than did Aoah+/+ AMs ( Fig 5D ) , suggesting that AOAH-dependent inactivation of LPS dampens LPS-induced KC production by lung epithelial cells . To find out whether Aoah-/- AM-derived LPS contributed to the enhanced MIP-2 and KC production , we treated the culture with a LPS inhibitor , polymyxin B , and found that polymyxin B largely reduced chemokine production ( S4 Fig ) . When transwells were used to separate AMs and MLE-12 , KC production was diminished , suggesting that cell-cell contact or proximity is important ( S4 Fig ) . Notably , AMs from LPS i . n . instilled Aoah-/-Tlr4-/- mice did not induce KC secretion when co-cultured with MLE-12 ( Fig 5D ) , whereas adding naïve Tlr4+/+ AMs but not Tlr4-/- AMs to the co-culture of day 7 Aoah-/-Tlr4-/- AMs and MLE-12 dramatically enhanced KC production ( Fig 5E ) . These results strongly suggest that stimulation of AMs via TLR4 is required for KC production by lung epithelial cells . This experiment ( Fig 5E ) also confirmed that stimulatory LPS can be released from Aoah-/-Tlr4-/- AMs . Since LPS stimulates AMs to produce pro-inflammatory cytokines , such as TNF-α and IL-1β , the cytokines may act on epithelial cells to secret chemokines [34] . As we failed to detect TNF-α and IL-1β in the cell culture using ELISA , which has detection limits of 7 . 8 and 15 . 6 pg/ml respectively , we tested the effects of anti-TNF-α and anti-IL-1β blocking antibodies in the co-culture . Blocking either TNF-α and IL-1β significantly reduced KC production; the TNF-α antibody had a more profound effect ( Fig 5F ) . These results suggest that AM-derived pro-inflammatory cytokines stimulate epithelial cells to produce KC . In line with the co-culture results , we found that 7 days after LPS instillation i . n . , Aoah-/- AMs had significantly higher levels of TNF-α , IL-1β , MIP-2 and KC mRNA than did Aoah+/+ AMs ( Fig 5G ) . FACS-sorted Aoah-/- lung epithelial cells also had higher KC mRNA expression than did Aoah+/+ epithelial cells ( Fig 5H ) . It has been shown that , distinct from macrophages form other sites , AMs that have been exposed to LPS in vivo are resistant to endotoxin tolerance or even increase their innate responsiveness [35–37] . To confirm that AMs maintain their responsiveness , 7 days after PBS or LPS instillation i . n . , we explanted AMs and stimulated them with a low dose ( 100 pg/ml ) of LPS . LPS-exposed AMs had increased IL-6 , MIP-2 and KC responses and sustained or slightly decreased TNF responses ( Fig 5I ) . Taken together , the data suggest that AMs can release the LPS they have taken up; when AOAH is lacking , the released bioactive LPS may directly act on AMs to secret MIP-2 and KC , and indirectly stimulate epithelial cells to produce KC . After exposure to LPS in vivo , instead of becoming tolerant , AMs maintained or increased their responsiveness to prolonged stimulation with low doses of LPS . The persistent chemokine/cytokine production may delay neutrophil clearance in Aoah-/- mouse lungs . We have shown that AOAH is required for the recovery from LPS-induced pulmonary inflammation/injury . We then asked whether AOAH was also important for recovery from pulmonary inflammation induced by Gram-negative bacteria , which contain LPS and many other TLR agonists [38] . We tested Klebsiella pneumoniae , a clinically important Gram-negative bacterium that may cause ARDS . As 1500 CFU of the Klebsiella pneumoniae strain we used killed 80% of Aoah+/+ mice , to induce significant pulmonary inflammation while preventing bacteria-induced death we instilled 5 X 106 heat-killed Klebsiella pneumoniae . One day after instilling Klebsiella pneumoniae , Aoah-/- and Aoah+/+ mouse airspaces had similar neutrophil infiltration , similar activation of AMs and their lungs had comparable cytokine and chemokine expression ( Fig 6A–6C ) , while 4 days after instillation , in contrast , we found significantly more neutrophils , lymphocytes and macrophages in Aoah-/- airspaces than in Aoah+/+ airspaces ( Fig 6A ) . There was also higher expression of MHC II and CD11c on Aoah-/- AMs than on Aoah+/+ AMs ( Fig 6B ) and Aoah-/- lungs had greater IL-6 , TNF-α , KC , MIP-2 , IL-17a , CXCL5 and IL-10 mRNA expression than did Aoah+/+ lungs ( Fig 6C ) . AOAH thus promoted recovery from lung inflammation induced by an opportunistic Gram-negative bacterial pathogen , indicating that even though many other TLR agonists were present , the detoxification of LPS by AOAH was required for resolution of inflammation . To study whether chronic exposure to LPS may cause accumulative effects in the lungs of mice that cannot inactivate LPS , Aoah+/+ and Aoah-/- mice were treated with 10 μg LPS i . n . every 3–4 days for 8 weeks . Four days after the last instillation , Aoah+/+ mice received LPS i . n . instillation had slightly more neutrophils and monocyte-macrophages than did control mice ( PBS , i . n . ) , while Aoah-/- mice had significantly more neutrophils , and lymphocytes in their BALF than did Aoah+/+ mice , demonstrating the exaggerated inflammation that occurs in Aoah-/- mouse airspaces ( Fig 7A ) . Pathological analysis also showed that Aoah-/- lung tissue had more leukocyte infiltrates and aggregates ( Fig 7B ) . Thus , AOAH controls chronic LPS exposure-induced lung inflammation .
Recognizing and responding to MAMP molecules are critical steps in antimicrobial host defense . When the infection is under control , timely resolution of inflammation is important for the host to prevent severe tissue injury and regain homeostasis [15] . How MAMP molecules are degraded/inactivated in the lung and whether the clearance of MAMPs plays a role in the resolution of inflammation have been unclear . In this study , we investigated the roles that an endotoxin-degrading ( deacylating ) enzyme , AOAH , plays in the recovery from endotoxin-induce ALI . We found that AOAH expression in AMs was highly inducible in response to certain MAMP molecules ( LPS and Pam3CSK4 ) ; that AMs took up LPS instilled i . n . ; that AOAH shortened LPS-induced alveolar and lung inflammation , alveolar damage and ameliorated morbidity and mortality; and that , in mice that lack AOAH , bioactive LPS can be released from AMs and stimulate AMs as well as lung epithelial cells ( indirectly ) to produce neutrophil chemoattractants that may delay neutrophil clearance from the lung . AOAH also accelerated the resolution of inflammation induced by whole Gram-negative bacteria and diminished the inflammation induced by chronic LPS exposure . Thus , AOAH hastened the resolution of LPS or Gram-negative bacteria-induced lung inflammation/injury and promoted the restoration of homeostasis ( Fig 8A ) . To our knowledge , this is the first evidence to date that disabling a MAMP molecule promotes recovery/resolution of infection-induced tissue injury . Naïve mouse lungs do not express high levels of AOAH mRNA or AOAH enzymatic activity [39] . However , AOAH expression in AMs is highly inducible in response to LPS and other TLR agonists ( such as Pam3CSK4 ) but not to HCl ( Fig 1 ) , consistent with the reported ability of TLR agonists to upregulate AOAH expression in dendritic cells [38] . These results indicate that when the lung detects MAMP molecules , not only are inflammatory responses elicited , but the program to destroy a key microbial signal molecule is also initiated . Indeed , this is the most dynamic up-regulation of AOAH expression observed so far [38 , 40 , 41] . When LPS was instilled i . n . , AMs took up more LPS than did neutrophils and other cells in the airspace . Because AOAH expression in AMs is induced , LPS can be degraded inside AMs ( Fig 1 ) . Stamme and Wright have shown that AMs deacylate LPS and that surfactant protein A enhances their ability to do so [42] . Neutrophils , which also make AOAH and internalize LPS , may also play a role in deacylating instilled LPS [19 , 43] . Furthermore , AOAH may be secreted [44] and LPS can be degraded in extracellular fluids . Katz et al . , showed that rabbit peritoneal exudate fluid was able to deacylate LPS [45] and Gioannini et al . reported that CD14 and LBP could present extracellular LPS for deacylation by AOAH [46] . Whether LPS can be degraded extracellularly in the lung requires further investigation . We reported previously that LPS injected i . p . was taken up by peritoneal macrophages [26 , 27 , 47] . In Aoah-/- mice , these macrophages released bioactive LPS into the peritoneal fluid and peritoneal macrophages remained tolerant for months [27] . Mice with prolonged tolerance mounted sluggish innate immune responses to Gram-negative bacterial challenge and died of uncontrolled infection [26] . In this study we found that Aoah-/- AMs also took up LPS and released bioactive LPS . Distinct from peritoneal macrophages , however , upon exposure to LPS in vivo , AMs do not become tolerant [35 , 36] . Instead , they respond to repeated LPS exposure by maintaining or even increasing their responsiveness [37 , 48]; they are able to produce chemoattractants and cytokines in the presence of persistent LPS ( Fig 5 ) , thus delaying clearance of neutrophils and prolonging inflammation . For these cells ( and presumably for other LPS-responsive cells that do not develop tolerance ) , MAMP inactivation is especially important to terminate inflammatory responses and prevent tissue injury . AOAH deficiency thus has distinct consequences in different body compartments: prolonged tolerance in the peritoneum yet sustained inflammation in the lung . As other inflammatory responses differ in the lung and peritoneum [35 , 49–52] , so the impact of AOAH-mediated LPS inactivation is also compartmentalized ( Fig 8B ) . AOAH did not diminish the initiation of inflammatory responses to LPS in the lung , as demonstrated by the similar degrees of alveolar damage , MPO activity , lung pathology , BALF and lung inflammatory cell infiltration , and lung inflammatory cytokine or chemokine expression that occurred in Aoah+/+ and Aoah-/- mice one day after LPS exposure ( Figs 2 , 3 and 5 ) . The requirement to induce AOAH expression ( Fig 1 ) and the slow kinetics of LPS-degradation by the enzyme in vivo [22–24] probably account for this observation . However , during the recovery phase , AOAH was important for inflammation resolution . Whereas Aoah+/+ mice started to recover from inflammation on day 4 when either high or low doses of LPS were instilled , Aoah-/- mice demonstrated persistent inflammation and alveolar damage , delayed recovery and elevated morbidity and mortality ( Figs 2 , 3 and 5 ) . Furthermore , after exposure to Gram-negative bacteria , Klebsiella pneumoniae , Aoah-/- mouse lung also had prolonged inflammation ( Fig 6 ) . These results strongly suggest that LPS is one of the most potent bacterial MAMP molecules and that it must be inactivated to permit the resolution of Gram-negative bacteria-induced inflammation in the lung . Neutrophils are among the first responders to infection . Clearance of neutrophils from tissue , as a marker of inflammation resolution , involves neutrophil apoptosis , efferocytosis by AMs , and cessation of further neutrophil recruitment [53] . Although studies have shown that LPS and inflammatory mediators inhibit neutrophil apoptosis and extend their functional longevity [54–56] , in our ALI model we did not observe different neutrophil apoptotic rates in Aoah-/- mouse lungs . Instead , we observed that the concentrations of neutrophil chemoattractants MIP-2 , KC and CXCL5 failed to drop as quickly as they did in the lungs of Aoah+/+ mice ( Figs 5 and 6 ) . Using an ex vivo culture system , we found strong evidence that Aoah-/- AMs could not inactivate the LPS they ingested and that they released LPS that could stimulate AMs and lung epithelial cells to produce neutrophil chemoattractants ( Figs 5 and 6 ) . AMs produce TNF-α and IL-1β upon LPS stimulation [34] . TNF-α is known to be able to induce epithelial cells to secret GM-CSF to promote tissue repair [57] and to enhance monocyte transmigration during influenza infection [58] . In this study , we found that AM-derived TNF-α and IL-1β stimulated KC production by epithelial cells , pointing to functional cross-talk between lung immune and parenchymal cells . In addition to macrophage-derived IL-1Ra and MMP12 , which attenuate alveolar neutrophil recruitment [53] , here we show that AMs promote neutrophil clearance by inactivating a MAMP molecule . AOAH inactivates LPS by removing the secondary fatty acyl chains from the lipid A moiety , converting a hexaacyl ( or hepta- or penta-acyl , in different LPSs ) structure into one that has only four acyl chains . The tetraacyl moiety binds to MD-2 yet does not initiate signaling by TLR4; in fact , AOAH-treated LPS can be a potent LPS antagonist [21] . Although the lack of ALI observed in LPS-instilled TLR4-/- mice argues strongly that signaling downstream of TLR4 is required , it is interesting to note that the recently reported cytosolic , caspase-based system for sensing LPS also recognizes hexaacyl lipid A; tetraacyl molecules bound to caspase 4/5/11 yet were poor agonists [59–61] . It is thus likely that AOAH-mediated deacylation/inactivation will also contribute to the resolution of inflammation induced by this mechanism . In human studies , single nucleotide polymorphisms in the AOAH gene have been shown to be associated with asthma [62] and chronic rhinosinusitis in Chinese and Canadian Caucasian populations [63] , suggesting that AOAH may regulate some pulmonary inflammatory responses in humans . It is important to note that another host enzyme , acidic mammalian chitinase ( AMCase ) , also modulates lung inflammation . When AMCase was over-expressed in mouse lung , eosinophil infiltration after fungal challenge was diminished [64] . Moreover , a gain-of-function AMCase haplotype has been associated with asthma protection [65] , while another study found that AMCase is causative in asthma [66] . These and our findings all point to the important roles that inactivating MAMP molecules may play in modulating MAMP-induced inflammation in the lung . In conclusion , we report a previously unappreciated role of AOAH in promoting resolution of LPS- or Gram-negative bacteria-induced lung injury . In addition to the many other pro-resolution mechanisms , our study demonstrates that inactivating MAMP molecules can be critical for recovery from MAMP-induced lung injury . It would be interesting to investigate whether MAMP-inactivation is required for turning on the synthesis of “the specialized proresolving mediators” such as lipoxins and resolvins [12 , 13 , 67] . Interventions that accelerate the degradation of LPS and other MAMP molecules in tissue may promote resolution of inflammation .
Aoah-/- , B6 . B10ScN-Tlr4lps-del/JthJ ( Tlr4−/− ) , Aoah-/-Tlr4-/- and control Aoah+/+ C57BL/6J mice were obtained from the National Institutes of Health , USA ( R . S . Munford ) . Aoah-/- mice were generated as previously described [22] . The Aoah gene mutation had been backcrossed to C57Bl/6J mice for at least 10 generations . Tlr4-/- mice have a 7 kb deletion in the TLR4 gene; the mutation had been backcrossed to C57Bl/6J for at least 6 generations . Aoah-/- Tlr4-/- were obtained by crossing Aoah-/- mice with Tlr4-/- mice . Ccr2-/- C57BL/6J mice were from Fudan University Shanghai Medical College ( Rui He ) . Age- and gender- matched 6–10 weeks old mice were used . The experiments in Figs 3 and 5 were repeated with similar results using littermates produced by Aoah heterozygous breeders . All mice were housed in a specific pathogen-free facility in the department of laboratory animal science of Fudan University . All mice were studied using protocols approved by the Institutional Animal Care and Use Committee ( IACUC ) of Fudan University ( approved animal protocol number 20140226–093 ) . All protocols adhered to the Guide for the Care and Use of Laboratory Animals . Mice were anesthetized i . p . with 0 . 5% pentobarbital sodium ( 50 μg/g body weight ) and exsanguinated by cutting the inferior vena cava . Bronchoalveolar lavage ( BAL ) was performed by cannulating the trachea with a 20-gauge catheter that was firmly fixed with a suture . The lung was then infused with 1 ml PBS containing 5 mM EDTA and the lavage fluid ( bronchoalveolar lavage fluid , BALF ) was extracted . This procedure was repeated 5 times and the BALF was combined . Cells in BALF were collected by centrifugation and then re-suspended in complete RPMI medium containing 10% fetal bovine serum ( Hyclone ) , 2 mM glutamine , 100 U/ml penicillin , and 0 . 1 mg/ml streptomycin ( Life Technologies ) . After adherence at 37°C for 2 hours and washing , mouse AMs were then untreated or treated with 10 ng/ml LPS O111 ( Sigma ) , 1 μg/ml Pam3CSK4 ( Invivogen , TLR1/2 agonist ) , 10 μg/ml Poly I:C ( Invivogen , TLR3 agonist ) or 1 μg/ml Pam3CSK4 + 10 μg/ml Poly I:C for 18 hours before AOAH mRNA analysis . Both Pam3CSK4 and Poly I:C had endotoxin levels < 0 . 001 EU/μg . We used Pam3CSK4 or Poly I:C to stimulate Tlr4+/+ and Tlr4-/- peritoneal macrophages and comparable amounts of IL-6 were measured in the culture medium , confirming low endotoxin contamination . In some experiments , Aoah+/+ mice were instilled i . n . with 200 μg LPS or 50 μl 0 . 2M hydrochloric acid ( HCl ) ; their BALF was harvested 18 hrs later and AMs were purified for AOAH expression analysis . Mice were anesthetized and instilled with 20 μg E . coli LPS-O111-FITC ( Sigma ) i . n . Eighteen hours later , cells in airspaces were collected in BALF and stained with anti-CD11c , anti-CD11b and Ly6G antibodies ( BD ) to identify AMs and other cell populations . Cells were then washed , fixed and permeablized with Cytofix/Cytoperm buffer ( BD ) . Anti-FITC antibody conjugated with FITC ( Clone LO-FLUO-1 , Life technologies ) was added to magnify the signal . Cells were analyzed by flow cytometry . Trypan blue , which quenches extracellular FITC , did not decrease FITC Geo mean fluorescence intensity , suggesting that most of the LPS-FITC had been internalized by the cells . Before and after mice received 200 μg E . coli LPS , i . n . , they were weighed daily and quantitatively assessed for their morbidity in a blinded manner . The clinical scoring system includes weight loss ( > 10% ) , piloerection , ocular exudate , rapid shallow breathing and lethargy . Each finding was assigned a score of 1 . LPS-induced acute lung injury: Aoah+/+ and Aoah-/- mice were anesthetized and instilled with 10 μg , 150 μg or 200 μg LPS O111 ( Sigma ) in 40–50 μl PBS or PBS only ( control ) i . n . Ten μl LPS or PBS was instilled each time and the instillation was repeated for 4 or 5 times with 1–2 minute intervals . On day 1 , 4 , 7 after LPS i . n . , mice were euthanized . After lungs were perfused with PBS , BALF was obtained and then lungs were excised for assays . Hydrochloric acid ( HCl ) -induced lung injury: This model mimics pneumonitis caused by aspiration of gastric contents ( aspiration pneumonitis [6 , 68] ) . Mice were anesthetized and then were instilled with 50 μl 0 . 2M HCl or PBS ( control ) i . n . On day 1 , 4 after LPS i . n . , mice were euthanized and immune cells in BALF were analyzed . LPS-induced chronic lung inflammation: Mice were anesthetized and instilled with 10 μg E . coli LPS or PBS i . n . twice a week for eight weeks . Four days after the last LPS administration , BAL was performed for immune cell analysis . After BAL was performed , the BALF was centrifuged . The cell-free supernatant was used to measure protein using a bicinchoninic acid ( BCA ) kit ( Pierce ) . The cell pellet was re-suspended in PBS and total cell numbers were counted using Cellometer ( Nexelcom ) . Cell differentials were counted after cytospin and Wright-Giemsa staining . Three hundred cells , including monocytes-macrophages , neutrophils and lymphocytes were counted for each sample . We tested alveolar leakage by measuring extravascular Evans blue in the lung . Briefly , mice were injected with 0 . 5 mg Evans blue ( Sigma ) i . v . 60 min before euthanasia . Lungs were then perfused to remove intravascular dye . Lungs were excised and homogenized in PBS . One volume of lung homogenate was incubated with 2 volumes of formamide and incubated at 60°C for 18 hrs before centrifugation . The optical density of the supernatant was measured at 620 nm and 740 nm using a Tecan reader . The concentrations of Evans blue were corrected for the presence of heme pigments using the following formula: A620 ( corrected ) = A620 ( raw ) — ( 1 . 1927 X A720 ) + 0 . 0071 [69] . The extravasated Evan blue dye concentrations were then calculated against a standard curve . RNA from AMs or lungs was isolated using RNA isolation kit ( Tiangen ) and reversely transcribed ( Tiangen ) . The primers used for RT-PCR were listed in S1 Table . Actin was used as the internal control and the relative gene expression was calculated by the ΔΔCt quantification method . Cells in BALF were collected by centrifugation , incubated with Fc blocking antibody ( purified anti-mouse CD16/32 , BioLegend ) to prevent binding of nonspecific FcγRIII/II , and then incubated with detection antibodies for 1 hr on ice . After washing , the samples were analyzed by CyAn-ADP ( Beckman Coulter ) and data were processed using Flow Jo software ( TreeStar , Inc ) . All antibodies used for flow cytometry were anti-mouse antigens . Anti-CD11b ( Clone M1/70 ) , anti-CD11c ( Clone N418 ) , anti-Siglec F ( Clone E50-2440 ) , anti-MHCII ( Clone M5/114 . 15 . 2 ) , anti-Ly6G ( Clone 1A8 ) , anti-CD45 ( Clone 30-F11 ) , anti-CD86 ( Clone FL1 ) were from BD Biosciences . Anti-F4/80 ( Clone BM8 ) antibody was from BioLegend . To measure immune cells in the lung , the lung was perfused and then the trachea was cannulated with a 20-gauge catheter . The lung was instilled with 1 ml digestion buffer , which contained RPMI 1640 , 1 mg/ml collagenase IV ( Sigma ) and 5U/ml DNase I ( Sigma ) . The lung was excised , cut into 1 mm3 pieces and incubated in a culture tube containing 2 ml digestion buffer at 37°C for 1 hr with shaking . The digested lung tissues were filtered through a 70 mm cell strainer . Red blood cells were then lysed by using ACK lysis buffer ( eBioscience ) . After total cell numbers were counted , cells were stained with antibodies and subjected to flow cytometric analysis of cell type . After lungs had been excised and fixed in 4% paraformaldehyde , they were sectioned and stained with hematoxylin and eosin ( H&E ) . The samples were examined for inflammatory cell infiltration , tissue damage and alveolar edema by using a Nikon E200 microscope . To identify the source of neutrophil chemoattractants , lung single cell suspensions were made as described above . Cells were counted and separated into CD45+ or CD45- cells by using anti-CD45 antibody-conjugated magnetic beads ( Miltenyi Biotec ) according to the manufacturer’s instructions . In another experiment , CD45- CD326+ alveolar epithelial cells were sorted using FACS . The purity of CD45+ , CD45- cells and CD45- CD326+ was above 95% by flow cytometric analysis . Lung samples were homogenized in a buffer containing 0 . 5% hexadecyltrimethylammonium bromide ( HTAB ) , 0 . 5 mM EDTA , 500 mM potassium phosphate buffer ( pH 7 . 0 ) . The lung tissue slurry was pelleted and the supernatant was added to MPO assay buffer , which contained 50 mM potassium phosphate buffer ( pH 7 . 0 ) , 0 . 0005% H2O2 ( w/w ) , 0 . 168 mg/ml o-dianisidine dihydrochloride ( ODH ) . After 25 mins incubation at 25°C , the plate was read at a wavelength of 490nm ( Tecan ) . To find out whether AMs can release the LPS they ingested , BALF was collected from Aoah+/+ and Aoah-/- mice seven days after they had received 150 μg LPS intranasally . The AMs were isolated and 0 . 5 X 106 AMs were cultured or co-cultured with the same number of mouse lung epithelial cells ( line MLE-12 , ATCC CRL2110 ) for 6 hrs , then MIP-2 and KC concentrations in the culture media were measured by using ELISA ( R&D ) . In some experiments LPS inhibitor polymyxin B ( 20 μg/ml ) ( Sigma ) , anti-mTNF-α ( Clone MP6-XT22 , BD ) or anti-IL-1β ( Clone , B122 , Biolegend ) blocking antibodies ( 10 μg/ml ) , or isotype control antibodies ( 10 μg/ml , BD and Biolegend respectively ) was added to the media . In other experiments , AMs were separated from MLE-12 cells by transwells ( Corning ) . We also instilled 150 μg LPS i . n . to Aoah-/- Tlr4-/- mice , and seven days later we co-cultured their AMs with MLE-12 cells plus naïve Tlr4+/+ or Tlr4-/- AMs for 6 hrs . KC concentrations were measured in the culture media . Aoah+/+ and Aoah-/- mice were instilled i . n . with heat-killed ( boiled for 15 minutes ) 5 X 106 Klebsiella pneumoniae subsp . pneumoniae ( ATCC , 43816 , Serotype 2 ) . Before instillation and 1 or 4 days after , their BALF were harvested for immune cell analysis . The inoculation dose was tested to be able to elicit significant pulmonary inflammation . One-way ANOVA test was used to compare multiple groups . Unpaired Student’s t test ( two-tailed ) was used for comparisons between two groups with significance set at p < 0 . 05 . To compare survival proportions , Log-rank test was used . To compare two trends over time , two-way ANOVA test was used . * , P < 0 . 05; ** , P < 0 . 01; *** , P < 0 . 001 . Data are presented as mean ± standard error of the mean ( SEM ) .
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Little is known about how MAMP molecules are degraded/inactivated and how inactivating them alters the course of inflammation in vivo . Our studies demonstrate that deficiency of acyloxyacyl hydrolase ( AOAH , a host enzyme that inactivates LPS ) , prolongs pulmonary inflammation and acute lung injury in mice challenged with LPS or Klebsiella pneumoniae . This is the strongest evidence to date that disabling a MAMP molecule promotes recovery of infection-induced tissue injury . Degradation/inactivation of MAMP molecules is especially important because alveolar macrophages , unlike macrophages in many other tissues , do not become “tolerant” when they are exposed to LPS . In other words , they lack this general mechanism for down-regulating inflammation . Our findings indicate that inactivating LPS can compensate to a large extent for this deficiency–without AOAH , the LPS- or Klebsiella-challenged mice failed to resolve acute lung injury and were more likely to die . We reported previously that persistently bioactive LPS can maintain tolerance in peritoneal macrophages for many weeks in AOAH-deficient mice . The new findings show that the inability to inactivate a MAMP has different consequences in the lungs and peritoneum , in keeping with evidence that innate immune responses are compartmentalized . Inactivation of MAMP molecules can be an important and non-redundant prerequisite for inflammation resolution .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[] |
2017
|
Acyloxyacyl hydrolase promotes the resolution of lipopolysaccharide-induced acute lung injury
|
To investigate the role of DNA topoisomerases in transcription , we have studied global gene expression in Saccharomyces cerevisiae cells deficient for topoisomerases I and II and performed single-gene analyses to support our findings . The genome-wide studies show a general transcriptional down-regulation upon lack of the enzymes , which correlates with gene activity but not gene length . Furthermore , our data reveal a distinct subclass of genes with a strong requirement for topoisomerases . These genes are characterized by high transcriptional plasticity , chromatin regulation , TATA box presence , and enrichment of a nucleosome at a critical position in the promoter region , in line with a repressible/inducible mode of regulation . Single-gene studies with a range of genes belonging to this group demonstrate that topoisomerases play an important role during activation of these genes . Subsequent in-depth analysis of the inducible PHO5 gene reveals that topoisomerases are essential for binding of the Pho4p transcription factor to the PHO5 promoter , which is required for promoter nucleosome removal during activation . In contrast , topoisomerases are dispensable for constitutive transcription initiation and elongation of PHO5 , as well as the nuclear entrance of Pho4p . Finally , we provide evidence that topoisomerases are required to maintain the PHO5 promoter in a superhelical state , which is competent for proper activation . In conclusion , our results reveal a hitherto unknown function of topoisomerases during transcriptional activation of genes with a repressible/inducible mode of regulation .
Early studies of transcription have demonstrated that DNA topoisomerases are important in the transcription process [1] . The enzymes transiently break and rejoin the phosphodiester backbone of DNA to allow the passage of individual DNA strands or double helices through one another [2] , [3] . In this way they regulate DNA superhelicity and solve topological problems arising during DNA metabolism . In Saccharomyces cerevisiae , DNA superhelicity is influenced by topoisomerases I and II ( Top1p and Top2p ) , encoded by the TOP1 and TOP2 genes , respectively [3] . Although both enzymes are able to relax supercoiled DNA , they show different substrate preferences , with Top2p being much faster than Top1p , when nucleosomal DNA is relaxed , whereas the opposite is the case during relaxation of naked DNA [4] . Despite these differences , early studies in yeast have demonstrated that transcription is more or less unaffected in yeast cells lacking either Top1p or Top2p , indicating that the two enzymes are redundant in the transcription process . Conversely , top1Δtop2ts mutants grown under restrictive conditions display a decreased rate of both rRNA and mRNA synthesis [1] . Transcription and DNA supercoiling are linked by a cause-effect relationship that operates in both directions . The transcriptional effect on supercoiling is explained by the Twin-Supercoiled-Domain-Model , which predicts that two domains of DNA supercoiling are generated during transcription elongation , provided that the RNA polymerase cannot rotate freely around the template , and that DNA rotation is hindered [5] . Thus , positive and negative supercoiling will be formed in front of and behind the advancing polymerase , respectively . The model , which has gained support from both in vitro and in vivo studies [1] , [6] , [7] , implies that a gradient of positive and negative supercoils will dissipate from an active transcription unit if topoisomerase activity is lacking . The effect exerted by supercoiling on transcription has in many cases been demonstrated to depend on the sign of the supercoils . Thus , positive supercoiling has been suggested to impair transcription initiation as well as elongation by inhibition of strand separation [8] , [9] . In contrast , negative supercoiling has been suggested to be more favorable for transcription , in that it may facilitate transcription initiation by enhancing complex formation at promoters [10]–[12] . The crosstalk between DNA supercoiling and transcription still remains elusive in vivo , where chromatin structure adds another layer of complexity . Dissociation and re-association of nucleosomes will release and absorb negative superhelicity , respectively , with a potential impact on transcription [13] , and topoisomerases have indeed been demonstrated to affect nucleosome dynamics [14]–[16] . Furthermore , chromatin has been suggested to adapt to positive supercoiling by a slight conformational change , which is reverted upon relaxation by either Top1p or Top2p [4] . This implies that the chromatin fiber is a torsionally resilient structure , which can act as a topological buffer in vivo and facilitate dissipation of topological strain [4] , [9] , [17] . In eukaryotes , a change in DNA superhelicity may thus exert an additional effect on transcription via changes at the chromatin level . Several studies have suggested that the individual topoisomerases play a role during transcription initiation . Thus , human topoisomerase I has been demonstrated to affect transcription initiation from TATA-containing promoters , functioning as a repressor of basal transcription but as an enhancer of activated transcription [18] . In line with this , studies with yeast Top1p have suggested that the enzyme exerts an inhibitory effect on transcription initiation of a subset of stress-inducible genes located in the silenced subtelomeric regions [19] . Concerning topoisomerase II , experiments performed with a topoisomerase II inhibitor have demonstrated a role of this enzyme in the activation of specific oncogenes , where activation reflects a change in promoter structure [20] . In addition , mammalian topoisomerase IIβ has been found to directly affect transcription initiation of an inducible gene by creating a specific DNA double strand break in the promoter region allowing nucleosome displacement and downstream protein recruitment [21] . Recent studies of transcription using genome-wide approaches have further substantiated a role of topoisomerases during transcription initiation . In a study performed in S . pombe , Top1p was suggested to be directly responsible for nucleosome disassembly in gene promoters prior to transcription [14] . However , in a study performed in S . cerevisiae , Top1p and Top2p were suggested to act redundantly to allow recruitment of RNA polymerase II to nucleosome-free promoters rather than to act in nucleosome removal per se [22] . In both cases topoisomerases were found to bind preferentially to promoter regions of highly active genes . The precise role of DNA topoisomerases in transcription is thus still not clear . Indeed , steps upstream of the engagement of polymerases and nucleosome removal could be influenced by DNA supercoiling , i . e . binding of transcriptional activators or repressors . In the present study , we have combined microarray gene expression analyses and single-gene studies using S . cerevisiae strains lacking either one or both DNA topoisomerases to unravel the implications of these enzymes on transcription . Although we demonstrate that the requirement for topoisomerases generally correlates with transcriptional activity we find that DNA topoisomerases have a major impact on transcription of a subset of genes , which are not unified by being highly transcribed per se . Rather , the most affected genes are characterized by features associated with highly regulated transcription initiation . Studies of several genes from this subgroup demonstrate that topoisomerases indeed are required for adequate and timely transcriptional induction . Finally , in case of the inducible PHO5 gene , we demonstrate that topoisomerase-mediated relaxation is required for binding of the Pho4p transcription factor , whereas constitutive PHO5 transcription is unaffected by topoisomerase deficiency .
To investigate the impact of DNA topoisomerases I and II ( Top1p and Top2p , respectively ) on genome-wide transcription , we examined the S . cerevisiae polyadenylated transcriptome by microarray analysis in top1Δ , top2ts , top1Δtop2ts and the isogenic wild-type strain . To bypass genome-wide effects of topological challenges caused by replication [23] , [24] as well as abortive mitosis due to lack of Top2p activity , the window of transcription was limited to the G1-phase of the cell cycle , and cells were grown at the restrictive temperature for conditional inhibition of Top2p ( Figure 1A ) . Due to the expected drop in RNA synthesis in cells lacking topoisomerase activity [1] , external normalization was used to compensate for unbalanced gene expression changes [25] ( see Text S1 ) . As seen in Figure 1B , a genome-wide decrease of most transcripts is observed in top1Δtop2ts , reflecting an absolute drop in mRNA abundance at the cellular level of ∼30% ( Figure 1C ) . Furthermore , around 20% of all genes are 2-fold or more up- or down-regulated in the double mutant , where the down-regulated genes account for ∼17% ( Figure 1D ) . In contrast , the single mutants show a drop of only 10% in mRNA abundance ( Figure 1C ) and a relatively low number of de-regulated genes ( Figure 1D ) , suggesting a redundant nature of the two enzymes in genome-wide transcription as indicated earlier [1] . To address , whether the global transcriptional down-regulation in topoisomerase deficient cells can be explained by effects predicted by the Twin-Supercoiled-Domain-Model , we considered two simple parameters , transcriptional activity and transcript length , which are both proportional to the number of DNA supercoils produced during transcription of a specific gene [5] . As shown in Figure 2A , a plot of gene expression changes in top1Δtop2ts against wild-type mRNA abundances reveals that genes with higher mRNA abundance are more affected by topoisomerase deficiency relative to genes with lower abundance ( Pearson correlation = −0 . 35 ) . In contrast to the double mutant , the single mutants show no correlation between transcript changes and wild-type mRNA abundance , consistent with the redundant nature of the two enzymes in genome-wide transcription . Relative measures of transcriptional activity for every gene were obtained from measures of mRNA abundance by taking gene specific values of polyA mRNA breakdown into account as described by Schreiber and co-workers [26] . As shown in Figure 2B , we found increasing topoisomerase dependency with increasing transcriptional activity ( Pearson correlation = −0 . 34 ) . We therefore conclude that topoisomerase deficiency generally has a larger impact on highly active genes relative to less active genes . A similar conclusion was reached by use of average RNA polymerase II occupancy instead of transcriptional activity ( Figure S1 ) . We next related the top1Δtop2ts transcript changes to a genome-wide survey of transcript lengths [27] . However , no correlation between transcript length and topoisomerase dependency was found ( Figure 2C ) ( Pearson correlation = 0 . 00 ) . Furthermore , a statistical test of all transcripts shorter than 0 . 5 kb ( n = 355 ) and larger than 4 . 5 kb ( n = 122 ) revealed no difference in the distribution of top1Δtop2ts gene expression changes ( P = 0 . 87 , Wilcoxon rank-sum test for different distribution ) . In conclusion , the data demonstrate that the requirement for topoisomerases during global gene transcription increases with increasing transcriptional activity , but is independent of transcript length . Despite the finding that transcriptional activity is a global indicator of topoisomerase dependency , we noticed that a range of the most actively transcribed genes were not among the most de-regulated genes in top1Δtop2ts ( Figure S2 ) . Thus , features other than transcriptional activity per se may be responsible for topoisomerase requirements during gene transcription . To look for common traits and overrepresentation of biological functions among the genes strongly affected by topoisomerase deficiency , we performed gene ontology analyses . As reported in Table S1 , topoisomerase deficiency preferentially affects transcription of genes involved in diverse metabolic pathways and in the response to stress , which are genes for which transcription is typically altered , when environmental conditions are changed [28] , [29] . We therefore investigated , whether genes affected by topoisomerases display a higher responsiveness to environmental changes relative to genes , which are unaffected by topoisomerases . A measure for responsiveness to environmental changes was derived for every gene by calculating the average transcript change of the gene across the Gasch data set consisting of 173 microarray transcription profiles obtained from cells subjected to diverse environmental perturbations [28] . As reported in Table S2 we found that topoisomerase dependent genes , including both up- and down-regulated genes , have significantly higher responsiveness to environmental changes relative to the rest of the genome . Our data therefore suggest that DNA topoisomerases play an important role in the regulatory network of gene expression in the response to environmental changes . This finding prompted us to look for denominators that are common to transcription of topoisomerase-dependent genes . Indeed , we found that this group of genes has a significant enrichment of genes with a TATA-box in the promoter region as well as genes dependent on the SAGA ( Spt-Ada-Gcn5-acetyltransferase ) complex ( Table S2 , Figure S2 ) . Both are features , which predominantly are associated with regulation of genes with a repressible/inducible mode of regulation [30] , [31] . In contrast , TATA-less genes tend to have housekeeping functions and are more constitutively transcribed . Taken together , these analyses suggest that topoisomerase-dependent genes are highly regulated . To investigate if topoisomerase-dependent genes have a higher regulatory capacity relative to genes , which are unaffected by topoisomerase deficiency we took advantage of a gene-specific measure of transcriptional plasticity . This measure has previously been defined as the dynamic range of transcript changes a gene displays in a large collection of >1 , 500 microarray analyses of gene expression [32] . Intriguingly , when plotting transcriptional plasticity against gene expression changes in top1Δtop2ts , we found a curvilinear relationship ( Figure 3A ) . This demonstrates that genes strongly affected by topoisomerase deficiency , including both up- and down-regulated genes , display a high transcriptional plasticity relative to less affected genes . Notably , transcriptional plasticity is correlated with topoisomerase dependency independent of expression levels ( Figure S3 ) . Overall , the characterization of topoisomerase-dependent genes as being highly regulated across a multitude of conditions suggests that topoisomerase deficiency perturbs some of the regulatory features inherent to this gene class . Transcription of highly regulated genes is generally associated with chromatin remodeling and histone modifying activities , which regulate the access of transcription factors and the general transcription machinery to promoters [33] . To further analyze the properties of the topoisomerase-dependent genes , we therefore used a measure of the sensitivity to chromatin regulation , which has been calculated as the expression variability a gene displays in 141 microarray profiles obtained in the absence of different chromatin modifiers [34] . This measure was plotted against gene expression changes in top1Δtop2ts ( Figure 3B ) . As shown in Figure 3B , the genes , which are most affected by topoisomerase deficiency , show the highest sensitivity to chromatin regulation in accordance with the high transcriptional plasticity of these genes . Thus , genes which are activated or repressed at the chromatin level are prone to be influenced by DNA topoisomerases . To further support this finding we calculated pairwise Pearson correlations between the top1Δtop2ts transcription profile and the profiles from more than 1 , 000 different microarrays from yeast . In this screen the most significant correlations were found to transcription profiles generated from yeast strains lacking factors affecting regulation of transcription via chromatin ( e . g . spt6ts , spt16ts , gcn5 mutation , histone depletion and histone tail deletion , Figure S4 ) . The observed correlation between de-regulated transcription in top1Δtop2ts and measures of transcriptional plasticity and chromatin regulation encouraged us to address , whether the genes with the strongest dependency on topoisomerases have a different promoter chromatin architecture compared to topoisomerase-independent genes . We therefore used a map of nucleosome occupancy across the yeast genome [35] to examine the nucleosome binding pattern in the promoter region of these genes . The 100 most up- and down-regulated genes as well as the 100 most unaffected genes in top1Δtop2ts were selected to identify a possible topoisomerase-dependent promoter nucleosome architecture . As seen in Figure 3C and Figure S5 , genes , which are strongly affected by topoisomerase deficiency , have a significant higher nucleosome occupancy in the conserved nucleosome-free region , which is a region known to be enriched for binding of transcription factors and chromatin regulators influencing transcription initiation [36] . Taken together , the data from the genome-wide analyses suggest that topoisomerase deficiency affects transcription of a group of genes , which can be characterized as being highly regulated , thus having a repressible/inducible mode of regulation . Given that highly regulated genes are characterized by tight control of initiation rather than elongation , the data point to an important role of topoisomerases during transcription initiation . We next wanted to substantiate the genome-wide findings by analyses of specific genes with a repressible/inducible mode of regulation that are known to be environmentally regulated and dependent on chromatin structure . For this purpose , twelve genes were selected from four commonly studied gene systems , representing phosphate- ( PHO5 , PHO8 , VTC1 , VTC3 ) , galactose- ( GAL1 , GAL2 , GAL7 , GAL10 ) , glucose- ( ADH2 , ADY2 , YAT1 ) and inositol-regulated ( INO1 ) promoters . In order to study the induction capabilities , wild-type and top1Δtop2ts cells were cultured under repressive conditions and transferred to the respective inducible conditions ( see Text S1 ) as outlined in the experimental setup presented in Figure 4A . As demonstrated in Figure 4B , transcription of all twelve genes is significantly compromised in the absence of DNA topoisomerases . Albeit the selected genes have important differences in many aspects associated with regulation of transcription , e . g . activator and co-factor requirements , the data show that topoisomerases have comparable transcriptional effects on the different inducible gene systems . To verify that topoisomerase deficiency exerts a specific effect on transcription of inducible genes , and furthermore confirm the specificity of the different inducible conditions , we included three housekeeping genes , ESC1 , ACT1 , and GAPDH , where measurements of transcript accumulation were obtained under either phosphate- , galactose- , or glucose-inducible conditions ( Figure S6 ) . These genes showed virtually no transcript changes in wild-type and top1Δtop2ts cells in the time frame , where the inducible genes showed up to several thousand fold increase in mRNA levels in the wild-type . Taken together , our results support a model , where topoisomerases are needed for adequate transcription of regulated genes , and thus corroborate our findings from the microarray gene expression analysis . To investigate in which step during transcription of a regulated gene topoisomerases exert their function , we focused on the well-characterized PHO5 gene , which was found to absolutely require topoisomerases as demonstrated in Figure 4B . This gene is repressed under high phosphate conditions , where the transcription factor Pho4p is phosphorylated by the Pho80p/Pho85p complex and retained in the cytoplasm . In the un-phosphorylated state under phosphate-free conditions , Pho4p enters the nucleus , where it binds the PHO5 promoter and trans-activates chromatin remodeling , thus being essential for PHO5 induction by promoter nucleosome removal [37] , [38] . To initially determine if topoisomerases are required for PHO5 activation per se or for continued PHO5 transcription upon activation , we took advantage of the fact that deletion of PHO80 leads to constitutive expression of PHO5 regardless of phosphate conditions [39] . Wild-type cells as well as pho80Δ and pho80Δtop1Δtop2ts mutants were analyzed in parallel for accumulation of PHO5 transcripts . As expected , both mutants show high PHO5 transcription levels under high phosphate conditions at the non-restrictive temperature ( 0 min time point ) , where the wild-type cells are fully repressed ( Figure 5A ) . However , upon transfer to inducible conditions at the restrictive temperature , pho80Δtop1Δtop2ts still accumulates PHO5 mRNA at a level comparable to pho80Δ and similar to the transcription level from the fully active PHO5 promoter in wild-type cells . The result demonstrates that topoisomerases have no effect on transcription from an already activated PHO5 promoter , and we therefore conclude that topoisomerases are needed for activation of PHO5 but not for continuous transcription initiation and elongation . To investigate if topoisomerases also play a role during transcriptional inactivation of PHO5 , we performed an experiment , where wild-type and top1Δtop2ts cells were grown under inducible conditions and then transferred to high phosphate to shut down expression . As seen in Figure 5B , the kinetics in the decrease of PHO5 mRNA levels were equivalent in top1Δtop2ts and wild-type cells , strongly indicating that topoisomerases are dispensable during transcriptional repression of PHO5 . Thus , although topoisomerases are essential for PHO5 activation , they do not seem to be required during PHO5 inactivation . To address how DNA topoisomerases affect transcriptional activation of PHO5 , we first compared the accumulation of PHO5 mRNA levels in the topoisomerase single and double mutants ( Figure 6A ) . The analysis shows that , whereas wild-type cells reach full induction after approximately 135 min under phosphate-free conditions , lack of either Top1p or Top2p results in a kinetic delay in PHO5 mRNA accumulation . In contrast , complete lack of topoisomerase activity results in a synthetic phenotype with an absolute inhibition of PHO5 activation . The fact that PHO5 transcription is sensitive to topoisomerase dosage strongly suggests that it is the total relaxation capacity of the cell , which is important for PHO5 transcription . The result thus indicates that PHO5 activation is influenced by changes in DNA superhelicity . To further investigate this we took advantage of the E . coli DNA topoisomerase I enzyme ( TopA ) . This enzyme only relaxes negative supercoiling and has earlier been used to alter DNA superhelicity on a global scale [8] , [9] . As shown in Figure 6B , expression of TopA from a high-copy plasmid in wild-type cells leads to reductions in PHO5 transcript levels upon transfer to inducing conditions , strongly suggesting that PHO5 activation is supercoiling sensitive . To examine the underlying cause of the perturbed PHO5 activation in top1Δtop2ts cells , we next studied the impact of topoisomerase deficiency on regulation of crucial steps upstream of transcription initiation . A previous study has shown an absolute requirement for nucleosome removal in trans from the PHO5 promoter region for transcription initiation to occur , which is dependent on binding of the Pho4p transcription factor to the PHO5 promoter [40] . Figure 7A shows the promoter structure of PHO5 with a nucleosome map , illustrating Pho4p binding sites and four highly positioned nucleosomes covering the promoter region . To investigate if nucleosome removal is affected in top1Δtop2ts we used Chromatin immunoprecipitation ( ChIP ) with an antibody against histone H3 to measure nucleosome occupancy in the PHO5 promoter in wild-type and top1Δtop2ts cells during PHO5 induction . As shown in Figure 7B , a decrease in the relative amount of qPCR products corresponding to the promoter region with the four nucleosomes is seen with increasing time following induction in the wild-type cells , consistent with results from Hörz and coworkers [41] . In contrast , no decrease is seen in top1Δtop2ts . These results suggest that topoisomerases are either required directly for the removal of repressive nucleosomes from the PHO5 promoter or for a step prior to this activity . We reason that this step cannot be de-regulation of a chromatin remodeling factor as strains disrupted for such factors have been shown to merely give rise to a kinetic delay in PHO5 activation and not an absolute inhibition [42]–[45] . Although less likely , we can however not rule out that two or more chromatin remodeling factors are de-regulated and together exert a synthetic phenotype . To eliminate the possibility that defective PHO5 induction in top1Δtop2ts is indirectly caused by disruption of the physiological stimulus leading to transcriptional activation , we analyzed the cellular localization of GFP-tagged Pho4p in wild-type and top1Δtop2ts cells . As expected , both strains exhibit nuclear accumulation of Pho4-GFP after 90 and 180 min under phosphate-free conditions at the restrictive temperature ( Figure 7C ) . The results thus limit the window of topoisomerase requirement to a step between Pho4p nuclear entrance and promoter nucleosome removal . We therefore finally addressed the possibility that binding of Pho4p , which has two binding sites in the PHO5 promoter ( the low affinity UAS1 site and the high affinity UAS2 site ) ( Figure 7A ) is perturbed in top1Δtop2ts . We constructed wild-type and top1Δtop2ts strains with a 13xcMyc-tagged version of Pho4p ( displaying normal PHO5 induction kinetics , Figure S7 ) , and performed ChIP analyses with a cMyc-antibody to monitor Pho4p binding to the PHO5 promoter . Intriguingly , in contrast to the situation in wild-type cells , Pho4-13xcMyc is not enriched in the PHO5 UAS1 and UAS2 regions in top1Δtop2ts after transfer of cells to inducible conditions ( Figure 7D ) . Even after 3 h in phosphate-free medium , where PHO5 is strongly induced in wild-type cells , Pho4p binding levels in the PHO5 promoter in top1Δtop2ts are similar to binding levels in the repressed state . We therefore conclude that topoisomerases are required to allow binding of Pho4p to the PHO5 promoter during transcriptional activation .
Our transcriptome analysis reveals that topoisomerase deficiency leads to a general down-regulation , giving rise to a reduction in mRNA levels of approximately 30% . Interestingly , transcriptional activity but not transcript length is an important cause of the global down-regulation in top1Δtop2ts ( Figure 2 ) . Since transcriptional activity reflects both the rate of elongation and initiation , down-regulation in top1Δtop2ts with increasing transcriptional activity can be explained by an impairment of elongation and/or initiation . However , most impairments of elongation will lead to increasing down-regulation with increasing transcript length , which we do not see . Our results are therefore most simply explained by an impairment of initiation of highly transcribed genes in the absence of topoisomerases as suggested by Roca et al . [9] . In support of this , global ChIP-chip studies of Top1p and Top2p have demonstrated that the enzymes bind intergenic/promoter regions in the yeast genome [14] , [22] , [23] in an activity dependent manner [14] , [22] . How do topoisomerases influence initiation of highly transcribed genes ? The fact that Top1p and Top2p act redundantly during genome-wide transcription ( Figure 1 and Figure 2A ) speaks against a structural or more specific role of either enzyme and rather suggests that they act via their common relaxation activity . Thus , the contribution of either enzyme alone seems sufficient to remove supercoiling to a degree , which maintains high levels of gene transcription in vivo , but when both enzymes are absent , unresolved supercoiling inhibits initiation in an activity dependent manner . The observation that the superhelical strain generated during RNA polymerase tracking does not result in a length dependent requirement for topoisomerases suggests that the RNA polymerase is able to track against a supercoiling gradient . Alternatively , the supercoils may rapidly dissipate into flanking chromosomal regions , where they may be buffered by chromatin structural transitions as suggested earlier [4] , [17] , merge with supercoils of opposite sign , or dissipate out of chromosomal ends by rotation of telomeres [9] . Lack of topoisomerase activity during RNA polymerase tracking may indeed enhance the pressure on alternative pathways for supercoil removal , possibly leading to superhelical changes throughout the chromosomes , including gene promoters , where it eventually may influence transcription initiation . However , we cannot rule out that transcription elongation by each polymerase may be affected by the superhelical strain generated ahead of it as suggested by Ekwall and coworkers based on observations in S . pombe [14] and recent studies from S . cerevisiae , where transcription of long genes was found to be affected exclusively in top2ts cells [46] . Our data suggest that any effect on elongation will have to be gene-length independent in top1Δtop2ts cells ( Figure 2C ) , or a length effect due to topoisomerase deficiency is masked by a much stronger effect from reduced initiation . Our data are consistent with the earlier observed length independency of transcription and intragenic RNA polymerase II binding in budding yeast top1Δtop2ts cells [9] , [22] , [46] . Based on our genome-wide studies a pattern emerges for genes , which are strongly dependent on topoisomerases . This gene group is enriched for genes with high responsiveness to environmental changes , a TATA box in their promoter , SAGA complex dependency , high transcriptional plasticity , sensitivity to chromatin regulation , and specific nucleosome architecture in the promoter ( Figure 3; Figures S3 , S4 , S5; and Table S2 ) . In summary , the analyses point toward a highly regulated mode of transcriptional activation for this group of genes . How can an effect on repressible/inducible genes be unraveled from microarray experiments performed with cells grown exclusively in rich media ( YPD ) ? For the majority of these genes , rich media will neither give complete repression nor complete activation of transcription . Rather , a subpopulation of cells will have a given gene in an active form , while the remaining cells will have the gene in a repressed form . We interpret a drop in transcript levels between wild-type cells and top1Δtop2ts cells in YPD media to reflect an inhibition of activation in these subpopulations due to topoisomerase deficiency . We therefore conclude that genes with high transcriptional plasticity are scored as de-regulated genes in YPD because topoisomerase-deficiency will perturb the periodic activation and/or repression of transcription of these genes . The observed enrichment of stress responsive genes among the up-regulated genes in top1Δtop2ts cells ( Table S1 ) could indicate that lack of topoisomerases leads to a general stress response . This could be the case if central players in the common response to environmental changes like activators or repressors are de-regulated by the lack of the enzymes or indirectly by the slow growth of top1Δtop2ts cells [47] . Arguing strongly against this , is that our Gene Ontology analysis did not reveal any enrichment of transcriptional activators and repressors including those involved in the regulation of stress responsive genes ( Table S1 ) . Pertinent to this discussion , stress responsive genes are primarily found in telomere proximal regions [48] , [49] , but the genes affected in top1Δtop2ts are not biased towards these regions ( Table S2 ) . In further support of a role of topoisomerases for regulated gene transcription we show that the enzymes stimulate transcriptional activation of twelve different inducible genes , including the PHO genes , the GAL genes , ADH2 , and INO1 ( Figure 4 ) . Attention has been drawn to some of these genes in earlier studies of topoisomerases . Thus , in a study of the PHO5 promoter nucleosome positioning it was noticed that PHO5 transcription was inhibited in the absence of topoisomerases [50] . Furthermore , ADH2 has been reported to be regulated by Top1p , which was suggested to repress its transcription by relaxation of negative DNA superhelicity [51] . Our finding that efficient GAL1 activation is dependent on topoisomerases is in contrast to earlier observations with endogenous GAL1 and a plasmid-borne GAL1-lacZ construct [1] , [6] , where high levels of GAL1 transcription was seen in the absence of topoisomerases . However , in a separate study , transcription of GAL1 was found to be strongly inhibited upon expression of E . coli TopA in the absence of yeast topoisomerases [8] in support of a role of these enzymes in GAL1 transcription . We have observed similar topoisomerase dependency in two different yeast strains , indicating that strain background and variable top2ts mutations do not account for the observed inconsistency ( data not shown ) . As we have used G1 arrested cells to exclude replication-associated effects in top1Δtop2ts rather than exponentially growing cells , it remains possible that different supercoiling levels exist in different phases of the cell cycle , which may underlie the discrepancy between ours and earlier studies concerning the dependency of GAL1 transcription on topoisomerases . Use of G1 arrested cells could also affect the studies of PHO5 induction , as arrested cells have been demonstrated to accumulate polyphosphate , a vacuolar Pi reserve , which will influence the rate of PHO5 induction [44] , [45] . It could thus be speculated that this reserve is responsible for the lack of PHO5 induction observed in the top1Δtop2ts cells , as these cells , due to their decreased metabolic activity , would be expected to need more time to consume the Pi reserve relative to wild-type cells . To exclude this possibility we deleted VTC1 and VTC4 in wild-type and top1Δtop2ts , as these genes are the two main genes required for polyphosphate synthesis . As seen in Figure S8 , like in top1Δtop2ts cells , induction of PHO5 still does not take place in vtc1Δtop1Δtop2ts and vtc4Δtop1Δtop2ts cells , confirming that it is the lack of topoisomerases and not excessive Pi reserves , which causes inhibition of PHO5 induction . This was further demonstrated by studying PHO5 induction using exponentially growing cells rather than G1 arrested cells ( Figure S9 ) . It is not yet clear how topoisomerases exert their function during transcription of highly regulated genes . Given that topoisomerase deficiency results in both up- and down-regulations ( with the vast majority of genes being down-regulated , Figure 1 ) , both stimulatory and repressive activities are potentially affected during transcription of these genes in top1Δtop2ts . In line with this , a highly regulated transcription pattern is known to be orchestrated by transactions between specific activators/repressors and their DNA binding sites , as well as by chromatin structure [32] , [33] , [52] . Interestingly , in the case of the PHO5 gene , topoisomerases are required for binding of the Pho4p transcription factor , which is critical for subsequent promoter nucleosome removal and transcriptional induction ( Figure 7 ) . PHO5 thus provides an example , where topoisomerase activity is required for a step upstream of the engagement of polymerases . Since we observe PHO5 induction , although with a kinetic delay , in top1Δ and top2ts single mutants at the restrictive temperature ( Figure 6A ) , as well as in top1Δtop2ts at the permissive temperature ( data not shown ) , we find it unlikely that either one of these enzymes play a more specific role during transcription initiation of PHO5 . Rather , our data suggest that it is lack of their redundant DNA relaxation activity that influences PHO5 transcription . In support of this , we find that E . coli TopA-mediated changes in global supercoiling levels in wild-type cells result in altered transcriptional output from PHO5 ( Figure 6B ) . We reason that indirect effects caused by a potential transcriptional de-regulation of co-factors in the PHO5 induction pathway in top1Δtop2ts is implausible , since Pho4p enters the nucleus ( Figure 7C ) , and only minor expression changes were seen with transcription factors involved in PHO5 transcription ( data not shown ) . Furthermore , we expect most of these effects to result in delayed PHO5 induction kinetics , as seen for the topoisomerase single mutants , rather than a total inhibition [42] , [43] . Taken together , our investigations suggest that DNA topoisomerases are required to maintain the genome in a state competent for transcription initiation . Top1p and Top2p seem to exert this role by a mutually redundant relaxation of DNA supercoils , thus influencing highly transcribed genes and highly regulated , chromatin-dependent genes . Any imbalance in net DNA superhelicity , which likely appears in top1Δtop2ts , may have profound effects on chromatin-regulated promoters . Topoisomerase deficiency may have pleiotropic effects affecting polymerase recruitment [18] , [22] , nucleosome assembly/disassembly equilibrium [14] , [53] or steps upstream to these activities as is the case with PHO5 , where binding of a transcription factor is inhibited . The different scenarios are not mutually exclusive , and DNA topoisomerases most likely function at numerous levels to influence DNA superhelicity for maintenance of transcriptional competency .
All S . cerevisiae strains are derivatives of W303a , and the associated manipulations for obtaining derivate strains are according to standard genetic techniques . For microarray analysis , yeast strains were grown to exponential phase at 25°C in YPD and further grown for 90 min at 25°C in YPD with α-factor ( Lipal Biochem , Zürich , Switzerland ) to synchronize cells in G1 . Cultures were then placed at 37°C for another 90 min for conditional inhibition of Top2p , where more α-factor was added to keep cells in G1 . Cultures were adjusted , so that an equal number of cells could be used for all yeast strains ( 6×107 cells ) . Finally , cells were harvested by centrifugation at 37°C . For each sample , aliquots were collected for fluorescence-activated cell sorting analysis as previously described [54] to ensure successful and persistent cell cycle arrest ( data not shown ) . Three independent sets of experiments were performed to obtain triplicate biological measurements . As the great majority of transcripts in yeast have short decay rates [55] , 90 min of Top2p inactivation was chosen before RNA extraction to ensure turnover of transcripts produced prior to conditional inhibition of Top2p . For analyses of gene-activation in four different inducible gene systems , cells were prepared as for the microarray analysis , except that cells were grown under individual repressive conditions and Top2p was inhibited at 37°C for 15 min prior to transfer of cells to the respective inducible conditions ( see Text S1 for composition of the various growth media ) . For the PHO5 activation experiments , cells were prepared as for the microarray analysis , but instead of YPD they were cultured in high phosphate medium ( yeast nitrogen base w/o phosphate and amino acids from ForMedium , Norfolk , UK ) . Glucose was added to 2% , amino acids were added to standard concentrations , and KH2PO4 was added to a concentration of 15 mM . After cell cycle arrest in G1 and conditional inhibition of Top2p , cells were shifted to phosphate-free medium ( as above , but without addition of KH2PO4 and supplemented with 7 . 35 mM KCl ) for induction of PHO5 . The fold increase observed in PHO5 mRNA levels , when cells are kept in phosphate-free medium for 180 minutes varies from experiment to experiment in the range from 50 fold to 350 fold . For this reason we show PHO5 inductions as percentages of maximum transcript accumulated in the wild-type strain . For transcriptional repression of PHO5 , cells were first cultured in phosphate-free medium at 25°C for 4 h to obtain high PHO5 transcription , where α-factor was added after 2 , 5 h . The temperature was then increased to 37°C to inhibit Top2p , and after 15 min at 37°C , cells were transferred to high phosphate conditions ( 15 mM KH2PO4 ) for PHO5 repression . See Table S3 for a list of strains used in this study . For the microarray experiments total RNA was initially prepared by acid phenol extraction . Immediately prior to RNA extraction , external spike-in Poly-A RNA's ( Affymetrix , Santa Clara , CA ) were added to an equal number of cells from all four yeast strains to enable external normalization . High-quality RNA was obtained by further purification of phenol-extracted RNA on RNeasy columns ( Qiagen , Valencia , CA ) according to manufacturer's directions . RNA quality was assessed by gel electrophoresis and spectrophotometry ( GeneQuant II , Pharmacia Biotech ) . Gene expression profiling was performed using Affymetrix Yeast Genome 2 . 0 GeneChip oligonucleotide arrays essentially according to Affymetrix protocols . Normalization procedure and data processing can be viewed in detail in Text S1 . To estimate global mRNA changes we calculated and compared the total intensity from all detectable probe sets on the mutant and wild-type arrays . For correlation to wild-type transcription levels , all mRNA abundances were averaged across biological triplicates ( microarray signal values ) . Arbitrary transcriptional activities were calculated by dividing average expression levels in the triplicate wild-type arrays by genome-wide mRNA half-life data [55] ( URL: http://www-genome . stanford . edu/turnover/ ) , as described by Schreiber and colleagues [26] . These measures were median-normalized for presentation in Figure 2 . We collected transcript lengths from the transcription map generated by David et al . [27] . Measures of transcriptional plasticity for every gene were obtained from Barkai and co-workers [32] . Measures of sensitivity to chromatin regulation were derived by Choi and Kim [34] ( gathered from URL: http://www . nature . com/ng/journal/v41/n4/suppinfo/ng . 319_S1 . html ) . Analysis of nucleosome occupancy was performed with the use of a recent map of nucleosome positions in S . cerevisiae [35] ( URL: http://chemogenomics . stanford . edu/supplements/03nuc/ ) . The data on nucleosome positions aligned according to transcription start site were used . For analysis of transcription levels , cells were grown as described above , and samples ( ∼108 cells ) were taken at the indicated time points . RNA was purified as for the microarray analysis followed by DNase I treatment , and cDNA was made by SuperScript II RT-PCR ( Invitrogen , Carlsbad , CA ) using oligo dT primer . Real-time PCR was performed with DYNAmo SyBR Green qPCR kit ( Finnzymes , Vantaa , Finland ) and used to quantify mRNA levels , using a Stratagene MX3000 ( Agilent , Santa Clara , CA ) . For each yeast strain , Ct-values from triplicate qPCR amplifications were averaged across three independent measurements . ChIP was performed on 2 . 5×108 cells as described previously [54] with minor modifications . Histone H3 was precipitated with monoclonal antibodies recognizing the C-terminal tail ( ab1791 available from Abcam , Cambridge , UK ) and Pho4-13xcMyc was precipitated using a monoclonal antibody ( Santa Cruz Biotech , Santa Cruz , CA ) . For ChIP of Pho4-13xcMyc , cell extract was incubated with beads coupled with antibody overnight instead of 2 h . For H3 ChIP , fold increase was calculated between antibody-coupled Dynabeads ( IP ) and BSA-coated Dynabeads ( background ) and normalized to the fold increase from an intra-genic sequence in a gene ( YOL151W ) not affected by topoisomerase activity as assessed by qPCR ( data not shown ) , and the 0 min time point was set to 1 . Normalizing to a telomeric locus ( TEL06R ) gave similar results . Pho4-13xcMyc ChIP was calculated in the same way , but using the GAL1/10 promoter region as control region . Primer sequences are listed in Table S4 . Wild-type and top1Δtop2ts cells were treated as for the PHO5 induction experiments , and fluorescence microscopy was performed as described previously [54] . The gene expression data have been deposited in the NCBI Gene Expression Omnibus database with accession number GSE22809 .
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Gene expression is controlled at many different levels to assure appropriate responses to internal and environmental changes . The effect of topological changes in the DNA double helix on gene transcription in vivo is a poorly understood factor in the regulation of eukaryotic gene expression . Topological changes are constantly generated by DNA tracking processes and may influence gene expression if not constantly removed by DNA topoisomerases . For decades it has been generally accepted that these enzymes regulate transcription by removing excess topological strain generated during tracking of the RNA polymerase , but we still lack a more holistic view of how these enzymes influence gene transcription in their native environment . Here , we examine both global and gene-specific changes in transcription following lack of DNA topoisomerases in budding yeast . Taken together , our findings show that topoisomerases play a profound role during transcriptional activation of genes with a repressible/inducible mode of regulation . For the PHO5 gene , which is investigated in more detail , we demonstrate that topoisomerases are required for binding of a transcription factor , which is crucial for promoter opening during PHO5 activation . Our data thus suggest that inducible gene promoters are highly sensitive to changes in DNA superhelicity .
|
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"Results",
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"Methods"
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"model",
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2012
|
DNA Topoisomerases Maintain Promoters in a State Competent for Transcriptional Activation in Saccharomyces cerevisiae
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Human metapneumovirus ( hMPV ) is a leading cause of acute respiratory tract infection in infants , as well as in the elderly and immunocompromised patients . No effective treatment or vaccine for hMPV is currently available . A recombinant hMPV lacking the G protein ( rhMPV-ΔG ) was recently developed as a potential vaccine candidate and shown to be attenuated in the respiratory tract of a rodent model of infection . The mechanism of its attenuation , as well as the role of G protein in modulation of hMPV-induced cellular responses in vitro , as well as in vivo , is currently unknown . In this study , we found that rhMPV-ΔG-infected airway epithelial cells produced higher levels of chemokines and type I interferon ( IFN ) compared to cells infected with rhMPV-WT . Infection of airway epithelial cells with rhMPV-ΔG enhanced activation of transcription factors belonging to the nuclear factor ( NF ) -κB and interferon regulatory factor ( IRF ) families , as revealed by increased nuclear translocation and/or phosphorylation of these transcription factors . Compared to rhMPV-WT , rhMPV-ΔG also increased IRF- and NF-κB-dependent gene transcription , which was reversely inhibited by G protein expression . Since RNA helicases have been shown to play a fundamental role in initiating viral-induced cellular signaling , we investigated whether retinoic induced gene ( RIG ) -I was the target of G protein inhibitory activity . We found that indeed G protein associated with RIG-I and inhibited RIG-I-dependent gene transcription , identifying an important mechanism by which hMPV affects innate immune responses . This is the first study investigating the role of hMPV G protein in cellular signaling and identifies G as an important virulence factor , as it inhibits the production of important immune and antiviral mediators by targeting RIG-I , a major intracellular viral RNA sensor .
Human metapneumovirus ( hMPV ) is a leading cause of both upper and lower respiratory tract infections in infants , elderly and immunocompromised patients worldwide [1] . It is an enveloped , nonsegmented , negative-strand RNA virus , belonging to the Paramyxoviridae family , expressing three putative viral membrane proteins , the fusion protein F , the attachment glycoprotein G and the small hydrophobic protein SH [2] . The role of G protein in viral replication was recently investigated both in vitro and in vivo . Recombinant hMPV ( rhMPV ) in which the G protein was deleted ( rhMPV-ΔG ) exhibited reduced replication in the upper and lower respiratory tract of Syrian hamsters and African green monkeys [3] , [4] . The mechanism ( s ) underlying rhMPV-ΔG attenuation , as well as the role of the hMPV G protein in modulating host cell responses are largely unknown . Sequence analysis of hMPV G protein suggests that it is a type II mucin-like glycosylated protein [2] . The membrane anchor of G protein is proximal to the N termini and its C termini is oriented externally . Although the postulated function of G protein is for attachment , it is not the only attachment protein since hMPV F protein alone is sufficient to mediate attachment and fusion in absence of other surface proteins [3] , 5 . Although the reduced attachment ability of G deleted mutant might be the reason for the observed attenuation of rhMPV-ΔG in vivo , it is also possible that hMPV G protein may have anti-viral function by suppressing the secretion of pro-inflammatory and/or antiviral molecules upon infection , similar to what has been recently shown for the respiratory syncytial virus ( RSV ) G protein . RSV lacking G protein induces more cytokines in viral-infected monocytes and human lung epithelial cells , compared to WT RSV [6] , [7] . Increased CC and CXC chemokine expression by G protein deletion was also observed in a BALB/c mouse model of RSV infection [8] . The mechanism for RSV G protein anti-inflammatory activity is not clear . In this study we investigated whether G protein could modulate host innate immune responses following infection with hMPV . Our results show that G protein indeed plays a role in regulating the signaling pathways leading to the production of pro-inflammatory molecules and type I IFN by affecting activation of NF-κB and IRF , two key transcription factors involved in IFN , cytokine and chemokine gene expression . Two RNA helicases , retinoic inducible gene ( RIG ) -I and melanoma differentiation associated gene ( MDA ) -5 have been identified to be essential for IFN induction by several viruses including NDV , Sendai , HCV and RSV [9]–[11] . We have recently shown that RIG-I plays a major role in hMPV-induced cellular signaling [12] . Both RIG-I and MDA-5 share a helicase domain , that is required for their interaction with viral RNA [9] , [13] , and a CARD domain , that mediates their interaction with the adaptor molecule IPS-1/MAVS/VISA/Cardif , leading to subsequent activation of downstream signaling molecules such as IRFs , NF-κB and AP-1 [14] , [15] . In this study , we found that hMPV G protein interacts with RIG-I and blocks RIG-I- , but not MDA-5- or MAVS-dependent gene transcription , identifying G as an important virulence factor , responsible for inhibiting innate immune responses to hMPV infection .
To investigate the role of hMPV glycoprotein G in hMPV-induced cellular responses , we generated wild-type ( WT ) recombinant hMPV , as well as hMPV lacking G protein ( ΔG ) , using a reverse genetic system approach [3] , [16] . To verify whether G was properly deleted , viral RNA was prepared and subsequently subjected to reverse transcriptase polymerase chain reaction ( RT-PCR ) using paired primers for G or SH gene amplification , as described in Methods . As expected , there was no band corresponding to amplified G in rhMPV-ΔG , while SH gene , used as positive control , was detected from both rhMPV-WT and rhMPV-ΔG ( Fig . 1A ) . We next determined whether viral replication of the recombinant viruses was similar to the parental Canadian hMPV83 isolate . To do so , LLC-MK2 cells were infected with hMPV , naïve or recombinant , at MOI of 0 . 1 . At 5 days p . i , the naïve hMPV and rhMPV-WT were harvested . We found that the titers of naïve hMPV and rhMPV-WT were essentially the same , while viral titers of rhMPV-ΔG were slightly lower , between two and three fold less than naïve hMPV and rhMPV-WT at all time points tested . A similar difference was noted when viruses were grown in Vero cells . To determine whether the initial replication phase of the WT and ΔG mutant viruses in airway epithelial cells , the target cell of hMPV infection , was similar , A549 cells were infected with rhMPV-WT or -ΔG at MOI of 2 and harvested at different time post-infection ( p . i . ) to measure viral titers and determine viral antigen expression . Immunofluorescence analysis using anti-hMPV polyclonal antibody showed that the percentage of cells infected with rhMPV-WT or -ΔG at 15 and 24 h p . i . was similar , around 80–85% ( data not shown ) . However , there was significant less F protein expression in rhMPV-ΔG-infected cells , compared to rhMPV-WT ( Fig . 1B ) , and viral titers at 6 , 15 and 24 h p . i . were lower ( one third to half log less ) in A549 cells infected with rhMPV-ΔG compared to WT virus , indicating a role of G protein in hMPV replication in airway epithelial cells . It was recently shown that rhMPV-ΔG is highly attenuated in the lower and upper respiratory tract of animal models of infection , compared to rhMPV-WT [3] , [4] . Type I interferons play a major role in limiting viral replication [reviewed by [17]] . To determine whether the restriction of rhMPV-ΔG replication in vivo could be due to increased IFN-α/β production , airway epithelial cells were infected with either rhMPV-ΔG or rhMPV-WT and cell supernatants were harvested at various time p . i to measure both IFN-α and β by ELISA . As show in Fig . 2 , infection of A549 cells with rhMPV-ΔG resulted in a 4-fold and 7-fold increase in IFN-α secretion at 15 h and 24 h p . i respectively , compared to rhMPV-WT . Similarly , IFN-β secretion was 13-fold and 20-fold higher in cells infected with rhMPV-ΔG at 15 h and 24 h p . i . compared to cells infected with rhMPV-WT . To determine whether G protein deletion had a broader effect on hMPV-induced secretion of pro-inflammatory and immunoregulatory molecules , we compared the secretion pattern of chemokines and cytokines in A549 cells infected with either rhMPV-WT or rhMPV-ΔG , using a combination of ELISA and Bio-Plex assays ( Fig . 3 ) . rhMPV-ΔG induced significantly higher amounts of the cytokine IL-6 , the CXC chemokines IL-8 and IP-10 , and CC chemokines MCP-1 , MIP-1α and RANTES at both 15 and 24 h p . i , compared to hMPV-WT . A significant difference in IL-8 and MIP-1α induction between rhMPV-WT- and rhMPV- ΔG-infected cells was noted as early as 6 h p . i . Transcription factors of the interferon regulatory factor ( IRF ) family have been shown to play an essential role in viral-induced expression of type I IFN genes ( reviewed in [18] ) . They also regulate the induction of several other genes involved in the immune/inflammatory response to viral infections , including chemokines , such as RANTES and IP-10 , and cytokines , such as IL-15 [Reviewed in [18]] . Among the different members of the IRF family , IRF-1 , -3 , -5 and -7 have been identified as direct transducers of viral-induced signaling , with IRF-3 being necessary for IFN-β and RANTES gene expression in response to paramyxovirus infections [19] . To investigate the role of G protein in hMPV-induced type I interferon expression and IRF protein activation , we initially determined the effect of G protein deletion on IFN-β gene transcription using transient transfection assays . A549 cells were transfected with a reporter plasmid containing the luciferase gene under control of the IFN-β promoter ( IFN-β-LUC ) [11] , and either mock infected or infected with rhMPV-WT or -ΔG . Cells were harvested at 15 h p . i . to measure luciferase activity . As shown in Fig . 4A , luciferase activity was significantly higher ( 3 fold ) in A549 cells infected for 15 h with rhMPV-ΔG , compared to rhMPV-WT . Enhanced activation of the IFN-β promoter in cells infected with rhMPV-ΔG was also observed at 24 h p . i . ( data not shown ) . To confirm the inhibitory role of G in the induction of IFN-β , we infected A549 cells with rhMPV-ΔG in presence of plasmid expressing either G or F protein . As shown in Fig . 4A , expression of hMPV G but not F protein reversibly inhibited the enhanced IFN-β gene transcription in response to rhMPV-ΔG infection . Similarly , G protein expression significantly decreased IFN-β gene transcription induced by RSV , another paramyxovirus ( Fig . S1 ) . To determine whether G protein deletion specifically affected hMPV-induced IRF-dependent gene transcription , A549 cells were transiently transfected with a construct containing multiple copies of the RANTES ISRE site linked to the luciferase reporter gene [20] and infected with rhMPV-WT or rhMPV-ΔG . Infection of rhMPV-ΔG resulted in significantly higher luciferase activity , compared to rhMPV-WT , which was significantly inhibited by G but not F protein expression ( Fig . 4B ) . IRF-3 activation is necessary for IFN-β and RANTES gene expression in response to paramyxovirus infections [21] , [22] . IRF-3 is constitutively expressed and in unstimulated cells appears in two forms , when resolved by SDS-PAGE , defined as forms I and II [23] . Following viral infections , IRF-3 undergoes a shift in molecular weight and migrates as forms III and IV , due to the virus-induced C-terminal phosphorylation [23] , which occurs on specific serine residues and is necessary for IRF nuclear translocation , dimerization , binding to DNA and activation of transcription ( Reviewed in [24] ) . To determine whether changes in abundance of IRF-dependent gene expression , as well as the increased IRF-driven gene transcription observed in response to rhMPV- ΔG infection , resulted from enhanced IRF-3 protein activation , we investigated IRF serine phosphorylation and nuclear translocation by Western blot analysis , using nuclear extracts from control and rhMPV-infected A549 cells ( Fig . 4C ) . IRF-3 phosphorylation occurred earlier and was significantly higher in rhMPV-ΔG infected cells at all time points of infection , compared to rhMPV-WT , which induced IRF-3 phosphorylation only at 15 and 24 h p . i . ( Fig . 4C ) . The enhanced IRF-3 phosphorylation was paralleled by a significant increase in nuclear translocation , also observed at all time points of rhMPV-ΔG infection , compared to rhMPV-WT ( Fig . 4C ) . Enhanced activation was also demonstrated by the shift in molecular weight and slower gel migration of IRF-3 in cells infected with rhMPV-ΔG at 3 , 6 , 15 and 24 h p . i . , compared to rhMPV-WT , in which IRF-3 nuclear translocation and molecular shift occurred only at 15 and 24 h p . i . NF-κB is a superfamily of ubiquitous transcription factors composed of NF-kB1 or p50 , NF-kB2 or p52 , Rel A or p65 , RelB and c-Rel proteins , which can form homo- and hetero-dimers and produce complexes with various transcriptional activities [25] . Their activation is controlled by accessory inhibitory proteins called IκBs [26] . NF-κB inducing stimuli cause IκB phosphorylation , through activation of the multicomponent IκB kinase ( IKK ) complex [25] , with subsequent IκB proteolytic degradation [27] , event that allows NF-κB to enter the nucleus and activate target gene transcription . A number of paramyxovirus-inducible inflammatory and immunoregulatory genes require NF-κB for their transcription , as we have shown in vitro for IL-8 [28] , RANTES [22] , as well as other chemokines , cytokine , secreted proteins and signaling molecules [29] . To determine whether the observed increased in IL-8 production in response to rhMPV-ΔG infection was due to enhanced IL-8 gene transcription , A549 cells were transfected with a construct containing the human IL-8 promoter linked to the luciferase reporter gene [30] and infected with either rhMPV-WT or rhMPV-ΔG . Consistent with IL-8 secretion , rhMPV-ΔG-induced IL-8 promoter activation occurred earlier and was significantly higher ( ∼3 and 1 . 8 fold increase at 6 and 15 h p . i . ) than rhMPV-WT ( Fig . 5A ) . To investigate the role of NF-κB in rhMPV-ΔG-induced enhanced IL-8 gene transcription , 293 cells were transiently transfected with a construct containing multiple copies of the IL-8 NF-κB site linked to the luciferase reporter gene [30] and infected with either rhMPV-WT or -ΔG . Compared to rhMPV-WT , infection with rhMPV-ΔG resulted in significant higher NF-κB-driven gene transcription at 6 and 15 h p . i . , with the most significant difference occurring at 15 h p . i . , which was inhibited by expression of G but not F protein ( Fig . 5B ) . We have previously show that p65 and p50 are the two major NF-κB family members induced by paramyxovirus infection of airway epithelial cells [28] , with p65 being necessary for a variety of viral-induced chemokines and cytokine gene expression [29] . To determine whether G protein plays a role in modulating hMPV-induced NF-κB activation , we investigated p65 and p50 nuclear translocation in A549 cells infected with either rhMPV-WT or -ΔG . Both viruses induced significant p65 and p50 nuclear translocation as early as 3h p . i . , however the nuclear abundance of both NF-κB subunits was significantly higher in rhMPV-ΔG infected cells , compared to rhMPV-WT ( Fig . 5C ) . In conclusion , G protein deletion enhanced hMPV-induced expression of pro-inflammatory and immunoregulatory mediators , likely through enhanced activation of transcription factors belonging to the NF-κB and IRF families . The RIG-I/MDA-5/MAVS pathway plays an essential role for initiating cellular signals leading to the activation of transcription factors and subsequent induction of type I IFN in the course of viral infections [13] , [31] , [32] . In recent investigations , we found this pathway is necessary for hMPV-induced gene expression in airway epithelial cells [12] . Therefore , we investigated whether the G protein inhibitory effect on NF-κB and IRF activation and subsequent induction of pro-inflammatory and anti-viral molecules occurred via inhibiting RNA helicase-initiated cellular signaling . To do so , A549 cells were transfected with Flag-tagged RIG-I , MDA-5 or MAVS expression plasmids with a plasmid containing the luciferase reporter gene under the control of the IFN-β promoter ( IFN-β-Luc ) . Individual expression of all three signaling molecules significantly induced IFN-β promoter activation , in the absence of viral infection ( Fig . 6A ) . RIG-I-dependent IFN-β promoter activation was inhibited by G protein expression , while there was no effect on MDA-5- or MAVS-induced luciferase activity ( Fig . 6A ) . Next , we investigated whether G protein was able to directly interact with RIG-I , disrupting its ability to mediate cellular signaling , as it was recently shown for influenza virus NS1 protein [33]–[35] . 293 cells were transfected with V5-tagged G and Flag-tagged RIG-I expression plasmids . Vectors expressing V5 or Flag only were used as negative controls . After 30 h of transfection , cells were lysed followed by immunoprecipitation using anti-V5 antibody ( Fig . 6B ) . The immunoprecipitated complex was separated on SDS-PAGE and transferred onto PVDF membrane . Western blot using anti-Flag antibody revealed that RIG-I coprecipitated with G protein . Reverse immunoprecipitation , using anti-Flag to precipitate expressed RIG-I and then using anti-V5 antibody for Western blot also confirmed that G was present in the immuno-precipitated complex ( Fig . 6B ) . A similar experiment performed using a V5-tagged F expression plasmid , instead of G , and Flag-tagged RIG-I did not show any interaction between the two proteins ( Fig . S2 ) . To confirm that G interacts with RIG-I in conditions of naïve protein expression , A549 cells were mock infected or infected with rhMPV-WT or rhMPV-ΔG . For each condition , half sample was coimmunoprecipitated using anti-RIG-I antibody , while the other half was exposed to an isotype antibody to rule out nonspecific protein binding during the immunoprecipitation . Two specific bands around 90 and 50 kD were detected in rhMPV-WT-infected samples , but not in the mock infected or rhMPV-ΔG samples , using an anti-hMPV antiserum , likely corresponding to the two major glycosylated forms of G protein ( Fig . 6C ) , as described in [3] . The same size bands were detected by the anti-hMPV antiserum when G was expressed in 293 cells , while the F protein , used as a control , run with a different molecular weight ( Fig . S3 ) .
The innate immune response represents a critical component of the host defense against viruses and is coordinated at the cellular level by activation of transcription factors that regulate the expression of inducible gene products with antiviral and/or inflammatory activity . As the immune system evolved to fight viral infections , so viruses developed strategies to evade the host immune responses , mainly by targeting the type I interferon system . HMPV is the second most common cause of epidemic respiratory infections in infants and young children and a significant cause of respiratory tract infections in the elderly and immunocompromised patients . The availability of the reverse genetic system for negative sense RNA viruses has allowed the dissection of viral protein functions in viral replication as well as in cellular signaling . As a recently identified virus , little is known about the role of individual hMPV proteins in modulating host cell responses . In this study , we found that hMPV infection of airway epithelial cells , the primary target of hMPV infection [36] , [37] , induced the secretion of a variety of cytokines and chemokines , as well as type I interferons , whose expression is coordinated by subsets of transcription factors belonging to the NF-κB and IRF families . Surprisingly , deletion of G protein resulted in enhanced production of chemokines and type I interferon ( IFN ) , as well as increased activation of both families of transcription factors . The enhanced responses to rhMPV-ΔG infection were not due to an increased ability of rhMPV-ΔG to replicate , as the accumulation of F protein in infected airway epithelial cells and viral titers were lower in cells infected with rhMPV-ΔG , compared to rhMPV-WT . Circumvention of the IFN response occurs through different strategies . Two major categories include either direct suppression of IFN production or interference with IFN signaling , through inhibition of the JAK/STAT pathway . The kinetics of enhanced chemokine and IFN production and transcription factor activation in response to rhMPV-ΔG infection , which occurred at early time points of infection , suggested that G protein regulated an early signaling event triggered in response to hMPV infection of airway epithelial cells . RIG-I and MDA-5 are two RNA helicases that have been recently identified as fundamental sensors of viral infections [10] , [38] . They recognize single and double-stranded RNA and their engagement triggers a signaling cascade leading to NF-κB and IRF activation , through interaction with the mitochondrial antiviral signaling ( MAVS ) adaptor molecule [39] . Several viral proteins have been shown to be able to disrupt the RIG-I/MDA-5/MAVS signaling pathway by sequestering viral RNA from helicase binding and/or disrupting helicase interaction with downstream signaling molecules or by increasing protein degradation . Influenza A NS1 protein employs the first two mechanisms to block RIG-I-mediated IFN induction [40] . Similarly , V proteins of paramyxoviruses have been shown to bind MDA-5 and inhibit IFN-β production [13] [41] . On the other hand , poliovirus infection induces MDA-5 degradation , also inhibiting IFN induction [42] , [43] . HCV 3/4A proteases cleave MAVS at the level of mitochondria insertion , releasing the protein to the cytoplasmic compartment , therefore preventing further transmission of RIG-I-dependent signaling , resulting in the inhibition of host's antiviral responses [39] . In this study , we found that hMPV G protein physically interacts with RIG-I and inhibits RIG-I , but not MDA-5 and MAVS-induced IFN-β transcription , justifying the broad inhibitory effect of G protein on activation of NF-κB and IRF transcription factors and induction of antiviral and pro-inflammatory molecules . The inhibitory effect of hMPV G protein on cellular signaling could be a common feature of surface glycoproteins of enveloped single strand , negative strand RNA viruses . RSV G protein has been shown to modulate cytokine and chemokine production , as infection with a mutant RSV lacking the full-length G protein or the soluble part of G protein ( sG ) enhanced production of IL-6 and IL-8 in monocytes [6] , as well as IL-8 and RANTES secretion and ICAM expression in airway epithelial cells [7] . RSV-ΔG also caused more lung inflammation , compared to WT , in a mouse model of infection [6] . The G protein of pneumonia virus of mice , a murine relative of RSV , has also been recently identified as an important virulence factor , as viral replication of a recombinant mutant virus lacking G is severely restricted in a BALB/c mouse model of infection [44] . Similarly , the surface glycoproteins of hantaviruses , in particular the ones associated with hemorrhagic pulmonary syndrome ( HPS ) , have been shown to affect IRF-3 activation and IFN production , via interaction with RIG-I and TBK-1 , a kinase responsible for viral-induced IRF-3 phosphorylation , as well as to inhibit IFN-mediated cellular responses [45] , [46] . hPMV infection is associated with production of inflammatory mediators not only in vitro but also in vivo [47] . The high attenuation of rhMPV-ΔG replication in the lower respiratory tracts of rodent models of infection suggest that it could be developed as a vaccine candidate [3] [4] . Our findings suggest that careful investigation is needed in an animal model of infection to address whether rhMPV-ΔG can cause enhance lung inflammation and impaired lung function , as recently shown for RSV lacking the G protein [6] , [48] . In summary , this study provides us with novel information on the role of hMPV G protein in regulating host cell responses . Further studies are needed to determine the exact mechanism by which G protein inhibits RIG-I activation and to define the domains/amino acid residues mediating RIG-I and G interaction .
LLC-MK2 cells ( ATCC , Manassas , VA ) were maintained in MEM ( Invitrogen GIBCO , Carlsbad , CA ) supplemented with 10% fetal bovine serum and Penicillin and Streptomycin ( 100 U/ml ) . The Canadian isolate hMPV83 and its derived recombinant viruses were propagated in LLC-MK2 cells at 35°C in the absence of serum and in the presence of 1 µg of trypsin/ml ( Worthington , Lakewood , NJ ) , and were sucrose purified , as previously described [47] . Viral titer was determined by immunostaining in LLC-MK2 cells , as previously described [49] . A549 , human alveolar type II-like epithelial cells , and 293 , a human embryonic kidney epithelial cell line ( both from ATCC , Manassas , VA ) , were maintained in F12K and MEM medium respectively , containing 10% ( v/v ) FBS , 10 mM glutamine , 100 IU/ml penicillin and 100 µg/ml streptomycin . Cell monolayers were infected with hMPV at multiplicity of infection ( MOI ) of 2 in serum-free medium containing antibiotics and 1 µg of trypsin/ml for all experiment , unless otherwise stated . An equivalent amount of sucrose solution was added to uninfected A549 cells , as control . BSR T7/5 cells , baby hamster kidney cells that constitutively expressing the T7 RNA polymerase , were a gift from Dr . Conzelmann , Munich , Germany . They were maintained in Glasgow minimal essential medium ( MEM ) supplemented with 1% amino acids , 10% fetal bovine serum , 12 mg/L tryptose phosphate broth , 1 mg/ml of Geneticin , and 100 U/ml of Penicillin and Streptomycin . A plasmid encoding hMPV antigenome was constructed as described in Biacchesi et al . [16] , with some modifications . Briefly , viral RNA was purified from hMPV CAN83-infected LLC-MK2 cells using QIAamp viral RNA kit ( Qiagen , Alameda , CA ) . First strand cDNA was then generated using Superscript III reverse transcriptase ( Invitrogen Carlsbad , CA ) . PCR was carried out using Pfu DNA polymerase ( Stratagene , La Jolla , CA ) following manufacturer's instruction . The complete hMPV antigenome cloned in pBSKSII vector ( a gift from Dr . Buchholz , NIAID , Bethesda ) was obtained by sequential ligation of three fragments amplified from the cDNA template . Fragment I was first cloned using forward primer: 5′-ACGCGAAAAAAACGCGTATAAATTAAGTTAC-3′ and reverse primer: 5′- TTTGTCCCGTTCTTGATTgctAgCATTCTTATTCTAACTTg-3′ . The cloned fragment which contains the putative N , P , and M genes was first cloned into TOPO cloning vector ( Invitrogen , Carlsbad , CA ) . On the downstream end of this fragment , an NheI site was created by four nucleotide substitutions in the putative M–F intergenic region as a marker to distinguish between cDNA-derived and biologically derived hMPV . In order to insert a T7 RNA polymerase promoter ( T7p ) at the 3′ end of the antigenome , fragment I inserted TOPO was used as template to clone the T7p-fragment I using same reverse primer and forward primer: 5′-cgcgacgtcTAATACGACTCACTATAGGGACGCGAAAAAAACGCGTATAAATTAAGTTAC-3′ . Italicized letters indicate restriction enzyme site . Underlined letters indicate T7 RNA polymerase promoter ( T7p ) sequence . Construction of fragment II , which contains F , M2 , SH and G ) and fragment III , containing L and part of the hepatitis delta virus ribozyme , was done as described in Biacchesi et al . [16] . Construction of G deleted mutant cDNA was done as described in Biacchesi et al . [3] . hMPV cDNA was also used as a template to clone the support genes necessary for the virus recovery , as described in Biacchesi et al . [16] . Amplified PCR fragments , encoding N , P and M2-1 , were first inserted into TOPO cloning vector ( Invitrogen , Carlsbad , CA ) , and then restricted with AflIII/XhoI . PCR fragments were then subcloned into NCoI/XhoI digested PTM1 , a T7 promoter containing vector ( a generous gift from Dr . Moss at NIH ) . For the N protein , site-directed mutagenesis was performed to silently mutate an AflIII site before cloning the gene into PTM1 . To clone the polymerase L , full length L cloned in TOPO vector was cut first with BamHI and blunt ended using T4 DNA polymerase ( Invitrogen , Carlsbad , CA ) following manufacturer's instruction . PTM1 was also cut first with NcoI and then blunted using T4 DNA polymerase as well . Both vectors were then cut with XhoI and the L fragment was cloned into the blunted NcoI and cohesive XhoI site of PTM1 . The plasmids containing the full-length hMPV-WT or -ΔG antigenome or individual support genes were sequenced to verify the absence of significant mutations . Primer sequence for cloning the recombinant viruses , as well as the support viral proteins , are available upon request . Confluent BSR T7/5 cells in six-well dishes were transfected with 5 µg of antigenomic plasmid corresponding to hMPV wild-type ( rhMPV-WT ) , or one lacking G ( rhMPV-ΔG ) gene , together with 1–2 µg of PTM1 expressing P and N proteins , and 0 . 5–1 µg of PTM1 expressing M2-1 and L proteins . Lipofectamine 2000 ( Invitrogen ) was used as transfection reagent , according to manufacturer's instructions . After overnight incubation at 35°C , transfection medium was removed and replaced with Glasgow MEM without trypsin or serum . Trypsin was added on day 3 post-transfection to a final concentration of 1 µg/ml , then cell-medium mixtures were passaged onto fresh LLC-MK2 cells and incubated at 35°C for the next few days . Typical viral CPE were usually observed around day 5–6 post-infection ( p . i . ) . Recombinant virus generation was confirmed by restriction digestion and the sequencing of viral RNA , as described in Biacchesi et al . [16] . The recovered viruses were then amplified for two passages in LLC-MK2 cells and saved as stock viral preparations . Viruses with no more than 4–5 passages were used in all experiments . G protein was cloned using the plasmid encoding hMPV antigenome as a template . PCR was carried out using Pfu DNA polymerase ( Stratagene , La Jolla , CA ) following manufacturer's instruction . The primer sequences for V5-tagged G were: forward: 5′- ACGCgaattcATGGAGGTGAAAGTAGAGAA-3′ , and reverse: 5′- TctcgagTCACGTAGAATCGAGACCGAGGAGAGGGTTAGGGATAGGCTTACCGTTTTGCATTGTGCTTACAGATGCCTG-3′ . Italicized letters indicate restriction enzyme site . Underlined letters indicate V5 sequence . The cloned V5-tagged G was first cloned into the TOPO cloning vector , and then cut by EcoRI and XhoI and subcloned into pCAGGS vector . Sequence integrity was verified by sequencing performed by the protein chemistry laboratory at UTMB . Plasmids containing either the IFN-β promoter , as well as plasmids containing multiple copies of the IL-8 NF-κB site or of the RANTES ISRE site , linked to the luciferase reporter gene , have been previously described [11] , [20] , [22] , [30] . Logarithmically growing A549 or 293 cells were transfected in triplicate with the reporter plasmids using FuGene 6 ( Roche , Indianapolis , IN ) , as previously described [22] . The next day cells were infected with recombinant hMPV , either WT or ΔG , at MOI of 2 . Uninfected plates served as control . At various times post-infection , cells were lysed to measure independently luciferase and β-galactosidase reporter activity , as previously described [50] . Luciferase was normalized to the internal control β-galactosidase activity . All experiments were performed in duplicate or triplicate . In the experiments , in which the role of overexpressed G in modulating viral-induced response was investigated , a plasmid encoding G or its control vector was cotransfected with these luciferase reporter genes . 293 cells with 60–70% confluence in 6-well plate were cotransfected with 0 . 5 µg of pcDNA6 containing Flag-tagged RIG-I and 0 . 1 µg of either pCAGGS encoding V5-tagged G protein or the empty vector , used as a negative control . Cells were harvested 30 h after transfection and immunoprecipitation was carried out using immunoprecipitation Kit from Roche ( Cat # 11719 , Indianapolis , IN ) . In brief , 6×106 cells were lysed using 1 . 5 ml of lysis buffer . To reduce background caused by non-specific adsorption of irrelevant cellular proteins to protein A/G-agarose , a preclearing step was performed by incubating the sample with 50 µl of the protein A/G-agarose for 3 h at 4°C on a rocking platform . Precleared samples were then exposed to 5 µg of antibody against either V5 ( Invitrogen , Carlsbad , CA ) or Flag ( Sigma , St . Louis , MO ) or to an isotype antibody control , for 1 h at 4°C . 50 µl of the protein A/G-agarose were added to the samples and incubated overnight at 4°C . Immunecomplexes were recovered by centrifugation and washed three times using buffers with different ion strength provided by the kit . The immunoprecipitated complexes were eluted from the beads and subjected to SDS-PAGE and Western blot analysis . To investigate the interaction of endogenous RIG-I with G protein during hMPV infection , confluent A549 cells in 6-well plate were mock infected or infected with rhMPV-WT or -ΔG , at MOI of 2 for 24 h . Cells were lysed and immunoprecipitated using anti-RIG-I antibody ( Abgent , San Diego , CA , cat . #AP1900a ) or isotype control antibody , as described above . The presence of G protein in the complex was then detected using polyclonal anti-hMPV antibody , a gift from Medimmune , Mountain View , CA . Nuclear extracts of uninfected and infected cells were prepared using hypotonic/nonionic detergent lysis , according to Schaffner protocol [51] . To prevent contamination with cytoplasmic proteins , isolated nuclei were purified by centrifugation through 1 . 7 M sucrose buffer A for 30 min , at 12 , 000 rpm , before nuclear protein extraction , as previously described [52] . Total cell lysates of uninfected and infected cells were prepared by adding ice-cold lysis buffer ( 50 mM Tric-HCl , pH 7 . 4 , 150 mM NaCl , 1 mM EGTA , 0 . 25% sodium deoxycholate , 1 mM Na3VO4 , 1 mM NaF , 1% Triton X-100 and 1 µg/ml of aprotinin , leupeptin and pepstatin ) . After incubation on ice for 10 min , the lysates were collected and detergent insoluble materials were removed by centrifugation at 4°C at 14 , 000 g . After normalizing for protein content , using Bio-Rad , Hercules , CA . Nuclear extracts or total cell lysates were fractionated by SDS-PAGE , and transferred to polyvinylidene difluoride membranes . Membranes were blocked with 5% milk in TBS-Tween and incubated with the proper primary antibodies according to manufacturer's instruction . Primary antibodies used for IRF-3 and phosphorylated IRF-3 detection was from Santa Cruz ( Santa Cruz Biotechnology , Inc . , Santa Cruz , CA ) and Upstate ( Upstate , Lake Placid , NY ) respectively . Rabbit anti-p65 and p50 were purchase from Cell Signaling ( Cell Signaling Technology , Inc , Danvers , MA ) . Monoclonal anti-hMPV F was a gift from Medimmune , Mountain View , CA . Proper horseradish-coupled secondary antibody was then used and proteins detected by enhanced chemiluminescence assay ( Amersham , Piscataway , NJ ) . IFN-α and -β concentrations were determined by commercial enzyme-linked immunosorbent assays ( ELISA ) , according to the manufacturer's instructions ( PBL , Piscataway , NJ ) . Chemokines and cytokines ( IL-1RA , IL-1β , IL-2 , IL-4 , IL-5 , IL-6 , IL-7 , IL-9 , IL-10 , IL-12 p70 , IL-13 , IL-17 , G-CSF , GM-CSF , IFN-γ , IP-10 , EOTAXIN , MIP-1α , MIP-1β , GCSF , FGFB , PDGF , VEGF and TNF-α ) were quantified by Luminex-based Bio-Plex system ( Bio-Rad Laboratories , Hercules , CA ) according to the manufacturer's instructions . The lower limit of detection for all cytokines measured by Bio-Plex is 3 pg/ml . Statistical significance was analyzed by using analysis of variance ( ANOVA ) . P value of less than 0 . 05 was considered significant . Results are shown as mean±SEM .
|
Human metapneumovirus ( hMPV ) , a member of the Paramyxoviridae family , is an important cause of respiratory morbidity throughout life . The contribution of viral-specific proteins to the pathogenesis of hMPV infection and immune evasion is largely unknown . Previous work has suggested that the glycoprotein G of hMPV is not necessary for the process of viral fusion and attachment to host cells , and a recombinant hMPV lacking the G protein ( rhMPV-ΔG ) shows an attenuated phenotype in the respiratory tract of animal models of infection . Airway epithelial cells , a major component of the innate immune system , are a primary target of hMPV infection . In this study , we show that hMPV G protein functions as a major inhibitory factor of the host antiviral response by blocking production of inducible chemokines and IFN-α/β . A major finding of this work is the demonstration that hMPV G protein interacts with RIG-I , a cytoplasmic viral sensor . As result , hMPV G protein inhibits RIG-I-dependent signaling pathways , including activation of NF-κB and IRF-3 , two transcription factors necessary for the synthesis of inflammatory and antiviral cytokines . Understanding the function of hMPV proteins is critical for the future design of effective antiviral therapies and rationale design of vaccine candidates .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"virology/vaccines",
"infectious",
"diseases/respiratory",
"infections",
"immunology/innate",
"immunity",
"cell",
"biology/cell",
"signaling"
] |
2008
|
Human Metapneumovirus Glycoprotein G Inhibits Innate Immune Responses
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The CDC recommends that healthcare settings provide influenza patients with facemasks as a means of reducing transmission to staff and other patients , and a recent report suggested that surgical masks can capture influenza virus in large droplet spray . However , there is minimal data on influenza virus aerosol shedding , the infectiousness of exhaled aerosols , and none on the impact of facemasks on viral aerosol shedding from patients with seasonal influenza . We collected samples of exhaled particles ( one with and one without a facemask ) in two size fractions ( “coarse”>5 µm , “fine”≤5 µm ) from 37 volunteers within 5 days of seasonal influenza onset , measured viral copy number using quantitative RT-PCR , and tested the fine-particle fraction for culturable virus . Fine particles contained 8 . 8 ( 95% CI 4 . 1 to 19 ) fold more viral copies than did coarse particles . Surgical masks reduced viral copy numbers in the fine fraction by 2 . 8 fold ( 95% CI 1 . 5 to 5 . 2 ) and in the coarse fraction by 25 fold ( 95% CI 3 . 5 to 180 ) . Overall , masks produced a 3 . 4 fold ( 95% CI 1 . 8 to 6 . 3 ) reduction in viral aerosol shedding . Correlations between nasopharyngeal swab and the aerosol fraction copy numbers were weak ( r = 0 . 17 , coarse; r = 0 . 29 , fine fraction ) . Copy numbers in exhaled breath declined rapidly with day after onset of illness . Two subjects with the highest copy numbers gave culture positive fine particle samples . Surgical masks worn by patients reduce aerosols shedding of virus . The abundance of viral copies in fine particle aerosols and evidence for their infectiousness suggests an important role in seasonal influenza transmission . Monitoring exhaled virus aerosols will be important for validation of experimental transmission studies in humans .
Transmission of influenza virus between humans may occur by three routes: ( 1 ) direct or indirect contact between an infected and a susceptible person , usually resulting in contamination of a susceptible person's hands followed by hand to respiratory mucosa contact; ( 2 ) large droplet spray wherein droplets of respiratory fluid greater than approximately 100 µm in diameter are expelled with sufficient momentum to deliver a direct hit on the respiratory mucosa; and ( 3 ) aerosols generated by release of smaller , virus-containing droplets , as may occur during tidal breathing and coughing [1] , [2] , that rapidly evaporate into residual particles ( droplet nuclei ) , which are inhaled and deposited in the respiratory tract [3]–[6] . There is significant evidence for each of these routes [7] , [8] , but their relative importance is not known [3] . As a result , the Institute of Medicine recommended that healthcare workers in contact with 2009-H1N1 patients use protection against all of the possible routes of infection , including use of fit-tested N95 respirators [3] . A year after the 2009 pandemic , there was no greater clarity on the importance of the various modes of transmission [9] . The U . S . Centers for Disease Control and Prevention recently funded an experimental study of person-to-person transmission to address this important knowledge gap [10] . However , an experimental study using intranasal inoculation to infect experimental donors [11] will need to show that the donors and naturally infected persons shed similar virus aerosols with regard to quantity , particle size distribution , and infectiousness , given that earlier experiments suggested that intranasal inoculation requires quantitatively larger doses and produces qualitatively milder illness than does inoculation via aerosol [12] . In an occupational hygiene context , personal protection is usually the last resort , after source mitigation and environmental controls are exhausted [13] . Thus , it is worthwhile considering whether surgical facemasks could be effective as a means of source control . The CDC recommends that persons with influenza wear surgical masks when in contact with susceptible individuals [14] , [15] . However , there is only one report studying mask impact on containment of infectious large droplet spray during influenza infection [16] , and no data on surgical mask impact on release of infectious viral aerosols . In the current study of patients infected with seasonal influenza , we describe the number of copies of viral RNA in two aerosol size fractions , report the culturability of virus in the fine-particle fraction , and the effect of surgical masks .
We screened 89 volunteers: 33 ( 37% ) tested positive for influenza using the rapid test ( 20 influenza A and 13 influenza B ) and were asked to provide exhaled breath samples . Eight additional volunteers with negative rapid tests who reported a cough and who had a temperature of ≥37 . 8°C were also invited to participate . In total , 38 volunteers were confirmed to have influenza virus infection by PCR of nasopharyngeal specimens . Exhaled breath data with and without a surgical mask are complete for 37 of the 38 volunteers ( 21 influenza A , 16 influenza B ) ; data for one volunteer has been excluded due to laboratory error in sample processing . One of the infected subjects reported receiving influenza vaccine for the current year . None of the subjects sneezed during the sample collection . Table 1 shows the sex , symptom and fever prevalence , and influenza virus type and Table 2 shows descriptive statistics for age and viral RNA copy number in swabs and exhaled aerosol fractions of the 37 volunteers with confirmed influenza infection . The viral copy numbers in each of the five specimens for all 37 cases are shown in Table S1 . We detected influenza virus RNA in the coarse fraction ( particles greater than 5 µm ) collected from 11% ( 4 of 37 volunteers ) while wearing surgical masks and from 43% ( 16 of 37 ) while not wearing a mask ( relative risk for virus detection with mask = 0 . 25 , 95% confidence interval ( CI ) 0 . 09 to 0 . 67; McNemar's test p = 0 . 003 ) . The median number of coarse fraction viral copies ( Figure 1 ) was below the limit of detection with and without facemasks; the 75th percentile dropped from 37 to below the limit of detection with use of surgical masks . Using Tobit analysis , we estimated that the geometric mean coarse fraction copy number without a facemask was 12 ( 95% confidence interval ( CI ) , 4 to 37 ) and that the effect of facemasks was to produce a statistically significant 25 fold reduction in the copy number ( 95% CI 3 . 5 to 180 , p = 0 . 002 ) to <0 . 5 copies per 30 min sample . We detected viral RNA in 78% ( 29 of 37 ) of fine particle samples collected from volunteers when they were wearing a mask and in 92% ( 34 of 37 ) of samples collected when they were not wearing a mask . Thus , the relative risk for any virus detection with mask versus without a mask was 0 . 85 and borderline statistically significant ( CI 0 . 72 to 1 . 01; McNemar's test p = 0 . 06 ) . However , the reduction in copy number was statistically significant: The median number of viral copies in the fine particle fraction was 250 with masks and 560 without masks . The geometric mean copy number in the fine particle fraction without a facemask was 110 ( 95% CI 45 to 260 ) and the facemasks produced a 2 . 8 fold reduction in copy number ( 95% CI 1 . 5 to 5 . 2 , p = 0 . 001 ) . Combining the coarse and fine fractions , we detected viral RNA in 29 ( 78% ) subjects when wearing facemasks and 35 ( 95% ) when not wearing facemasks ( McNemar's test p = 0 . 01 ) . Surgical masks produced a 3 . 4 ( 95% CI 1 . 8 to 6 . 3 ) fold reduction in viral copies in exhaled breath . Fine fraction copy numbers were on average 8 . 8 ( 95% CI 4 . 1 to 19 ) times larger than coarse fraction copy numbers . The coarse and fine fraction copy numbers were correlated ( r = 0 . 60 , p<0 . 0001 ) . The viral load in the nasopharyngeal swab specimen , however , was not correlated with that in the coarse fraction ( r = 0 . 17 , p = 0 . 31 ) and only weakly with that in the fine fraction ( r = 0 . 29 , p = 0 . 08 ) . There was no significant difference in copy number between influenza A and B virus infection in either the coarse ( p = 0 . 28 ) or fine ( p = 0 . 26 ) fraction . Reported asthma ( p = 0 . 029 ) and feverishness ( p = 0 . 014 ) were associated with significantly lower fine fraction copy numbers . However , coarse fraction copy numbers were not significantly impacted and temperature measured at the time of testing was not associated with exhaled copy numbers . Vaccination in any prior year was associated with a non-significant trend toward lower copy numbers in coarse ( p = 0 . 11 ) and fine fractions ( p = 0 . 15 ) ; there were too few having received the current season's vaccine to analyze . Self-reported tachypnea , breathing difficulty , smoking , and lymphadenopathy were not associated with significant shifts in exhaled copy numbers . We recovered infectious virus from fine particle samples ( with and without mask ) produced by the two subjects with the highest numbers of viral RNA copies in the fine particle fraction after blind passage on MDCK cells . Sequence analysis showed that the two isolates were seasonal H1N1 , with sequence differences from each other and unrelated to any viruses present in the Veterinary Medicine laboratories at the time these samples were cultured . Virus copy number ( Table 3 ) declined with time since onset of symptoms . In the coarse fraction , each additional day after onset was associated with a 6 . 0 fold drop in the number of virus copies detected ( 95% CI 1 . 7 to 21 fold ) . Fine particles also declined with time , each additional day after onset was associated with a 2 . 4 fold drop in the number of copies detected ( 95% CI 1 . 1 to 5 . 1 fold ) .
We measured exhaled influenza viral particle copy number by quantitative RT- PCR in two particle size fractions , ≥5 µm ( coarse ) and <5 µm ( fine ) , and assayed the fine fraction for culturable virus . We observed that viral copy numbers were greater in the fine than in the coarse fraction , and recovered infectious virus from the fine particle fraction collected from the two samples with the highest RNA copy numbers . These results , combined with older data suggesting that the infectious dose via aerosol is about two orders of magnitude lower than via large droplets [12] , suggest an important role for aerosols in seasonal influenza transmission . Surgical masks nearly eliminated viral RNA detection in the coarse aerosol fraction with a 25 fold reduction in the number of viral copies , a statistically significant 2 . 8 fold reduction in copies detected in the fine aerosol fraction , and an overall statistically significant 3 . 4 fold reduction of viral copy number in the exhaled aerosols . This finding supports current Centers for Disease Control and Prevention recommendations that healthcare facilities encourage patients with influenza-like illness to don surgical facemasks as one component of an influenza infection control program [17] . When volunteers were not wearing surgical masks , we detected virus RNA in coarse particles exhaled by 43% and in fine particles exhaled by 92% of influenza patients . This is in contrast to the report by Johnson et al [16] , who detected influenza virus RNA in cough generated large droplet spray from 100% of influenza patients over two brief sampling trials , and from 78% on each trial . These discrepant findings are likely due to the very different collection techniques and particle sizes collected in these two studies . We used a specially designed aerosol sampler to collect particles from 0 . 05 to 50 µm in diameter . Johnson et al , by contrast , used simple deposition on petri dishes , and based on particle settling rates and collection times , that method would have been unlikely to collect particles with diameters of less than approximately 50 µm because smaller particles would have remained suspended in air and flowed around the petri dishes . We view results from Johnson et al and the present study as complementary . Together the studies show that surgical masks can limit the emission of large droplet spray and aerosol droplets larger than 5 µm [16] . However , surgical masks are not as efficient at preventing release of very small particles . It is well known that surgical masks are not effective for preventing exposure to fine particles when worn as personal protection [18] . We had hypothesized that when used as source control , exhaled droplets might be large enough prior to evaporation to be effectively captured , primarily through impaction . This appears to be true for virus carried in coarse particles . But the majority of virus in the exhaled aerosol appear to be in the fine fraction that is not well contained . Nevertheless , the overall 3 . 4 fold reduction in aerosol copy numbers we observed combined with a nearly complete elimination of large droplet spray demonstrated by Johnson et al . suggests that surgical masks worn by infected persons could have a clinically significant impact on transmission . For example if one hypothesized that all transmission were due to aerosol particles <50 µm , and estimated a reproductive number of 1 . 5 for influenza ( i . e . each infection generates 1 . 5 new infections on average at the start of the epidemic ) [19] , then the use of surgical masks by every infected case could reduce the reproductive number below 1 [20] . Compliance , however , would be a major limitation resulting in lower efficacy in real-world practice [21] , [22] . While it is generally assumed that large droplets shed from the respiratory tract contain infectious virus , there are limited data that indicate that fine particle aerosols released from the human respiratory tract contain infectious virus . In one previous study by Lindsley et al , infectious virus was detected in 2 of 21 cough aerosol samples , once with a sampler that did not discriminate between coarse and fine particles and once in the coarse particle fraction of a second instrument [23] . This observation , along with our observation that it was possible to recover culturable virus from the fine-particle fraction using our device demonstrates that humans generate infectious influenza aerosols in both coarse and fine particle fractions . This lends support to the hypothesis that aerosols may be a common pathway for influenza transmission among humans [8] , [24] . However , a clear test of the hypothesis requires intervention studies that can interrupt only one mode of transmission without interfering with others [25] . We only detected infectious virus in exhaled breath samples with high ( 104 to 105 ) copy numbers by quantitative RT-PCR . This implies that the ratio of total viral particles to infectious virus was about 103 to 104 , compared with 102 to 103 for laboratory stocks and experimental aerosols [26] . It is not yet known whether the low recovery of infectious virus ( despite high copy numbers of viral RNA ) represents technical difficulties in sampling and culturing exhaled breath samples or whether the vast majority of the virus exhaled by influenza A patients is actually non-infectious . These findings are consistent with those by Lindsley et al . [23] We designed the sampler specifically to overcome problems with existing bioaerosol samplers , including efficiently collecting sub-micron particles into a liquid and use of appropriate buffer to preserve infectiousness [27] . We have previously shown that collection on solid , dry collection media resulted in large losses of culturability [26] . Therefore , we did not attempt to culture the coarse fraction collected on a Teflon substrate . Subsequent studies in our laboratory indicated that about 50% of the infectious virus is lost during the concentration step of our procedure ( data not shown ) , suggesting that this is one contributing factor in the low rate of recovery of infectious virus in this study . The lack of strong correlation between the viral load in the nasopharyngeal and aerosol samples is possibly of interest . This may merely be a result of nasopharyngeal sample variability; in future studies , control for sample quality by PCR of a cellular gene may be helpful . Our sampler , as is the case with all samplers for fine and ultrafine particles , has an upper limit to the size droplet that can be pulled into its inlet airstream . Thus , a second possible explanation for the lack of correlation is that the nasopharynx is primarily a source for very large droplets ( >50 µm ) that we would not have detected . Furthermore , none of our subjects sneezed; an efficient method of generating droplets from the upper respiratory tract . This may imply that the smaller droplets we detected were generated in the lower respiratory tract and that the viral load at that location is not strongly correlated with the nasopharyngeal load . Alternatively , shedding into aerosol droplets may be driven by other host factors ( e . g . asthma , symptom severity , and immune response ) , co-infection with other agents , virus factors affecting release from the epithelium , or the nature of the resident microbiome . If shedding into aerosol is determined in large part by the location of infection in the respiratory tract , this may have implications for experimental studies of transmission [11] , [28] . Such studies will need to monitor aerosol shedding to determine whether nasal inoculation of donors results in aerosol shedding that mimics naturally acquired infection to validate the experimental design and aid the interpretation of results . Most of the viral aerosol generation we observed occurred during the first days of symptomatic illness ( Table 3 ) , consistent with studies of shedding monitored by nasal washes [29] . We studied each individual on only one occasion and , by design , have little data beyond day 3 . Further longitudinal studies of viral aerosol generation are needed to confirm these findings . New study designs will be needed to examine aerosol generation before and on the day of symptom onset in community acquired infection . A limitation of our study is that we recruited patients with certain signs and symptoms or who were positive on a rapid test or had fever , and therefore our data could be biased towards patients with higher viral loads [21] . However , we still observed significant inter-individual variation and modeling suggests that cases with higher viral loads are disproportionately important in the spread of influenza [30] , [31] . Additional studies are also needed to determine how aerosol generation correlates with symptoms ( including milder disease ) , presence of other health conditions , age ( we studied a narrow age distribution ) , and co-infection with other respiratory viruses so that recommendations for infection control can be critically evaluated .
We recruited volunteers with influenza-like illness from the Lowell , MA community , primarily among students and staff of the University of Massachusetts , beginning January 29 and ending March 12 , 2009 . The study protocol was approved by the Institutional Review Boards of the University of Massachusetts Lowell , Lowell General Hospital , and Saints Memorial Hospital , Lowell , MA . Oral informed consent was obtained by providing each subject with a detailed consent information form . Collection of a signed copy of the form was waived because it would have been the only personally identifiable information retained by this minimal risk study . Volunteers learned of the study through flyers and notices posted on campus and by referral from health care providers . We screened self-referred volunteers by telephone for influenza-like illness ( ILI ) . Persons who reported onset of fever and cough within the preceding 72 hours or were referred by a health care provider were invited to the laboratory for testing . We collected a nasopharyngeal specimen using a flocked swab ( 501CS01 , Copan Diagnostics , Murrieta , CA ) and temperature was taken with a digital ear thermometer ( Model 18-200-000 , Mabis Healthcare , Waukegan , IL ) . All volunteers with a temperature ≥37 . 8°C and a cough and volunteers without fever who provided a nasopharyngeal specimen positive for influenza by point of care testing ( QuikVue Influenza A/B , Quidel Corp . , San Diego , CA ) were invited to provide exhaled breath samples , answer a questionnaire , and provide a second nasopharyngeal specimen for analysis by PCR . Only subjects with influenza infection confirmed by PCR were included in the data analysis . We collected exhaled breath with the subject seated in front of the inlet for a sampler designed for human exhaled breath collection , Figure 2 , ( G-II ) described in detail by McDevitt et al . [27] Briefly , the G-II inlet was cone shaped so that the subject's face was situated inside the large end of an open cone with air drawn continuously around the subject and into the sampler . The cone allows the subject to breathe normally and unlike use of a mouthpiece , the subject could also wear a mask . The cone served as a capture type ventilation hood allowing collection of exhaled breath with minimal fugitive emissions even when the subject was wearing a mask with resultant redirection of flow . Intake air ( 130 L/min ) flowed through a conventional slit impactor that collected particles larger than 5 µm on a Teflon surface ( “coarse” particle fraction ) . To collect a “fine” particle fraction , water vapor was condensed on the remaining particles , which created droplets large enough to be captured by a 1 . 0-µm slit impactor . The 1 . 0-µm impactor was composed of a slit and a steel impaction surface sealed inside a large reservoir . Impacted droplets drained from the impaction surface into a buffer-containing liquid in the bottom of the reservoir . Concentrated buffer was pumped into the reservoir during collection to match the accumulation of water from collected droplets and maintain phosphate buffered saline with 0 . 1% bovine serum albumin throughout collection . The sampler was shown to be 85% efficient for particles greater than 50 nm in diameter and was comparable to the SKC BioSampler for detection and recovery of influenza A/PR/8/34 H1N1 by PCR and culture . Between subjects , the apparatus was disassembled and cleaned with a 0 . 5% hypochlorite solution . Exhaled particles were collected for 30 minutes while the subject wore an ear-loop surgical mask ( Kimberly-Clark , Roswell , GA ) and then for 30 minutes without a mask . Subjects were asked to cough 10 times at approximately 10-minute intervals for a total of 30 coughs during each 30 minute sample . One subject coughed frequently such that forced coughs were not required . No subjects were observed to sneeze . Immediately after collection , the Teflon impaction surface was removed and temporarily stored at −20°C . The impactors were scraped with a flocked swab wetted with Dulbecco's phosphate buffered saline with calcium and magnesium ( Hyclone , Thermo Scientific , Waltham , MA ) with 0 . 1% bovine serum albumin ( DPBS++BSA ) . The swab was eluted in 600 µl of DPBS++BSA for 1 minute with vortexing . The resulting sample was stored at −80°C . The fine particle fraction collected in DPBS++BSA buffer ( 100 to 150 ml volume ) was maintained at 4°C and concentrated by ultrafiltration using Amicon Ultra 15 filter units with a molecular weight cut off of 100 kD ( Millipore , Bedford , MA ) to a volume of approximately 400 µl . Following ultrafiltration , the filter was washed with 200 µl of DPBS++BSA , and the wash solution was combined with the retentate . Samples were stored at −80°C . RNA extraction in Trizol-chloroform , reverse transcription , and quantitative PCR were performed as previously described [1] , [32] . Quantitative PCR was performed using an Applied Biosystems Prism 7300 detection system ( Foster City , CA ) for coarse fraction samples or a LightCycler 480 ( Roche , Indianapolis , IN ) for the fine particle fraction . Duplicate samples were analyzed using influenza A and B primers described by van Elden et al . [33] A standard curve was constructed in each assay with cDNA extracted from a stock of influenza A ( A/Puerto Rico/8/1934 , Advanced Biotechnologies Incorporated , Columbia , MD ) with a concentration of 3 . 0×1011 virus particles per mL or a stock of influenza B ( B/Lee/1940 , Advanced Biotechnologies Incorporated , Columbia , MD ) with a concentration of 8 . 6×1010 virus particles per mL as determined by electron microscopy . Results are expressed as the total number of virus particles by reference to the standard curve , rounded to the closest integer value . The limits of detection were 6 and 11 viral RNA copies per qPCR well for influenza A and B respectively . Fine particle samples from all subjects were cultured for infectious virus on MDCK cells . Confluent cells in 24-well plates ( Corning , NY , USA ) were inoculated with 0 . 1 ml of the concentrated sample diluted 1∶1 in OptiMEM® I medium ( Invitrogen , Carlsbad , California ) . The plates were incubated at 37°C for 1 h with rocking every 15 min , and 0 . 8 ml of OptiMEM® I media with 1 µg/ml of TPCK-trypsin was added to each well and incubated for 72–96 h . The cells were checked daily for cytopathic effect ( CPE ) and if none was detected , two blind passages were performed using cell supernatant . At each passage , supernatants were tested for influenza virus by hemagglutination ( HA ) assay using 0 . 5% chicken red blood cells . Positive samples were confirmed by Flu DETECT ( Synbiotics , CA , USA ) strip test and by amplification of the hemagglutination ( HA ) gene by RT-PCR followed by sequencing . We analyzed the effect of surgical masks as a ) log relative risk for production of any virus aerosols assuming a binomial distribution using generalized estimating equations with exchangeable within-subject correlation to account for repeated measures , and b ) the geometric mean counts of virus particles detected in exhaled breath by qPCR and fractional reduction in copy number using Tobit regression analysis on log copy number with a random effect to account for variability between individuals . Tobit analysis was also used to compare coarse and fine particle fractions . Tobit regression avoids bias that would arise from assigning samples below the limit of detection a specific value such as zero or the limit divided by the square root of 2 . Surgical mask use was the dependent variable . We also computed McNemar's test for paired samples to examine mask effect and Spearman's correlation coefficient to examine the relationship between the load in the nasopharyngeal swab and aerosol fractions . Statistical analyses were performed using SAS ( Procs GenMod , NLMixed , Lifereg , Freq , Corr , and Means , version 9 . 2 , Cary , NC ) .
|
The relative importance of direct and indirect contact , large droplet spray , and aerosols as modes of influenza transmission is not known but is important in devising effective interventions . Surgical facemasks worn by patients are recommended by the CDC as a means of reducing the spread of influenza in healthcare facilities . We sought to determine the total number of viral RNA copies present in exhaled breath and cough aerosols , whether the RNA copies in fine particle aerosols represent infectious virus , and whether surgical facemasks reduce the amount of virus shed into aerosols by people infected with seasonal influenza viruses . We found that total viral copies detected by molecular methods were 8 . 8 times more numerous in fine ( ≤5 µm ) than in coarse ( >5 µm ) aerosol particles and that the fine particles from cases with the highest total number of viral RNA copies contained infectious virus . Surgical masks reduced the overall number of RNA copies by 3 . 4 fold . These results suggest an important role for aerosols in transmission of influenza virus and that surgical facemasks worn by infected persons are potentially an effective means of limiting the spread of influenza .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
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"public",
"health",
"medicine",
"infectious",
"diseases",
"public",
"health",
"and",
"epidemiology",
"aerosols",
"influenza",
"chemistry",
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2013
|
Influenza Virus Aerosols in Human Exhaled Breath: Particle Size, Culturability, and Effect of Surgical Masks
|
Gene networks are commonly interpreted as encoding functional information in their connections . An extensively validated principle called guilt by association states that genes which are associated or interacting are more likely to share function . Guilt by association provides the central top-down principle for analyzing gene networks in functional terms or assessing their quality in encoding functional information . In this work , we show that functional information within gene networks is typically concentrated in only a very few interactions whose properties cannot be reliably related to the rest of the network . In effect , the apparent encoding of function within networks has been largely driven by outliers whose behaviour cannot even be generalized to individual genes , let alone to the network at large . While experimentalist-driven analysis of interactions may use prior expert knowledge to focus on the small fraction of critically important data , large-scale computational analyses have typically assumed that high-performance cross-validation in a network is due to a generalizable encoding of function . Because we find that gene function is not systemically encoded in networks , but dependent on specific and critical interactions , we conclude it is necessary to focus on the details of how networks encode function and what information computational analyses use to extract functional meaning . We explore a number of consequences of this and find that network structure itself provides clues as to which connections are critical and that systemic properties , such as scale-free-like behaviour , do not map onto the functional connectivity within networks .
It is widely thought that to understand gene function , genes must be studied in the context of networks . Concurrent with this appreciation of complexity – and partially driven by it – the quantity of data available has grown enormously , especially for networks of interactions among genes or their products . Such networks can consist of millions of interactions across tens of thousands of genes , derived from protein binding assays [1]–[4] , RNA coexpression analysis [5]–[7] and other methods [8]–[11] . In systems biology , there is enormous interest in using high-throughput approaches to systematically glean information from these networks ( e . g . , [12]–[15] ) . Information from such networks is now embedded in numerous studies and tools used by molecular biologists ( e . g . , [16] , [17] ) , typically in combination with codifications of gene function exemplified by the Gene Ontology [18] . If one agrees that the function of a gene is partially a property determined by its context or relationships in the network , assessing the functional role of any given gene is challenging , as in principle one must consider all the interactions of the gene , in the context of the network . Biologists have dealt with these challenges in part by leveraging the biological principle commonly referred to as “guilt by association” ( GBA ) . GBA states that genes with related function tend to be protein interaction partners or share features such as expression patterns [19] . While not always referred to by name , GBA is a concept used extremely commonly in biology and which underlies a key way in which gene function is analyzed and discovered , whether on a gene-by-gene basis or using high-throughput methods . For example , an experimentalist who identifies a protein interaction infers a functional relationship between the proteins . Similarly two genes which interact genetically can be inferred to play roles in a common process leading to the phenotype [20] . This basic biological principle has been exploited by computational biologists as a method for assigning function in general , using machine learning approaches [21] , [22] . This is made possible by the development of large interaction networks , often created by aggregating numerous isolated reports of associations as well as from high-throughput data sets . It has been repeatedly shown that in such networks there is a very statistically significant relationship between , for example , shared Gene Ontology annotations and network edges . Indeed , this relationship has even been used to “correct” networks so they are more highly aligned with GO annotations [23] , , on the assumption that parts of the network that do not align with known function are more likely to be mistaken . Tremendous effort has gone into improving computational GBA approaches for the purpose of predicting function [25]–[32] . However , the number of biologically proven predictions based on such high-throughput approaches is still small and the promise of GBA as a general unbiased method for filling in unknown gene function has not come to fruition . In addition to their use in interpreting or inferring gene function , GBA approaches are also commonly used to assess the quality of networks , under the assumption that a high-quality network should map well onto known gene function information ( see , for example , [33] , [34] ) . In computational applications of GBA , “performance” is usually assessed using cross-validation , in which known functions are masked from part of the network and the ability to recover the information is measured . A common metric is the precision with which genes sharing a function preferentially connect to one another [13] , [25]; readers unfamiliar with prediction assessment methods are also referred to [35] and Text S1 ( section 1 ) . Built into this approach is the key assumption that GBA performance allows one to make statements about the network as a whole . Gene function is not the only way in which networks are assessed . Another popular approach is to examine structural properties of the network , such as the distribution of node degrees in the network ( number of associations per gene ) . It has been observed that many biological networks show “scale-free-like” behaviour ( as evidenced by a power-law distribution of node degrees ) , or other related characteristics resulting in a heavy-tailed distribution of node degrees [36] . Similar to the situation for gene function , it is thought that a sign of high network quality is a power-law distribution of node degrees and some authors have even used this as a criterion for refining networks , on the assumption that data which conflicts with a power-law distribution is low-quality [37] , [38] . The relationship between such properties and GBA has not been well-explored . While the significance of being scale-free is the subject of some debate [39] , it is still commonly assumed that it reflects some more fundamental “biological relevance” of a network and contributes to the function of the network ( and thus can be thought of “encoding functionality” ) . This paper represents an attempt to assess these types of assumptions , and in doing so derive some general principles about how function is “encoded” in current gene networks . Previously , we showed that gene function can be predicted from networks without using “guilt” . We observed that a trivial ranking of genes by their node degrees results in surprisingly good GBA performance; about one-half of performance could be attributed entirely to node degree effects [35] . Node degree is predictive because genes that have high node degree tend to have many functions ( e . g . GO terms; we call such genes “highly multifunctional” ) . Thus for any given prediction task , algorithms that assign any given function to high node-degree genes are rewarded by good performance without using information on which genes are associated with which . More concretely , when studying any biological process , simply assuming P53 ( for example ) is implicated will go a surprisingly long way , and networks encode this completely generic information in their node degree . In this paper , we show that multifunctionality has a second effect on the interpretation of gene networks , and one that has especially serious implications for the interpretation and utility of GBA , and more generally for current assumptions about the how networks encode function . We focus on the identification of small numbers of connections between multifunctional genes , representing “exceptional edges” that concentrate functional information in a small part of the network . We show that networks of millions of edges can be reduced in size by four orders of magnitude while still retaining much of the functional information . We go on to show that this effect guarantees that cross-validation performance of GBA as currently conceived is a useless measure of generalizability with respect to the ability to extract novel information . Further , because information about biological function is not encoded in the network systemically , the edges that do encode function may not overlap with those generating “important” network-level properties , such as whether the network is scale-free . We determine that as currently formulated , gene function information is not distributed in the network as is commonly assumed . Instead , almost all existing functional information is encoded either in a tiny number of edges involving only a handful of genes , or not at all . We conclude that computational attempts to scale up and automate GBA have failed to capture the essential elements that made it effective on a case-by-case basis .
A key phenomenon is what happens when two highly multifunctional genes are connected in the network . Such edges will tend to be both critical and exceptional . An edge between two genes that share a GO term is useful for prediction of that GO term during cross-validation , thus such edges have an increased probability of being critical compared to randomly selected edges . Intuitively , the more GO terms two connected genes share , the more GO terms for which that edge is likely to be critical . In principle this can have dramatic effects . For example , considering the ∼20000 genes in the mouse genome , a network constructed with just 100 edges among pairs of genes which share the largest number of GO terms yields an MAP across GO terms of ∼0 . 09 , much higher than the expected value of 0 . 002 if edges were selected at random . That is , the average rank of genes predicted to possess a given function based on their neighbours in the network is substantially elevated across many functions , even using data for only a few genes . This level of performance , with interactions present for only 181 genes , is higher than that obtained with a real network; for a carefully characterized mouse gene network of 4 . 5 million edges [25] , the performance of the real network can be matched with a network of only 23 edges among 45 genes ( MAP = 0 . 047; Figure 2A ) . These connections are therefore sufficient to generate the results obtained with the real network . Not all of these “most exceptional edges” necessarily exist in a real network , but it turns out that many do and have a dramatic impact on prediction . We assessed 10 mouse gene networks of different types for their degree of overlap with the 100 exceptional edges . The amount of overlapping is strongly predictive of the MAP performance of the real networks ( correlation 0 . 94 , Figure 2B ) . Because these networks incorporate data of diverse types ( see Table 1 ) , this suggests the effects of exceptionality are not an artifact of a particular type of network data . In the aggregated mouse network mentioned earlier , removing the 26 edges ( 0 . 004% of the total ) overlapping with the top 100 exceptional edges from the highest performing network results in a large drop in the MAP ( 15% ) . This suggests that a tiny number of edges may account for a large fraction of performance across most GO groups while using no information about most genes and that not only are these connections sufficient to obtain function prediction performance , but they may also be necessary . Because the value of additional edges in the “exceptional edge” network does not dramatically decline when adding more edges ( at 150 edges , the MAP is 0 . 11 , far above that of the original network ) , it is possible a small number of edges accounts for virtually all performance in the real network . These results strongly suggest that in the mouse network , information on gene function is concentrated on too few genes to be of much practical use , at least with regards to how gene function is typically defined ( e . g . , GO ) . We performed a detailed analysis of multiple Saccharomyces cerevisiae gene interaction networks [1] , [2] , [4] , [40] , [41] , [42] , which are more tractable to analyze exhaustively than the mouse networks due to their smaller size ( much sparser as well as having 1/3 the number of genes ) . We propose that these networks ( and their aggregate ) are representative of the highest-quality data available for gene function analysis . Using an aggregate of five of the networks , we identified critical edges by removing single edges and testing the average precision of each of 1746 GO terms ( see Methods ) , for each edge in the network . This yielded a dataset consisting of gene function prediction performance for each GO term in each of 72481 networks , each differing from the complete network by just one edge . This data set allows us to determine which individual connections are necessary to generate meaningful predictions for any given function; it can be visualized as a matrix of 72481 connections by 1746 average precisions of gene function prediction for that GO group using that network ( missing one connection ) . A critical edge , then , is one in which edge removal changes precision substantially for a given GO group , while exceptionality can be determined by aggregating the criticality of a connection across all GO groups . Removing any single edge usually has little effect on performance for any given GO term , but when it does have an effect , it is drastic . In Figure 3A , a sub-network for a representative GO term is shown; the distribution of the average precision values for this GO term with edges removed contains an extreme outlier ( Figure 3B ) . These genes have 27 unique interactions with one another and over 1200 connections to other genes . The average precision of this group using the complete network is 0 . 057 ( p<10−4 ) , high enough to be of practical importance to an experimentalist ( a functionally related gene is expected among the top 20 genes associated with genes within the GO group ) . However , the majority of functional information comes from a single edge , in which a gene within the GO group has a lone connection to another gene within the GO group . From the point of view of function prediction , this is problematic since most predictions going forward may have nothing to do with that edge or the two genes the edge links , and thus lack any evidence for being correct . Using this edge removal method , for each of 1746 GO terms , we identified the most critical edge . A single edge contributes very strongly to performance for the majority of GO terms , with an average contribution of 39% ( see Figure S1 ) . This means that when predictions were made in cross-validation , at least one of the folds had a ranking in which a true positive “hit” gene ranked highly due to one connection . This includes many GO groups where removing an edge has an effect greater than 100% ( removing the edge dropped performance below that expected on average by chance; fixing the maximum possible effect at 100% yields an average effect of 24% ) . We obtained very similar results to these when testing six networks individually ( our five constituent networks plus YeastNet [23] ) , with two informative exceptions that had fewer GO groups with a critical edge ( see Figure S2 ) . In the case of YeastNet this is because the network had been specifically tuned to reinforce GO learning in that edges were added or removed using knowledge from GO [23] . In contrast , the yeast genetic interaction network [34] suffers from a very low number of significantly learnable GO groups ( only 3% of GO group have average precisions more than 0 . 01 above the expected value , in contrast to the BioGRID protein interaction network [41] , where 67% of GO groups have at least that level of performance ) ; networks without learnable information also don't have critical information ( an alternative representation of genetic interactions , which does show critical edges concomitant with higher performance , is considered in Text S1 , section 3 ) . It turns out that many of the GO groups share the same “most critical edge” ( see Figure S3 ) : we identified 100 edges in the aggregate yeast network that are the most critical for ∼1/3 of the GO groups . Using just these edges for prediction of all GO terms we would expect a bimodal distribution of performance , in which the ∼1/3 of the GO groups for which the 100 edges are critical would have average precisions of approximately 60% of the full matrix ( since critical edges account for ∼40% of performance on average ) , while 2/3 of GO groups would have a performance drawn from the null distribution with most average precisions below 0 . 005 . In fact , as shown in Figure 3C , more GO groups are learnable than expected ( 1/2 ) , due to the presence of “nearly critical” edges ( see Text S1 , section 4 ) . Adding edges by their average degree of criticality across all GO groups ( their exceptionality ) , we see the network performance quickly improves above that of the full network ( Figure 4A ) . If we define a critical edge as one affecting the learnability of at least one GO group by 10% , we obtain a network of 4870 edges from the yeast data . We consider this larger set of edges to determine which interactions may be necessary ( rather than merely sufficient ) to generate function prediction performance . While a very small number of edges are sufficient , it is possible that redundancy in the network makes removing those few edges insufficient to remove all functional information . Interestingly , these 4870 edges are not necessarily between two members of the GO group for which the edge is critical ( an “internal” edge ) and in 50% of these GO groups , at least one of the connections was an external critical connection . Sometimes an edge is critical because it correctly documents non-membership ( an “external” edge ) . In this case , a non-member gene connected to an in-set gene would be highly ranked were it not for a critical connection to a gene outside the set . The earlier ranking of connections by their exceptionality gives a better sense of what connectivity is sufficient to generate gene function prediction performance . A network with as few as 350 connections generates better function prediction performance in the remainder of the 72131 connections . As in the mouse network , these critical connections provide essentially all of the learnable information in the network ( Figure 4B ) . These edges are also important even in the context of the full network , since their removal causes a significant decline in performance ( Figure 4B ) , and while their removal does not remove all functional information from the network , they are also not redundant with it ( as seen in the decline in precision-recalls ) We noted that there is a small subset of GO groups with very high learnability in the full network data ( average precision>0 . 5 ) . No groups have such high performance when only exceptional edges are used , suggesting something other than critical edges is responsible . A cursory inspection reveals these outliers are highly enriched for GO terms representing protein complexes . Such GO terms have an extremely high MAP on average ( 0 . 33; N = 91; Figure S4; Text S1 , section 5 ) . The network properties of these groups are also unusual , with a “clique-like” structure in contrast to other GO terms that tend to have very sparse connections among the members ( Figure S4 ) . Because of this property , we would not expect any edge to be critical . In addition , edges within the complex have a very different “meaning” than edges connecting complex members with genes outside . In particular , the former can be used to infer complex membership , but the latter obviously cannot . There is no reason to think the high learnability of protein complexes would reflect well on predicting the function of genes interacting with but not in the complex; nor can it be used to infer anything about the learnability of other functional groups . A remaining issue is whether there are any GO terms for which we might expect some generalizable predictability . For this to be the case , the group should be learnable in cross-validation , but not have any especially – meaning dominantly - critical edges ( or equivalently have many edges strongly improving average precision ) . This would at least increase the confidence that other edges ( used for extracting novel information ) are functionally relevant . Unfortunately GO groups that lack critical edges altogether tend not to be learnable in cross-validation and very rarely do GO groups have very many critical connections ( Table S1 ) . We argue that the presence of exceptional edges is a problem , and ideally the network would not contain them . This is because they concentrate most of the apparent functional information in a tiny fraction of the network and are not specific to any one function , and therefore cannot provide specific functional information about most genes . On the other hand , critical edges are the only readily available correlate for functionally relevant connections . Thus the ideal network would contain only critical edges ( which are hopefully the functionally relevant ones ) , but few exceptional edges . However , it is not satisfactory to evaluate criticality using impact on learnability , as this would result in overfitting . It is therefore desirable to identify more general properties of critical edges other than their impact on learnability . We sought a correlate of criticality which can be used to prioritize some connections over others . Based on our previous research showing that high node degree genes are generic in their functionality [35] , we suspected that edges involving genes with high node degree ( hubs ) are less likely to be critical . This is because losing a gene's only connection is more likely to damage learning performance than removing one of dozens . In addition , hubs may represent highly-studied genes potentially more open to the accumulation of false positive connections . In Figure S5 , we can see that the fraction of critical edges a gene possesses decreases as a function of its total number of connections . We propose , then , to prune the network by privileging connections on low node degree genes . This is consistent with our previous work showing that hubs tend to attract computational predictions at the expense of less-well-characterized genes ( “rich get richer” ) [35] . This pruning yields a network that , even with 1/2 of connections removed , performs similarly to the original network ( Figure 5A ) . The specific predictions made are also very similar , with genes that are predicted strongly in the original network tending to have similar relative ranks in the pruned network ( Text S1 , section 6 and Figure S7 ) . While this has not necessarily improved the situation with respect to generalizing , removing edges from the network implies that fewer predictions will be made in the first place , which is helpful in that it removes potentially misleading results . It further suggests that , at least with respect to GO , gene networks contain many irrelevant edges that can potentially be identified using principled means . We tested this pruning procedure in an independently constructed network of human protein interaction data . We find that pruning the human network by half did not remove functional information , as determined from the function predictions ( Figure 5B ) . We confirmed that this network pruning worked by preferentially selecting exceptional edges by examining the human network for criticality , as in the yeast network . We found roughly comparable criticality , with the 1475 GO groups with average precision above 0 . 01 having a critical connection average effect of 44% of their performance ( the threshold of 0 . 01 allows for the fact that fewer GO groups are learnable from the human data ) . One possibility is that the ability to discern criticality in both networks merely reflects interactions present in both networks through homologies . In fact , mapping the criticality of connections between the two networks through homology reveals no correlation between the two ( r = −0 . 02 ) ; what is critical in one network is no more likely than average to be critical in the other . We have suggested that a major problem with the existence of exceptional edges is that they reduce supposedly “network-wide” properties to the properties of a very small part of the network . As a specific example of this problem ( beyond describing the information encoded in networks ) , we consider a well-studied network property , whether the network is scale free ( or at least scale-free-like , with a very heavy tail to the degree distribution ) [43] . Our original yeast protein interaction network has a “scale free” structure , as exhibited in the distribution of its node degree ( see Figure S6 ) . However , our results show that connections of high node degree genes are preferentially free of specific functional information , suggesting that the two most famous properties of biological networks , functional association and approximate scale freeness , are largely independent . To demonstrate this , we perform the pruning by node degree in the yeast network which we know improves GBA performance , but has the effect of truncating the node degree distribution ( Figure S6 ) . While truncated power-law distributions for networks have been previously discussed [44] , this degree of scaling is generally not reported , and there is clearly a dominant scale in the network . The pruned network node degree distribution is well characterized by its average node degree of 12 and the distribution does not appear at all to follow a power law distribution . The power law node degree structure in this network was preferentially encoded in connections that contain no known functional information . Because exceptional edges preferentially encode function , one reasonable expectation might be that they are higher quality in terms of their experimental support . To test this , we employed the HIPPIE database ( http://cbdm . mdc-berlin . de/tools/hippie/ ) which characterizes protein interactions by the strength of evidence supporting them ( including experimental techniques employed ) . There is a weak but significant rank correlation between exceptionality and data quality as judged by HIPPIE ( r = 0 . 09 , p<0 . 01 ) ; higher quality data is more likely to encode exceptionality . While we would not expect a particularly strong trend across the network at large ( due to our emphasis on the role of outliers ) , another factor is serving to weaken the correlation . Edges that encode no known function , and therefore accrue exceptionality only by virtue of encoding non-membership in a function ( these are the “external” edges discussed above ) , show a trend in the opposite direction to those edges which largely encode functionality “internally” ( or are strongly functionally relevant as judged by a high semantic similarity of GO annotations; Jaccard index>0 . 75 ) . Edges which encode non-functionality are significantly associated with better quality linkage ( p<0 . 05 ) , while those that encode direct functionality are significantly associated with lower quality linkage ( p<0 . 05 ) . One possible interpretation of this result is that it reflects differences in the degree to which genes are studied , and that highly multi-functional genes may more readily accumulate “high quality” interaction data with one another than they may accumulate low-quality connections with less studied genes [45] . To further examine how exceptional edges arise , we looked at the role they play in randomly constructed networks , in which any given connection is equally likely to occur . We first conducted experiments using randomly defined “GO groups” of fixed size ( 20 genes; see Methods ) . The distribution of MAP values across 1000 random networks was approximately normal ( p∼0 . 5 , Kolmogorov-Smirnov test ) , but as expected most networks generated in this way do not yield significantly high MAP values . We used the statistical parameters from our initial simulations to pick a MAP threshold ( more than 3 standard deviations from the mean ) for 100000 random networks . Averaging across the 876 such networks produced during our simulation , we obtain exceptional edges in the sense that the 24 connections most frequently reoccurring across those networks yields a ( very small ) network which performs well ( z-score>3; that is , above the threshold used to select the 876 individual networks ) . Examining these edges , they have an elevated semantic similarity in their “pseudo-GO” annotations ( Jaccard similarity of 0 . 09 compared to an expected value of 0 . 01; p<0 . 01 ) . Based on this , it appears that exceptional connections occur in high-scoring random networks for the simple reason that it is easier to accidentally obtain small number of highly impactful ( exceptional ) edges than many edges with smaller effects on performance ( the latter would be expected if there was systemic encoding of function throughout the network ) . We obtained similar results with the same type of random networks trained using on the real Gene Ontology , suggesting that the appearance of criticality in gene function prediction is not an artifact of GO structure .
One way of viewing our findings is that the GBA principle , which is fruitfully applied by biologists on a small scale when analyzing genes one at a time , does not scale easily to networks . Our results suggest that , for any given function , most associations are either useless or misleading . This is likely to be partly due to noise but also the fact that large networks are not constructed with a particular gene ( or function ) in mind . Small-scale studies do not escape this problem , but when testing the associations of a single gene under more controlled conditions , especially in “function-specific” conditions , biologists can more efficiently reject spurious findings and enrich for functionally-relevant associations . For these reasons we suspect that large-scale attempts to analyze gene function will continue to be frustrated by the mismatch between the content of the network and “gene function” as it is currently systematized . The notable exception is protein complexes . The problem with the mismatch between gene function and the networks could also be seen as lying either with GO ( and other systems of defining gene function ) , or with the networks themselves . Indeed , our results suggest that the apparent agreement of GO and gene networks is largely an illusion ( again , with the exception of protein complexes ) . Thus function information might be extracted from networks , but not routinely using schemes like GO as a guide . However , as mentioned above it is also likely that the gene networks themselves are problematic , in that they likely contain many edges that are not functionally relevant . The “ever more data” approach common to the field runs the risk of filling gene networks with false positives as the occasional errors in individual experiments are aggregated , and it is very difficult to prove the lack of an interaction . In support of this , protein interactions in the BioGRID network have declined in average apparent functionality over the past fifteen years ( Figure S8 ) , with the Jaccard similarity for connections added in a given year declining on average ( r = −0 . 95 , p<0 . 01 ) . This problem is exacerbated by the necessary reliance on computation , which makes it harder to see which part of the data is providing learning performance . It seems one has to decide whether it makes more sense to “fix” the networks so that they are more functionally relevant , or to discard GO and its relatives for this purpose in favour of an alternative ( potentially equally problematic ) that matches the networks better . The former makes sense if one is interested in predicting GO group membership . While this is treated as an important goal by many , it has in fact been thrust upon the field as a default; predicting GO terms has become a proxy for predicting gene function in general . Our results on network pruning by node degree suggest that current networks can be cleaned up extensively without hurting GO prediction in cross-validation , but generalizing to make useful new predictions is still a very serious problem . Replacing GO also seems very challenging: all current systematizations of gene function that we are aware of are currently highly correlated with GO ( or indeed directly mapped to GO ) , such as KEGG , MIPS , EC numbers , Pfam , and so on; we are certainly not aware of any systematization which is more learnable than GO ( if there was , GO would not be used as much for this purpose ) . There is at least a third alternative , to use the network itself to define function , where the main function to be “predicted” is “gene X interacts with gene Y” . This is of course a common exploratory way to use the data ( “What is my gene connected to ? ” ) , but the quality of the network itself becomes paramount , and as a definition of function it verges on the trivial . Furthermore , “gene X interacts with gene Y” is most definitely not a function that is any meaningful sense “distributed” in the network . Guilt by association ( in the most general sense ) has provided essentially the sole principled interpretation of network data from a functional perspective . Without it , rather than providing information on function , connectivity in this sense is only information on mechanisms; we must essentially switch from a top-down perspective , informed by GBA , to a bottom-up perspective based on the specific insight interactions provide . If interaction data has a purely observational meaning , then network quality can only be assessed by its replicability and consistency , standards by which most network data would probably perform poorly . Other network-derived definitions of gene function such as “hubbiness” or “betweenness centrality” [46] that are less sensitive to network quality are potentially more useful , but only help throw the limitations of the network for deriving more precise statements about gene function into relief . We note that while we have not directly addressed all variants of GBA which focus on predicting protein interactions , regulatory relationships , or the effects of mutations , these either amount to making statements about the network itself ( filling in missing edges , or interpreting an edge ) , or are likely to behave similarly to GO prediction . We conclude that gene networks encode information on gene function , but primarily in ways that are highly localized and with very limited predictive ability . Many gene function prediction methods explicitly treat “protein-complex”-like structures ( cliques ) as an optimal way to encode function ( e . g . [25] , [47] ) . Functional information encoded in this way is readily retrievable by algorithmic means and shows optimal “guilt by association” . While this captures some functions , it is not what one would expect or desire as a general property of a gene network for function prediction purposes . If those cliques are not connected together ( allowing perfect GBA for the functions encoded by the clique ) , one cannot predict any additional functions . On the other hand , if the cliques are connected together , one must ask what the desired structure of that “coarser” network should be ( treating cliques like genes ) . If the answer is that it should also be clique-like in order to optimize GBA , one rapidly exhausts the network in a small set of hierarchical modules . This might be satisfactory if one supposes that gene function is strongly hierarchical ( and also fairly simply organized ) , but this is clearly counter to the state of affairs . Indeed , in real networks genes with similar functional annotations tend to be connected together not just for “protein complexes” but for most functions ( the GO annotation Jaccard similarity matrix in our yeast data yields a high MAP of 0 . 65 ) . Thus , it is possible in principle to encode functionality more broadly , without requiring cliques , and without relying on multiple networks to obtain specificity . While we have highlighted the role of exceptional edges as a problem , we also believe that recognizing the importance of exceptional edges more clearly replicates the way biologists work with data; thus , the classification of interactions with greater detail is a step toward “fixing” guilt by association . Our results lead to some concrete recommendations for gene network analysis . First , if one is assessing network quality using GBA-like approaches , it is essential to test the effect of critical edges . Because exhaustively identifying critical edges is computationally intensive , our approach for pruning edges based on node degree provides a useful and easy-to-compute diagnostic . If pruning ( say ) ½ of the network has little effect on GBA performance , it is obvious that most of the ( measurable ) functionally-relevant information is concentrated in a very small fraction of the network , making global statements about network quality unlikely to be of use . A separate assessment of the network for the completeness of recovery of protein complexes is also reasonable , bearing in mind that these have very distinct properties . Our second set of recommendations is directed at investigators who are attempting to create gene function prediction tools . Cross-validation performance will be a useless measure of the quality of new predictions unless it is first shown that , for any given classification task , performance is not due to a single edge . Again , doing this exhaustively is computationally expensive , but our results provide some rules of thumb . One should test the effect of the removal of edges that involve an in-group gene; such edges are at least enriched for critical edges ( bear in mind that a critical edge can involve two out-of-group genes , so negative results for this test are not conclusive ) . These tests should be used in conjunction with our previous suggestion that learning performance be compared to that provided by node-degree ranking [35] .
Additional information on the methods , implementations and data is available at www . chibi . ubc . ca/critcon . Gene networks: The mouse network data consisted of 10 data matrices representing associations among 21603 genes , with 774 GO groups ( 10–300 genes each ) being used for assessment as in [25] . Our yeast PPIN was obtained by aggregating data from [1] , [2] , [4] , [40] , [41] , [42] and contained 72481 unique interactions Our human PPIN was obtained by aggregating data from [48]–[53] and contained 100623 unique interactions . Additional detail on the component networks is provided in Table 1 . Gene lists: We analyzed the list of 20710 human genes from the UCSC GoldenPath database [54] “known gene” table . The 6200 yeast gene list was obtained from NCBI [55] . The mouse gene list was as used in [25] . Algorithm: For guilt by association analysis , we ranked genes by a voting scheme within the training set ( by ranked coexpression ) relative to genes outside the training . Despite its simplicity , this method gives performance comparable to the best-performing algorithms [56] , with the benefit of being extremely fast . Cross-validation: Eight-fold cross-validation was used in assessing the mouse data , and three-fold cross validation was used to detect critical connections in the yeast and human data and for assessment consistency . Performance was assessed by taking the precision averaged across all true positives within a particular testing set ( that is , the discrete sum ) , yielding the area under the precision-recall curve or average precision ( see Text S1 , section 1 ) . Our findings hold for other measures such as receiver operating characteristic ( ROC ) curves , but as shown in [35] , ROC curves are sensitive to node degree effects . In contrast precision-recall curves allow us to more effectively isolate the effect of critical edges . Critical edges were detected by performing the full gene function cross-validation across all GO groups for each of the networks resultant from removing one edge from the full network , in both the human , yeast , and constituent networks . Exceptional edges were chosen by aggregating the average precision the network resultant from a given edge being removed , across all GO groups . The more performance is degraded across all GO groups , the higher the exceptionality of the edge . Exceptional edges were predicted by selecting the gene pair possessing the largest number of overlapping GO functions , weighting each GO function by the inverse of the number of times it had already been used to add gene pairs , and repeating until the desired number of edges were obtained . Simulations: Random networks were constructed of size 1000 genes with sparsity 0 . 002 ( 1000 edges ) and assessed for functional performance using a random set of gene groupings ( 100 groups of size 20 ) . MAP across the groups was assessed using neighbour-voting , and those networks scoring more than three standard deviations above the mean of 1000 simulations were aggregated to determine commonalities in their connectivity .
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The analysis of gene function and gene networks is a major theme of post-genome biomedical research . Historically , many attempts to understand gene function leverage a biological principle known as “guilt by association” ( GBA ) . GBA states that genes with related functions tend to share properties such as genetic or physical interactions . In the past ten years , GBA has been scaled up for application to large gene networks , becoming a favored way to grapple with the complex interdependencies of gene functions in the face of floods of genomics and proteomics data . However , there is a growing realization that scaled-up GBA is not a panacea . In this study , we report a precise identification of the limits of GBA and show that it cannot provide a way to understand gene networks in a way that is simultaneously general and useful . Our findings indicate that the assumptions underlying the high-throughput use of gene networks to interpret function are fundamentally flawed , with wide-ranging implications for the interpretation of genome-wide data .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"genetics",
"biology",
"computational",
"biology",
"genetics",
"and",
"genomics"
] |
2012
|
“Guilt by Association” Is the Exception Rather Than the Rule in Gene Networks
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The oomycete Hyaloperonospora arabidopsidis ( Hpa ) is the causal agent of downy mildew on the model plant Arabidopsis thaliana and has been adapted as a model system to investigate pathogen virulence strategies and plant disease resistance mechanisms . Recognition of Hpa infection occurs when plant resistance proteins ( R-genes ) detect the presence or activity of pathogen-derived protein effectors delivered to the plant host . This study examines the Hpa effector ATR13 Emco5 and its recognition by RPP13-Nd , the cognate R-gene that triggers programmed cell death ( HR ) in the presence of recognized ATR13 variants . Herein , we use NMR to solve the backbone structure of ATR13 Emco5 , revealing both a helical domain and a disordered internal loop . Additionally , we use site-directed and random mutagenesis to identify several amino acid residues involved in the recognition response conferred by RPP13-Nd . Using our structure as a scaffold , we map these residues to one of two surface-exposed patches of residues under diversifying selection . Exploring possible roles of the disordered region within the ATR13 structure , we perform domain swapping experiments and identify a peptide sequence involved in nucleolar localization . We conclude that ATR13 is a highly dynamic protein with no clear structural homologues that contains two surface-exposed patches of polymorphism , only one of which is involved in RPP13-Nd recognition specificity .
Oomycetes are a devastating class of filamentous eukaryotic pathogens that afflict plants and animals alike [1] , [2] . Notorious for their role in the Irish Potato Famine and more recently for their decimation of the live oak species throughout California , oomycetes are highly pathogenic eukaryotic microbes that are difficult to control in the field—quickly overcoming chemical control methods and costing billions of dollars annually in crop losses [3] , [4] . Despite the enormous impact of these pathogens , our knowledge of how they manipulate plant metabolism and overcome host defenses resulting in disease is still extremely limited . Many oomycetes are obligate biotrophs , making them difficult , if not impossible , to culture and are therefore genetically intractable . Phytopathogenic oomycetes like Hyaloperonospora arabidopsidis ( Hpa ) grow intercellularly , forming parasitic structures called haustoria that play a role in feeding and suppression of host defense systems . A cohort of pathogen proteins known as effectors are secreted across this haustorial membrane , a subset of which are further translocated across the plant plasma membrane by an unknown mechanism functional in both plants and animals [5] , [6] . The role of most of these oomycete effectors in pathogen virulence has remained elusive , as many of their protein sequences lack similarity to proteins currently in the databases [7] , [8] . The Hpa/Arabidopsis pathosystem is an ideal model for studying oomycete-host interactions . High levels of genetic diversity existing between naturally occurring populations of both Hpa and A . thaliana , along with genome sequence availability ( www . arabidopsis . org , http://oomycetes . genomeprojectsolutions-databases . com/ ) , allows for genetic exploration and dissection of each species . ATR13 is an RxLR effector from the downy mildew oomycete Hyaloperonospora arabidopsidis ( Hpa ) that is recognized in A . thaliana in a race-specific manner by its cognate R-gene , RPP13 [9] . This class of proteins contains an RxLR motif that is implicated in host translocation . Both ATR13 and RPP13 are highly polymorphic genes , implying that the alleles have undergone diversifying selection at their respective loci [9] , [10] . The maintenance of ATR13 in all isolates of Hpa , together with the evidence of diversifying selection at this locus [11] , implies that this effector confers a benefit to the invading oomycete . However , the function of ATR13 has been difficult to extrapolate as no known proteins share sequence similarity to this effector . Several effector molecules from other classes of pathogens have been structurally elucidated providing insight into their mode of action and virulence . The fungal effector AvrL567 from Melampsora lini , a flax rust , has similarity to ToxA [12] , a protein involved in cell death induction from the necrotrophic wheat pathogen Pyrenophora tritici-repentis [13] . The NEP1-like effector , NLPpya , from the oomycete Pythium aphanidermatum has structural similarity to actinoporins , proteins derived from various marine invertebrates that form transmembrane pores facilitating membrane disintegration [14] . Additionally , crystal structures of bacterial effectors like AvrPto from Pseudomonas syringae in complex with their targets have provided a structural basis for the activation of plant immunity , showing how an effector interacts with its target and derepresses host defenses [15] . Recently , there has been a surge of structural information becoming available pertaining to RXLR effectors . The NMR structure of the Phytophthora capsici RXLR effector Avr3a4 , a close homolog to the P . infestans Avr3a that inhibits CMPG1 function in planta [16] , revealed a positive surface patch involved in binding phosphatidylinositol monophosphates ( PIPs ) —compounds essential for Avr3a accumulation and therefore function [17] . Chou et al . solved the crystal structure of ATR1 , an Hpa RXLR effector that adopts a two-domain structure comprised of 13 α-helices . Mapping by sequence conservation among ATR1 alleles revealed that polymorphic residues specifying RPP1 recognition were distributed in clusters along the surface of the protein [18] . Interestingly , Boutemy et al . have used structural biology and bioinformatics to show that Avr3a11 from P . capsici and PexRD2 from P . infestans share a conserved α-helical fold ( termed the WY domain ) along with a predicted 44% of all annotated Phytophthora RXLR effectors [19] and the ATR1 protein described by Chou et al [18] . To obtain more information on the virulence function and recognition domains of ATR13 , we used Nuclear Magnetic Resonance ( NMR ) to solve its backbone structure . Further , we generate loss-of-recognition and gain-of-recognition mutants through both site-directed and random mutagenesis and map these mutations onto the structure to identify regions important in RPP13 recognition . Additionally , we describe a region of ATR13 required for nucleolar localization but show that ATR13 subcellular localization has no effect on recognition by RPP13 . Mutational effects of ATR13 Emco5 ( recognized by RPP13 ) and ATR13 Emoy2 ( unrecognized by RPP13 ) are assayed using the Agrobacterium tumefaciens/ Nicotiana benthamiana surrogate system [20] , where ATR13 recognition by RPP13 , in this case the Niederzenz allele ( RPP13-Nd ) , results in the hypersensitive response ( HR ) , a plant-specific form of programmed cell death purported to limit pathogen spread .
To obtain soluble protein for structural studies , we expressed truncations of three different alleles of ATR13: Emco5 , Maks9 , and Emoy2 , lacking the secretion peptide and RxLR translocation domain ( Δ41 truncations ) ( Figure 1A ) . Of the three alleles , ATR13 Emco5 produced the most soluble protein and was therefore selected for generation of crystals for structural determination . Efforts to crystallize ATR13 were successful ( Figure S1A–C ) ; however , we were unable to determine experimental phases . From our crystallographic efforts , we noticed that crystals required a minimum of two months to form , leading us to believe that some kind of natural proteolysis was taking place prior to crystallization . To address this , we performed limited proteolysis [21] using both trypsin and chymotrypsin on Δ41 ATR13 Emco5 samples to discover a more stable truncated version of the protein ( Figure 1B ) . Additionally , we determined that the protein in the crystals existed in two forms: the original Δ41 version and a cleaved version we identified as Δ53 ATR13 by mass spectrometry . To verify the biological relevance of our Δ53 ATR13 Emco5 truncation , we transiently expressed this truncation in N . benthamiana containing the RPP13-Nd transgene , and demonstrated its ability to trigger a hypersensitive response ( HR ) . Furthermore , to determine the minimal region necessary for RPP13-Nd recognition , truncations from both the N-terminus and C-terminus of ATR13 Emco5 were expressed transiently in N . benthamiana via Agrobacterium inoculations . While RPP13-Nd was able to recognize ATR13 Emco5 N-terminal truncations up to 62 amino acids , once 86 amino acids were removed , RPP13-Nd recognition was compromised ( Figure 1C ) . All C-terminal deletions resulted in compromised recognition , despite intact protein expression ( Figure 1D ) . After attempts to solve the structure using crystallography stalled , we turned to nuclear magnetic resonance spectrometry ( NMR ) to solve the structure of ATR13 . Our first 1H-15N heteronuclear single quantum coherence ( HSQC ) spectrum provided insight as to why crystallography was unsuccessful; only three-fourths of the expected signals were observed , suggesting that the remainder of the protein was not well ordered ( Figure 2A ) . Worried that this region of internal disorder was due to several missing direct repeats in the Emco5 allele relative to other alleles of ATR13 ( Figure 3A ) , we purified ATR13 Maks9 and collected its 1H-15N HSQC spectrum . The 1H-15N HSQC spectrum of the Maks9 allele contained approximately the same number of peaks as the Emco5 allele , suggesting that the insertion present in the longer alleles does not stabilize the disordered loop . Overlays of the Maks9 and Emco5 spectra also reveal significant overlap between peaks present in both samples , indicating that additional missing peaks in the Maks9 spectrum are most likely part of the direct repeat region not found in Emco5 . Backbone amide proton assignments were obtained using standard 3D triple resonance heteronuclear experiments for 81 residues including those in segments G56 through D96 , N104 and A105 , and L116 through Q154 with the exception of I92 . Only one sharp peak and two broad peaks remain unassigned in the HSQC spectrum , indicating that approximately 16 of the 95 expected HN signals are missing . By process of elimination , we conclude that these missing signals correspond to residues in segment 97 – 115 , as all other residues have backbone amide proton assignments . Of the residues with assigned HN resonances , approximately 88% of the side chain signals were also assigned . The chemical shift assignments were combined with NOE , JHNHα scalar couplings , hydrogen exchange measurements , and residual dipolar couplings to construct a structure of ATR13 . A summary of the NMR-derived restraints and structural statistics are presented in Table 1 . The well-ordered region of the structure ( 76–88 and 120–150 ) is defined by 14 restraints per residue . These data yielded a well-defined backbone fold , but the sidechains are less well defined . Restraint violations for ATR13 are good , as are most of the structural quality factors ( Table 1 ) . The relatively high value obtained from Verify3D [22] is due to the ill-defined state of the loop region ( residues E89 – Y115 ) ( see below ) . The portion of ATR13 elucidated by NMR consists of a central helix ( residues 122–135 ) that packs against a short helix and turn ( P77-H88 ) on one side , and a long C-terminal helix ( residues A140 – A150 ) on the other ( Figure 2C ) . The N-terminal residues prior to the first helix ( G54-P77 ) are not particularly well defined by the Nuclear Overhauser Effect ( NOE ) data . As an alternative , the steady-state 1H-15N NOE enhancement provides a qualitative measure of dynamics [23] . Rigid HN bonds typically have NOE enhancements of approximately 0 . 8 . As the sub-nanosecond dynamics increase , the NOE enhancement decreases , and can even become negative . Heteronuclear NOE values for residues G56 through D62 increase slowly from −1 . 0 to 0 . 5 , characteristic of a flexible N-terminus . However , there is some evidence that residues L64 through K76 are more ordered than could be defined . For example , the program TALOS [24] , which compares measured CA , CB , CO , and HA chemical shifts to those from a database of known structures , predicts that the phi / psi angles for residues L64-K70 adopt a helical conformation , while in the later portions of the segment ( S69-K76 ) several weak dNN NOEs and small 3JHNHA couplings ( ∼5 Hz ) suggest a turn or helical structure . These data give rise to the hint of structure for S69 through K76 ( Figure 2B , C ) . Residues L64 through K76 also have fairly high heteronuclear NOE values ( ∼0 . 7 ) which supports the premise that there is some order within this region that is not defined by the NMR data . The most outstanding feature of the structure is an ill-defined loop that extends from E89 to Y115 . In the segment 97 – 115 , only N104 and A105 are assigned . N104 and A105 show reduced 1H-15N heteronuclear NOE values ( ∼0 . 5 ) , also suggesting that at least a portion of the segment is flexible ( Figure 2B ) . In attempts to solve the crystal structure , density for the single selenomethionine ( M113 ) located within the loop region was displaced from the remaining protein density , which is consistent with disorder in this region . As stated earlier , approximately 16 amide signals were missing from the 1H-15N HSQC spectrum , most of which correspond to residues within this region . The absence of peaks suggests that this loop has flexibility on an intermediate time scale under the conditions studied . Structural homology searches using the Dali server ( http://ekhidna . biocenter . helsinki . fi/dali_server/start ) yielded few candidate proteins with very weak structural similarity ( Figure S2 ) indicating that ATR13 possesses a fold that has not yet been described in the PDB databank . Using sixteen alleles of ATR13 ( Figure S3 ) , we generated an alignment in Jalview [25] and determined percent conservation of residue identity across ATR13 alleles using BLOSUM [26] . Conservation scores were mapped onto a single representative low-energy model of the ATR13 Emco5 structure ( used throughout ) with the Chimera software package [27] , displaying highly polymorphic residues in shades of orange and conserved residues in shades of blue ( Figure 3 ) . This model shows two discreet pockets of polymorphism on the surface of the ATR13 protein . Additional low energy models of ATR13 Emco5 displaying percent conservation show similar polymorphic patches ( Figure S4 ) . Exploiting natural variation occurring between recognized and unrecognized alleles of ATR13 , Emco5 and Emoy2 respectively , we singly or doubly mutated polymorphic residues possessing vastly different chemical properties between alleles , or residues previously implicated in recognition ( Figure 3A ) [11] . Using the surrogate Agrobacterium/N . benthamiana system [20] , we demonstrated that single amino acid changes of these residues have little to no effect on recognition of ATR13 Emco5 by RPP13Nd . However , in several cases , double mutations reduced the intensity of the hypersensitive response ( E133/166K , T119/152I and T119/152I , R148/181Q ) or eliminated it , as is the case with F73N , T119/152I ( Figure 4A ) . When mapped onto the structure , F73 and T119/152 appear to be surface-exposed and in close proximity ( Figure 4C ) , implicating this specific region in avirulence determination . As a complement to the loss-of-recognition screen , we performed gain-of-recognition random mutagenesis on ATR13 Emoy2 . After screening 800 mutant ATR13 Emoy2 alleles for altered recognition by RPP13-Nd on N . benthamiana , we identified nine clones that possessed an intermediate recognition phenotype ( Figure 4B ) . All nine of these mutants had either the I119/152T , or N73Y/S/I substitutions , lending support to the theory that these two residues are critical for RPP13-Nd mediated HR . In addition , like unrecognized alleles , the Maks9 variant of ATR13 has an asparagine at residue 73 , however it is recognized by RPP13-Nd . In this allele , when N73 is substituted with a phenylalanine like that found in Emco5 and most recognized alleles , the resistance response by RPP13-Nd is more robust than that generated against wildtype Maks9 ( data not shown ) again implicating this residue position as crucial for full RPP13-Nd recognition . To more thoroughly explore the avirulence role of ATR13 in conjunction with RPP13-Nd , we performed random mutagenesis to identify additional amino acids that play a role in ATR13 recognition . Of 1 , 200 colonies screened , 95 clones showed a loss-of-recognition phenotype . When sequenced , 50 of these clones had either frame shift mutations or early stop codons , while the remaining 45 had either single , double , or triple mutations ( Figure S5C ) . We also sequenced 95 mutant clones showing intact HR signaling . These retention-of-recognition ( ROR ) mutants were used to eliminate background mutations that did not alter recognition ( Table S1 ) . When inoculated onto N . benthamiana , the 45 LOR mutants display varied timing and intensity of hypersensitive response , as well as a range of mutant protein stabilities relative to wildtype levels ( Figure S5A , B ) . Fourteen mutant alleles of ATR13 Emco5 appear to accumulate amounts of protein equaling or in excess of the wildtype level ( Figure S5C ) , and when analyzed in the context of the structure , these residues are nested within its core rather than surface-exposed ( Figure 4C ) , suggesting that the overall fold of the protein is altered rather than the interaction surface . Interestingly , most of the altered residues occur in regions that are conserved among natural ATR13 variants . When we mutate one of these conserved residues , Y115/148N , from another recognized allele of ATR13 , Maks9 , we again abolished recognition by RPP13Nd , showing that the altered phenotype is not specific to the mutant ATR13 Emco5 ( data not shown ) . The disordered residues in the Emco5 allele of ATR13 flank an insertion present in other alleles , including Maks9 and Emoy2 , both of which we have observed in the nucleolus . To assess whether this 33 amino acid insertion was responsible for nucleolar targeting , we embedded this sequence at the analogous position in the Emco5 allele ( Figure 5A ) , usually excluded from the nucleolus and present in both nucleus and cytoplasm . We show that the addition of this 33 amino acid insertion results in a dramatic change in localization of the Emco5 allele—the chimeric form of ATR13 Emco5 becomes highly enriched in the nucleolus ( Figure 5B ) . To check if the deletion of this insertion in the Emoy2 allele abrogated nucleolar localization , we removed these 33 amino acids and determined that while still present to a lesser degree in the nucleolus , Emoy2 was now present throughout the nucleus , similar to the wildtype Emco5 localization pattern . Despite the change in localization of these two alleles , RPP13-Nd recognition remained unaltered; Emco5+NoLS is still recognized and triggers HR , whereas Emoy2-NoLS remains unrecognized ( Figure 5C ) . It is also worth noting that in addition to nuclear or nucleolar localization , the cytoplasm appears to undergo dramatic changes when any of the alleles of ATR13 ( Emco5 , Maks9 , and Emoy2 ) are expressed in planta . Relative to GFP , ATR13 appears to localize to distinct cytoplasmic strands , as well as to punctate bodies associated with these cytoplasmic strands and throughout the cytoplasm . The cytoplasmic patterning associated with ATR13 Emco5 is quite dramatic , displaying an abundance of punctate spots throughout the cytoplasm .
The structure of ATR13 from Emco5 was determined to moderate resolution using NOE , 1JHNHA scalar coupling , hydrogen exchange , and residual dipolar coupling data . The presence of significant disordered regions , somewhat poor magnetization transfer , and in some cases peak overlap , hampered our efforts to obtain a structure of higher resolution . Nevertheless , the structure was of sufficient quality to permit comparison with other proteins in the protein data bank . Despite very weak resemblance to several proteins including GTP-binding nuclear RAN , Beta-1 subunit importin , and a serine/threonine phosphatase 2A , the global fold of ATR13 from Emco5 appears to have no obvious homology to known proteins in the PDB database . In contrast to other RXLR proteins , ATR13 does not possess the core α-helical fold that is conserved in ATR1 , Avr3a11 , and PexRD2 [18] [19] . ATR13 is a highly polymorphic protein , yet only a small subset of the polymorphic residues appear to be involved in RPP13Nd-mediated recognition . As is the case with ATR1 , polymorphic residues of ATR13 appear as clusters across the surface of the protein . We have shown two major surface-exposed patches on the ATR13 structure that are highly polymorphic , yet only one of these regions appears relevant to RPP13Nd recognition . Previous studies implicate E ( 114 ) 147 , T ( 119 ) 152 , and R ( 148 ) 181 as being essential for full RPP13Nd recognition of the Wela3 and Maks9 alleles of ATR13 [11] . Here we show that ATR13 Emco5 recognition is mediated specifically by F/N73 and T/I ( 119 ) 152 substitutions , as determined in both loss and gain-of-recognition mutagenesis screens . When mapped onto the structure of ATR13 Emco5 , these two residues are in close proximity and are solvent-exposed , suggesting a surface-exposed patch that is required for RPP13-Nd recognition . Hall et al . have shown that several Arabidopsis accessions contain R-genes other than RPP13 that function in ATR13 recognition [28] . These other R-genes , as well as other functional alleles of RPP13 , may serve as a driving force behind other polymorphic patches in ATR13 . It will be interesting to see if recognition conferred by these R-genes is affected when residues in either surface-exposed patch of ATR13 are mutated , or if the same LOR and GOR mutants identified in this study maintain their phenotype in the context of this other R-gene . Many of the residue changes uncovered during the LOR random mutagenesis screen occurred in residues that are conserved in both recognized and unrecognized alleles of ATR13 . There are distinct differences in timing and intensity of hypersensitive response , implicating these residues in proper folding or stability . However , several mutants appear to accumulate protein to the wildtype level , and at least one of these mutations , Y ( 115 ) 148N , also alters RPP13-Nd recognition of ATR13 Maks9 . Notably , in several of the NMR models this tyrosine is proximal to N73 or T ( 119 ) 152 , suggesting it may directly affect the orientation and accessibility of these residues . The disordered loop is one of the most interesting features of the ATR13 structure . This portion of ATR13 Emco5 flanks one of four 11 amino acid direct repeats found in other alleles of ATR13 [11] . These other alleles are shown to localize to the nucleolus when expressed in planta , whereas ATR13 Emco5 does not . When these three missing direct repeats are added to the ATR13 Emco5 allele , the chimera relocalizes to the plant nucleolus , suggesting that this region is involved in nucleolar localization . Nucleolar localization is difficult to predict , as little data is currently available regarding how proteins are targeted to the nucleolus [29] , however several hallmarks of nucleolar localization signals ( NoLS ) include surface exposed coiled coil domains containing an abundance of lysines or arginines [30] . In the 33 amino acid stretch that defines the nucleolar targeting sequence , lysines and arginines account for nearly one-third of residue content . Additionally , regions of disorder often require one or several ligands for stabilization [30] . This region of ATR13 could potentially bind rRNA , rDNA , or a protein involved in nucleolar trafficking . Thus far , this is one of the only described examples of an oomycete protein localizing to the plant nucleolus . Moreover , it is functional in the host rather than in the originating pathogen , suggesting a signaling hierarchy; secretion and translocation across the host plasma membrane occurring prior to nucleolar targeting . The nucleolus is best known for its role in ribosome biosynthesis , yet it is also essential for regulating the cell cycle and the cellular response to stress . In humans a mere 30% of known nucleolar proteins play a role in ribosome biosynthesis , whereas the remaining 70% play various roles in cell maintenance , apoptosis , DNA replication and repair , cell cycle control , and stress signaling [31] . In plants , the nucleolus has been shown to be a target of several pathogen classes , including a groundnut rosette virus that recruits RNA processing machinery to produce viral RNP ( ribonucleoprotein ) particles needed for systemic infection [32] . For the Picorna-like Potato virus A , the nucleolar localization of one of its proteins , Nla , is required for completion of its infection cycle on Nicotiana [33] . Globodera pallida , a potato cyst nematode , has also been shown to target the nucleolus during various life stages presumably to suppress host defense [34] . Interestingly , Gilroy et al . ( 2011 ) show that the host protein CMPG-1 , an E3 ligase involved in resistance signaling , accumulates in the nucleolus when the P . infestans effector Avr3a is transiently expressed in N . benthamiana [35] . With the varied roles the nucleolus plays in directing cellular activities , it seems an attractive target for an intercellular obligate biotroph requiring compromised host defense and a steady supply of nutrients . In light of our findings , examining the role of the nucleolus in oomycete pathogenesis is an area that requires further exploration . Knowing that several alleles of ATR13 are localized to the nucleolus , a structure necessary for a variety of cellular processes including the cellular stress response , we might look more closely at its role during pathogenesis and determine if known nucleolar controlled stress responses are altered upon challenge with pathogen-delivered ATR13 . In addition to its nuclear and nucleolar localization , ATR13 appears to localize to the cytoplasmic scaffolding and to discrete punctate spots along these strands . As an obligate biotroph , nutrient acquisition is one of the key factors influencing survival and success of the invading pathogen . To that end , hijacking cellular transport machinery would be an effective strategy for funneling nutrients from plant host to obligate pathogen . The various cellular localizations of ATR13 suggest it may possess multiple roles in pathogenesis , much like the EspF effector from Escherichia coli which has been shown to target the mitochondria , nucleolus , and cytoplasm of infected mammalian cells [36] . In this study we solve the structure of ATR13 , a structurally flexible and highly polymorphic effector protein from Hpa . We infer that its maintenance in Hpa , in spite of the drive to evade host recognition by RPP13 , illustrates its importance in pathogen virulence—especially in the context of Hpa's abbreviated effector repertoire [37] . We identify two ATR13 residues essential for robust HR in the presence of RPP13Nd . We map these residues onto our structure and show that they localize to a single solvent-exposed patch which corresponds to an area under high diversifying selection . Lastly , we show that the highly flexible internal loop we identified based on our NMR data plays a role in nucleolar localization and can be added to a non-nucleolar protein to redirect that protein to the nucleolus .
pET-DUET1 constructs were transformed into chemically competent Rosetta ( DE3 ) pLysS E . coli ( Novagen ) , and selected on LA plates containing 50 ug/ml carbenicillin . Single colonies were used for overnight starter cultures and diluted to an OD of 0 . 1 in LB +carb the following morning . These cultures were incubated at 37°C and agitated at 250 rpm until reaching an OD of 0 . 55 . Induction was initiated by the addition of IPTG to a final concentration of 500 uM . Cultures were induced for 16 h at 28°C and 250 rpm and cells were harvested by centrifugation at 3 , 000 rpm . Cells were resuspended in a small volume of buffer A ( 20 mM phosphate buffer pH 7 . 2 , 20 mM imidazole , 0 . 5 M NaCl , 10% glycerol ) , snap frozen in liquid nitrogen , and stored at −80°C . Overnight starter cultures were prepared as described above , spun down at 3 , 000 rpm for 15 minutes and washed once in M9 minimal media . For NMR experiments , uniformly 15N-labeled and uniformly 15N/13C-labeled ATR13 were expressed in E . coli using M9 minimal medium containing either 15N-labeled ammonium chloride , or 15N-labeled ammonium chloride and 13C-labeled glucose ( Cambridge Isotopes Laboratories ) . A 10% fractionally 13C-labeled sample was prepared by growing the bacteria in medium containing 10% 13C-labeled glucose . Protein yields ranged from 20 to 25 mg per liter . Frozen cell suspensions were thawed and incubated with 10 ug/ml of lysozyme on ice for 30 minutes . Cells were sonicated at 30% duty cycle , 30% output for three 30 second bursts , and cell debris was spun down at 19 , 000xg for 20 minutes . Lysate was filtered and loaded onto an equilibrated 5 ml Nickel column ( GE Healthcare ) , washed with 100 ml of buffer A , and eluted in 2 ml fractions from the column using an imidazole gradient ( final concentration 200 mM in buffer A ) . Fractions were run on SDS-PAGE gels and visualized using Coomassie stain . Those containing ATR13 were pooled and incubated with 6His-TEV protease overnight at 4°C while dialyzing against buffer A to remove imidazole added during elution . The TEV digest was then loaded onto an equilibrated nickel column and flow through containing cleaved ATR13 was collected; other contaminants and uncleaved 6His-ATR13 remained bound to the column . The flow through was then concentrated to a volume of 500 ul resulting in a 1 mM to 3 mM protein solution using a Millipore spin column ( 3 , 000 MW ) and dialyzed against 20 mM phosphate buffer pH 7 . 1 , 150 mM NaCl . Four aliquots of 1 mg/ml 6His-Δ19 ATR13 Emco5 protein in 500 ul were sent to Covance Inc . ( Princeton , NJ ) for custom antibody production . Two New Zealand white rabbits were used in the standard 118-day protocol and bleeds were checked against purified ATR13 protein on a dot blot . Antibody was enriched by affinity purification using ATR13 conjugated to CnBr-Sepharose 4B according to manufacturer's instructions ( GE Healthcare ) . Protein samples were prepared for NMR experiments by dissolving lyophilized protein in buffer containing 20 mM sodium phosphate pH 7 . 1 , 150 mM sodium chloride , and 5% D2O . The final protein concentration for each sample was approximately 1 mM . All spectra were recorded at 25°C on a Bruker Avance 500 MHz instrument equipped with a room temperature probe , unless stated otherwise . NMR data were processed with NMRPipe [38] and were analyzed using CARA [39] . Backbone assignments were made with standard 3D heteronuclear NMR experiments including HNCACB , CBCA ( CO ) NH , HNCO , HN ( CA ) CO , as well as a 3D 15N NOESY-HSQC ( 100 ms mixing time ) [40] , [41] . The latter experiment was acquired on a Bruker 800 MHz instrument equipped with a room temperature probe . Sidechain 1H/13C signals were assigned with HCCH-TOCSY , ( H ) CCH-TOCSY , and ( H ) CCH-COSY experiments and a 1H-15N TOCSY-HSQC spectrum ( 60 ms mixing time ) , and were confirmed with ( H ) C ( CO ) NH , and H ( CCO ) NH experiments , as well as a HCCH-COSY recorded at 800 MHz [40] , [41] . Magnetization transfer in ( H ) C ( CO ) NH , H ( CCO ) NH and 1H-15N TOCSY–HSQC spectra was poorer than would be expected for a 12 kDa protein , indicating some dynamics or transient protein-protein interactions . Phi torsion angle restraints were derived from 3JHNHA couplings obtained from an HNHA spectrum [42] . Stereospecific assignments for the methyl groups of 2 of 4 valine and 5 of 8 leucine residues were obtained by comparison of 1H-13C HSQC spectra of 10% and fully 13C-labeled samples [43] . NOEs were identified in the 3D 1H-15N NOESY-HSQC spectrum and a 1H-13C NOESY-HSQC spectrum ( 85 ms mixing time ) recorded on a Bruker Avance II 900 MHz instrument equipped with a cryoprobe . Residual dipolar couplings were measured from IPAP spectra [44] recorded on a 15N –labeled sample dissolved in buffer containing 12 mg/ml of Pf1 phage ( Asla Biotech Ltd , Riga , Latvia ) . Tensor parameters were determined from a histogram of the couplings and values based on intermediate structures [45] . The magnitude of the alignment tensor and rhombicity were set to – 11 Hz and 0 . 3 , respectively . Qualitative backbone dynamics information was obtained from a 1H-15N heteronuclear NOE experiment [46] . Initial structures were calculated with Cyana ( version 2 . 1 ) [47] . Residual dipolar coupling data was included in the final rounds of refinement using CNS ( version 1 . 3 ) [48] . Structures were viewed and analyzed using MOLMOL [49] . In the calculations , NOEs were classified qualitatively as strong ( 1 . 8–2 . 7 Å ) , medium , ( 1 . 8 – 3 . 5 Å ) or weak ( 1 . 8–5 . 0 Å ) , and Phi torsion angles were constrained to −60±30 deg for 3JHNHA values less than 6 Hz . Hydrogen bonds were identified on the basis on NOEs and slow amide proton exchange rates ( protection factors greater than 100 [50] ) . Constraints were applied between HN and O atoms ( 2 . 8–3 . 3 Å ) and between N and O atoms ( 1 . 8–2 . 3 Å ) . Force constants for NOEs , dihedral angles , and hydrogen bonds were set to default values . Force constants for HN residual dipolar couplings were set to 0 . 7 Kcal mole−1 Hz−1 to yield r . m . s . d . s equal to the uncertainties in the measurements ( ∼ 1 Hz ) . Assignments have been submitted to the BioMagResBank under accession number RCSB10216 and the 20 of 200 structures with the lowest energies have been deposited in the Protein Data Bank under accession number 2LAI . Site-directed mutants were generated using the Quikchange Lightning Site-Directed Mutagenesis kit ( Stratagene ) as per the manufacturer's instructions . For loss of function mutant screen , pENTR/D-Δ41 ATR13 Emco5 was subjected to random PCR mutagenesis using M13 primers and the Diversify Mutagenesis kit ( Clontech ) under buffer condition 4 , as described in the product manual . For gain of function mutagenesis , pENTR/D-Δ41 ATR13 Emoy2 was used as template under the same conditions . PCR product from both reactions was gel purified and recombined into the pEarleygate 202 vector [51] using LR clonase , transformed into maximum efficiency DH5α ( Invitrogen ) , and plated out on LA with kanamycin selection 25 ug/ml . The following day , colonies were harvested , miniprepped , and transformed into electrocompetent Agrobacterium tumefaciens GV3101 . 1 , 200 loss of function GV3101 colonies were resuspended in induction medium ( 0 . 1 mM MES pH5 . 6 , 0 . 1 mM MgCl2 , 0 . 1 mM Acetosyringone ) to an OD between 0 . 3–0 . 7 . After 3 hours at room temperature , suspensions were inoculated onto transgenic Nicotiana benthamiana containing RPP13Nd . Plants were scored for altered hypersensitive response at 24 h , 48 h , and 72 h post inoculation . 800 gain of function GV3101 colonies derived from the ATR13 Emoy2 allele were screened in an identical fashion as described above . Emco and Emoy NoLS chimeras were generated using two-step PCR fusions . For the Emco + NoLS construct , 5′ and 3′ portions of Emco were amplified from pENTR-Δ41ATR13 Emco using the following primers: 5′ caccatggcagccgccagcgaa 3′ , 5′ ctctataatcttctcgt ggatgcctttagc 3′ and 5′ gcacacgatcttcatgtctccaaatctaa 3′ , 5′ ctgtctgtcaagagca 3′ . The NoLS insert was amplified using pENTR-Δ41ATR13 Emoy as template with the following primers: 5′cgagaagattatagaggcatacgatcgtca 3′ and 5′catgaagatcgtgtgccatcttagatttgg 3′ . Products from these three reactions were run on a high percentage agarose gel and purified by expected size using the Qiaquick Gel Extraction kit from Qiagen . The purified PCR products were pooled and used as template with the following primers: 5′ caccatggcagccgccagcgaa 3′ and 5′ ctgtctgtcaagagca 3′ . Product was gel purified and cloned into pENTR via the TOPO reaction ( Invitrogen ) . The Emco+NoLS fusion was then cloned into pEG103 using LR clonase ( Invitrogen ) . For the Emoy –NoLS construct , 5′ and 3′ regions of ATR13 Emoy were amplified using pENTR-Δ41ATR13 Emoy with the following primer sets: 5′ caccatggcag ccgccagcgaa 3′ , 5′catgacgatcgtgtgcct ttataatcttctcgtggatgcc 3′ , and 5′cgagaagattataaaggcacacgatcgtcatg tctccaaa 3′ , 5′ctgactggcaacggc 3′ . Product from these reactions was pooled and amplified using the following primer pair: 5′caccatggcagccgccagcgaa 3′ , 5′ctgactggcaacggc 3′ . pEG103-ATR13Emoy –NoLS was obtained by following the procedure described above . Images were obtained using an LSM 710 Confocal from Carl Zeiss , Inc . Pictures were taken with 40x or 60x objectives using whole leaf mounts of N . benthamiana expressing ATR13 . Images were processed with ImageJ [52] .
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Understanding how pathogenic microbes suppress host defenses and extract host nutrients is crucial to engineering methods to manage pathogen spread . By delivering an arsenal of proteins called effectors into the host , pathogens can overcome various counter measures taken by plants and animals to control pathogen proliferation . The key to deciphering how these pathogens manipulate their hosts is to determine the function of each effector and to evaluate its role in pathogen virulence . In the case of oomycetes , effectors share little sequence similarity to any known proteins; therefore , structural and functional predictions are difficult . By solving the structure of ATR13 , we are able to contribute a protein structure to the PDB database and the scientific community at large . Our structure reveals the unique fold of our protein and illustrates how different evolutionary driving forces have shaped the surface topography of ATR13 . Additionally , our structure allows us to identify a peptide sequence that plays a role in nucleolar transport , permitting us to inform nucleolar localization prediction programs about oomycete targeting sequences .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"chemistry",
"biology",
"agriculture"
] |
2011
|
Structural Elucidation and Functional Characterization of the Hyaloperonospora arabidopsidis Effector Protein ATR13
|
The mosquito Aedes aegypti , the principal vector of dengue virus , has recently been infected experimentally with Wolbachia: intracellular bacteria that possess potential as dengue biological control agents . Wolbachia depend on their hosts for nutrients they are unable to synthesize themselves . Consequently , competition between Wolbachia and their host for resources could reduce host fitness under the competitive conditions commonly experienced by larvae of Ae . aegypti in the field , hampering the invasion of Wolbachia into natural mosquito populations . We assess the survival and development of Ae . aegypti larvae under starvation conditions when infected with each of three experimentally-generated Wolbachia strains: wMel , wMelPop and wAlbB , and compare their fitness to wild-type uninfected larvae . We find that all three Wolbachia infections reduce the survival of larvae relative to those that are uninfected , and the severity of the effect is concordant with previously characterized fitness costs to other life stages . We also investigate the ability of larvae to recover from extended food deprivation and find no effect of Wolbachia on this trait . Aedes aegypti larvae of all infection types were able to resume their development after one month of no food , pupate rapidly , emerge at a large size , and exhibit complete cytoplasmic incompatibility and maternal transmission . A lowered ability of Wolbachia-infected larvae to survive under starvation conditions will increase the threshold infection frequency required for Wolbachia to establish in highly competitive natural Ae . aegypti populations and will also reduce the speed of invasion . This study also provides insights into survival strategies of larvae when developing in stressful environments .
Dengue fever is an increasing threat to global health . An estimated 50 to 390 million new cases of dengue occur annually , with 2 . 5 billion people living in areas at risk of infection [1 , 2] . At present , dengue lacks an effective treatment or vaccine that protects against all serotypes of the virus . Thus , strategies to reduce infection incidence must rely on the control of its mosquito vector , principally Aedes aegypti [3 , 4] . While permanent eradication is unlikely to be achieved , many emerging genetic and biological approaches aim to reduce mosquito vectorial capacity [5 , 6] . A promising new approach to dengue control utilizes the obligate intracellular bacterium , Wolbachia . Wolbachia are maternally inherited [7] and usually manipulate the reproduction of their hosts to enhance their own transmission [8] . The most common manipulation induced by Wolbachia is cytoplasmic incompatibility; a mechanism where embryonic lethality occurs when an infected male mates with a female that is not infected with Wolbachia , providing infected females with a relative reproductive advantage [9 , 10] . Many Wolbachia infections also provide protection to their host against pathogens , including RNA viruses [11–13] . These traits have enabled Wolbachia to be implemented in strategies to both suppress [14 , 15] and replace [16–18] insect populations . While Ae . aegypti does not harbour a natural Wolbachia infection [19 , 20] , three infections have been stably introduced into the vector: the wMelPop and wMel strains originating from Drosophila melanogaster [21 , 22] and wAlbB from the mosquito Aedes albopictus [23] . All three infections are transmitted vertically at high rates and exhibit complete cytoplasmic incompatibility [21–23] , and these effects have remained stable after many years in the novel host [24–26] . Crucially , they also suppress the replication and transmission of dengue virus in Ae . aegypti [22 , 27 , 28] , giving them potential to reduce dengue incidence in transformed populations . Establishment of Wolbachia in a field population is facilitated largely by maternal transmission and cytoplasmic incompatibility [16 , 29 , 30] . However , because Wolbachia-infected mosquitoes must survive and reproduce in competition with the native inhabitants , lower relative fitness of infected mosquitoes can hamper the invasibility of Wolbachia [31–33] . The experimental Wolbachia infections established in Ae . aegypti vary considerably in their effects on mosquito life-history traits . The wMel infection is relatively benign and has invaded both caged [22] and field [18] populations . wMel remains at a high frequency in mosquitoes collected from the release sites , three years after releases of wMel ceased in two suburbs of Cairns , Australia[24] . Conversely , the wMelPop infection tends to overreplicate in host cells , leading to rapid tissue degeneration and early death [34–36] . It exacts a high fitness cost on Ae . aegypti; wMelPop shortens adult lifespan [21 , 37] , while fecundity [38] , blood feeding success [39 , 40] and quiescent egg viability [37 , 38 , 41] deteriorate rapidly with age . wMelPop also modifies behaviour and metabolism [42] , reduces the response of larvae to light stimulation [43] , delays larval development , and decreases viability and adult size when reared under crowded conditions [44] . The wAlbB infection has intermediate fitness costs , likely due to its moderate density in host tissues that lies between that of wMel and wMelPop [26] . While each of these infections can invade caged populations of Ae . aegypti [22 , 23 , 26] , the mosquitoes were not exposed to many of the selective pressures that exist in the field [6] . Suitable habitats for immature development in the field are limited; as a consequence , larvae are often subjected to competition for space and nutrition [45–48] . Though Wolbachia infection has no clear effect on Ae . aegypti larval development in the absence of stress [22 , 26 , 37 , 38] , some costs emerge when larvae are crowded [44] . Many fitness costs of Wolbachia in Ae . aegypti also tend to become clearer with age in both adults and eggs [26 , 37 , 39] . As larval development times can reach several weeks , or even months in the field [49] and often experience periods of food limitation [47 , 48] , deleterious effects of Wolbachia on larvae undetected in laboratory studies could emerge when development times are prolonged , impacting Wolbachia’s invasive potential . This could explain a lack of invasion success by wMelPop in natural populations despite multiple attempts to establish the infection in the field [50] . Aedes aegypti larvae are adapted to nutrient poor-habitats as food limitation is a major regulator of their population size [47 , 51] . Larvae decrease their rate of development in response to food scarcity , delaying metamorphosis until reaching a critical threshold of nutritional reserves [52–55] , and larvae can resist starvation for several weeks at a time [51 , 56–58] . This is achieved largely by expending their accumulated reserves [59–61] , though larvae also scavenge on dead conspecifics [62 , 63] and may even prey on younger larvae [64] to increase their chance of survival . Wolbachia depend on their hosts for a wide range of resources they cannot synthesize themselves [65–68] . Since Wolbachia increase the activity and metabolic rate of Ae . aegypti in adults , at least for the wMelPop infection [42] , we hypothesize that Wolbachia may also increase the rate at which energy reserves are depleted in larvae without food . Aedes aegypti breeding containers typically have low productivity and high food intermittency because leaf litter , animal detritus and the microorganisms that break them down are the primary source of nutrition [62 , 69 , 70] . Thus , the ability to survive periods of limited food is a critical aspect of larval fitness [47 , 51] . In the field , competition between Wolbachia and Ae . aegypti for resources could substantially reduce the survival of larvae , limiting the potential for Wolbachia to invade and persist in natural populations . In this study we investigate the effects of wMel , wAlbB and wMelPop infection on the ability of Ae . aegypti larvae to survive and develop under extreme nutrient stress . We compare the survival and development of Wolbachia-infected and uninfected larvae under starvation conditions when held in groups , when infected and uninfected larvae are together in the same container , or when isolated , and test their ability to recover when an influx of resources is provided . We also examine the ability of Wolbachia to express their reproductive effects when Ae . aegypti larvae are held under starvation conditions for extended periods . We then consider the likely impact of any fitness costs imposed by Wolbachia on the potential for these infections to invade highly competitive populations .
Aedes aegypti mosquitoes were sourced from Cairns , Queensland and maintained under laboratory conditions for at least two generations before use in experiments . Wolbachia-infected lines were generated by crossing male uninfected Cairns mosquitoes to laboratory-reared female mosquitoes infected with wMel [22] , wAlbB [23] or wMelPop [21] to maintain a similar genetic background ( >98% ) between colonies . Mosquitoes were kept in the laboratory at 26°C ± 1°C and 80–90% relative humidity with a 12:12 light: dark photoperiod , and maintained according to methods described by Axford et al . [26] . Within one week of emerging , female adults were allowed to feed to repletion on the forearm of a single human volunteer . Blood feeding of female mosquitoes on human volunteers for this research has been approved by the University of Melbourne Human Ethics Committee ( approval 0723847 ) . All adult subjects provided informed written consent ( no children were involved ) . Larvae were reared under a common regime before initiating the food-deprivation period for all experiments . At the beginning of each experiment , wMel-infected , wMelPop-infected , wAlbB-infected and uninfected eggs were hatched synchronously in separate trays containing 3 L of RO ( reverse osmosis ) water , 2–3 grains of yeast and one crushed tablet of TetraMin tropical fish food ( Tetra , Melle , Germany ) . Within three hours of hatching , cohorts of 200 1st instar larvae were transferred to plastic trays filled with 700 mL of RO water and fed TetraMin ad libitum for 72 hours . This rearing environment was chosen as development times do not differ significantly between Wolbachia-infected and uninfected larvae with abundant nutrition at this density . After the feeding period , larvae were pipetted into fresh trays of RO water . To remove any remaining food particles , larvae were rinsed by passing them through two additional trays of water before being added to experimental containers . All experiments used 72 hour old 3rd instar larvae of approximately the same size , and were conducted at 26°C ± 1°C and 80–90% relative humidity with a 12:12 light: dark photoperiod . We tested the ability of Wolbachia-infected and uninfected larvae to survive starvation conditions in the absence of conspecific larvae , removing any effects of resource competition and also the ability to scavenge on dead larvae . Two independent experiments were conducted; in each , 96 larvae per infection type ( see rearing regime ) were added individually to wells of Costar 12-well cell culture plates ( Corning , Corning , NY ) filled with ~4 mL of RO water only . Plates were enclosed in stockings and held in a tray covered with a mesh lid to minimize external sources of food input , and RO water was topped up daily to counter evaporation . For both experiments , wells were monitored for mortality daily until all larvae had died . A larva was considered dead when no movement was observed after fifteen seconds of physical stimulation . In the first experiment , plates were unmanipulated with the exception of maintaining a consistent volume of water in each well . In the second experiment , water was replaced completely twice per week to reduce the accumulation of microorganisms as a potential source of nutrition ( e . g . , bacteria , algae , protozoa , fungi ) and waste in the water [73] . For this experiment , larvae were removed from wells and rinsed by pipetting through multiple trays of RO water , then returned to wells filled with a fresh change of water . Two independent experiments tested the ability of Wolbachia-infected and uninfected larvae to survive starvation conditions when held in the presence of conspecific larvae . Larvae ( see rearing regime ) were added to circular plastic containers ( 9 . 5–11 . 5 cm radius , 7 cm height ) with mesh lids and filled with 200 mL of RO water only ( no TetraMin was provided ) . Mortality was scored every second day by temporarily pipetting larvae into a separate container of RO water . Numbers of dead and live larvae were counted before all larvae ( including dead larvae ) were returned to the original container . Water was refreshed every four days by transferring all larvae to a new container of RO water . In the first experiment , larvae were added to containers in groups of 50 . Each container was replicated eight times for the uninfected , wMel , wAlbB and wMelPop strains . The experiment was terminated when all larvae had died or had reached adulthood . During field releases , preferential mortality of Wolbachia-infected larvae in nutrient-deprived containers could release the remaining larvae from food stress , providing an advantage to uninfected larvae [71 , 72] . A second experiment was therefore conducted to determine whether there were differences in survival when Wolbachia-infected and uninfected larvae were held together in mixed proportions within the same container . Cohorts of larvae were added to plastic containers filled with 200 mL of RO water in the following proportions ( Wolbachia-infected to uninfected ) : 12:36 , 24:24 and 36:12 . Additional cohorts of 48 Wolbachia-infected and 48 uninfected larvae were set up as controls . Treatments ( mixed proportions ) were replicated eight times each , while the controls ( pure cohorts ) were replicated four times , and the experiment was repeated for the wMel , wAlbB and wMelPop infections . Containers were monitored as per the previous experiment , with the exception that the five longest surviving larvae in each container were removed and screened for their Wolbachia infection status ( see DNA extraction and Wolbachia detection ) . The proportion of individuals infected with Wolbachia in the longest surviving larvae was then compared with the initial proportion of larvae infected with Wolbachia in each container ( see statistical analysis ) . In both experiments , a few percent of larvae were able to reach the pupal and adult stages due to the availability of dead conspecific larvae as a food resource . All adults emerging throughout the two group experiments were stored in ethanol for wing length measurement and later tested for their Wolbachia infection status ( see wing length measurements and DNA extraction and Wolbachia detection ) . Their development time and sex were also recorded . An experiment was carried out to test the ability of Wolbachia-infected and uninfected larvae to recover from starvation conditions after providing an influx of resources . Larvae ( see rearing regime ) were added to RO water in groups of 50 ( see survival and development of larvae held in groups under starvation conditions ) . Containers were then divided into two treatments; larvae were re-fed TetraMin ad libitum after either 15 or 25 days of surviving starvation conditions . These two time points were chosen based on when substantial starvation-induced mortality had occurred; approximately 25% and 10% of larvae were remaining on Days 15 and 25 respectively ( S1 Fig ) . For each infection type and treatment , the following observations were recorded: the number of surviving larvae upon the resumption of feeding , rates of pupation and survival to the pupal stage after re-feeding , rates of adult emergence and survival to adulthood , and the body size ( see wing length measurements ) and sex ratio of emerging adults . Containers were replicated between six and eight times for each infection type and treatment . We ran a series of experiments to determine if the reproductive effects caused by Wolbachia remain robust when larvae are held under starvation conditions for an extended period . To test the level of cytoplasmic incompatibility induced by Wolbachia-infected males , larvae ( see rearing regime ) were added to containers of RO water and their development was suspended for ~30 days by maintaining them in the absence of TetraMin . After this period larvae were again fed TetraMin ad libitum until pupation . Pupae were sexed ( males are smaller than females ) , and male pupae pipetted into small cups of RO water and allowed to emerge in 1 . 5 L plastic containers with mesh sides and a stocking lid . Female pupae emerging from this treatment were set aside for an additional experiment on reproductive effects ( see fecundity and maternal transmission ) . After confirming the sex of all males as adults , newly-emerged uninfected females that were reared under standard laboratory conditions ( see colony maintenance and mosquito strains ) were added to each cage and allowed to mate freely with Wolbachia-infected males . Seven Wolbachia-infected males and seven uninfected females were held in each experimental cage , and crosses were replicated eight times for the wMel , wAlbB and wMelPop infections . Cages of adults were provided access to 10% sucrose solution and water throughout the experiment . Crosses between standard laboratory-reared Wolbachia-infected males and uninfected females were set up as controls , as these crosses are known to produce no viable offspring [21–23] . Females were then blood fed and eggs were collected according to Axford et al . [26] for three gonotrophic cycles . This experiment assessed the rate at which Wolbachia-infected females transmit the infection to their offspring when their development time is greatly extended . Food-deprived and re-fed larvae from the wMel , wAlbB and wMelPop lines ( see cytoplasmic incompatibility ) were sorted by sex , and 100 females per infection type were added to 12 L plastic cages and provided with 10% sucrose solution and a source of water . 100 uninfected males reared under standard laboratory conditions were then aspirated into each cage and allowed to mate freely . Females were then blood fed and isolated for oviposition according to Axford et al . [26] , and their progeny reared to adulthood and stored in absolute ethanol . Ten progeny each from 30 isolated females per infection type were tested for the presence of Wolbachia using PCR to determine maternal transmission efficiency ( see DNA extraction and Wolbachia detection ) . A set of control crosses was also completed for each infection type where both Wolbachia-infected females and uninfected males were reared under standard laboratory conditions . Ten progeny from 15 Wolbachia-infected females were tested for each of the wMel , wAlbB and wMelPop infections . These crosses have expected maternal transmission rates of close to 100% [21–23] . All female parents from the treatments and controls were scored for their fecundity , with a sample also measured for wing length ( see wing length measurements ) . Data from uninfected females reared under standard laboratory conditions from a concurrent experiment were included as a point of comparison . Linear measurements of wings were taken to give an indication of body size [74 , 75] . The right wing was removed from each adult and fixed on a slide under a 10 mm circular coverslip ( Menzel-Gläser , Braunschweig , Germany ) using Hoyer’s solution ( dH2O: gum arabic: chloral hydrate: glycerin in the ratio 5: 3: 20: 2 ) [76] . Wings were observed under a dissecting microscope fitted with a camera and measured using NIS-Elements BR ( Nikon Instruments , Japan ) . Wing length was determined by calculating the distance from the alular notch to the intersection of the radius 3 vein and outer margin , excluding the wing fringe scales [77] . Measurements in pixels were converted to millimetres by calibration with a graticule before the start of each set of measurements . Each measurement was repeated independently so that length represented the average of two measurements . Damaged or folded wings were excluded from the analysis . To test for the presence of Wolbachia in adult and immature mosquitoes , we carried out DNA extraction and Wolbachia detection according to methods described previously [24 , 26 , 78] . DNA from whole adults or larvae was extracted using 150 μL of 5% Chelex 100 resin ( Bio-Rad Laboratories , Hercules , CA ) . The PCR assay was conducted using a LightCycler 480 system ( Roche Applied Science , Indianapolis , IN ) ; mosquitoes were considered positive for Wolbachia when the mRpS6 ( Aedes universal ) and aRpS6 ( Ae . aegypti-specific ) primer sets were successfully amplified in addition to the appropriate Wolbachia-specific primer set ( wMel , wAlbB or wMelPop ) . Wolbachia-free mosquitoes tested positive for mRpS6 and aRpS6 and negative for all Wolbachia-specific primer sets . All data were analysed using SPSS statistics version 21 . 0 for Windows ( SPSS Inc , Chicago , IL ) . Survival data were investigated using Kaplan-Meier analysis; log-rank tests compared rates of mortality between lines and treatments . Wolbachia infection frequency was calculated as the proportion of individuals that tested positive for Wolbachia . For containers where both Wolbachia-infected and uninfected larvae were present , deviations from expected infection frequencies in larvae and adults were analysed using Chi-squared tests . Maternal transmission rates of Wolbachia were expressed as the proportion of infected offspring produced by infected mothers , for which 95% binomial confidence intervals were calculated . All other data were tested for normality using Shapiro-Wilk tests . Data that were not normally distributed were arcsine square-root transformed ( proportional data ) or square-root transformed and tested again . Normally distributed data were then analysed with one-way ANOVA and Tukey’s honest significant difference tests , while data that failed Shapiro-Wilk tests were analysed with non-parametric Kruskal-Wallis and Mann-Whitney U tests . Associations between wing length and development time were assessed with Pearson’s correlation if data were normally distributed or Spearman’s rank-order correlation where data could not be transformed for normality .
Kaplan-Meier ( KM ) analysis revealed a significant effect of Wolbachia infection type ( KM: χ2 = 123 . 273 , df = 3 , P < 0 . 0001 ) and water-replacement regime ( KM: χ2 = 678 . 532 , df = 1 , P < 0 . 0001 ) on the survival of larvae when isolated under starvation conditions . Whether water was refreshed in each well or left unmanipulated had a dramatic effect on survival , with the former ( mean ± SE = 20 . 682 ± 0 . 221 days ) reducing the mean survival time of larvae by half compared with unmanipulated experimental wells ( 40 . 286 ± 0 . 573 days , S2 Fig ) . An increased survival in the latter experiment is likely due to the build-up of microorganisms which are an important resource for mosquito larvae [70 , 73 , 79] . When water was not replaced , all three Wolbachia infections reduced survival; the wMel , wAlbB and wMelPop infections decreased mean survival 15 . 8 , 28 . 8 and 28 . 7% compared with uninfected larvae ( Fig 1A ) . All pairwise comparisons between the infection types were highly significant ( KM: all χ2 > 24 . 087 , df = 1 , all P < 0 . 0001 ) , with the exception that wMelPop and wAlbB did not differ significantly in their survival patterns under starvation conditions ( χ2 = 0 . 717 , df = 1 , P = 0 . 397 ) . Although there was a significant effect of Wolbachia infection type in both experiments , survival differences between Wolbachia-infected and uninfected larvae were reduced markedly when water was replaced every four days ( KM: χ2 = 17 . 939 , df = 3 , P = 0 . 0005 ) compared with wells that were unmanipulated ( χ2 = 150 . 024 , df = 3 , P < 0 . 0001 , Fig 1 ) . When water was replaced , all pairwise comparisons between infection types were significant ( KM: all χ2 > 4 . 262 , df = 1 , all P ≤ 0 . 039 ) except for between uninfected and wMel ( KM: χ2 = 1 . 707 , df = 1 , P = 0 . 191 ) , and wAlbB and wMelPop ( KM: χ2 = 0 . 630 , df = 1 , P = 0 . 427 ) ( Fig 1B ) . No pupae or adults emerged in either experiment where larvae were isolated . Wolbachia infection type also had a substantial effect on survival when larvae were held under starvation conditions in groups of 50 ( KM: χ2 = 225 . 821 , df = 3 , P < 0 . 0001 ) . Uninfected larvae had the greatest mean time of survival ( mean ± SE = 28 . 289 ± 0 . 532 days ) , with the wMel , wAlbB and wMelPop infections reducing survival times by 5 . 7 , 15 . 7 and 29 . 5% respectively ( Fig 2 ) . All pairwise comparisons between lines were significant ( KM: all χ2 > 7 . 411 , df = 1 , all P ≤ 0 . 006 ) . Note that emerging adults were excluded from Kaplan-Meier analyses rather than censored because the rate and number of adults emerging differed between infection types . Larvae from both Wolbachia-infected and uninfected lines readily consumed dead conspecifics throughout the experiment . We inferred scavenging based on observations that the number of dead larvae in each container fluctuated with mortality rather than increasing proportionally ( S3 Fig ) . Distributions of necrophagy closely matched larval mortality , with the mean time for larval consumption occurring less than one day after the mean time of death for both Wolbachia-infected and uninfected lines ( S4 Fig ) . Necrophagy likely contributed to increased survival time; larvae lived for longer in groups compared with larvae kept in isolation under otherwise similar conditions . While survival began to decline earlier in the group experiment , rates of mortality became considerably slower when the majority of larvae had died ( S2 Fig ) . Less than five percent of larvae reached pupation or adulthood during this experiment ( Table 1 ) . Wolbachia infection type had a significant effect on the total number of larvae that survived to both the pupal ( one-way ANOVA: F3 , 28 = 3 . 417 , P = 0 . 031 ) and adult ( F3 , 28 = 5 . 647 , P = 0 . 004 ) stages , and also affected the development times of those pupae ( Kruskal-Wallis: χ2 = 31 . 499 , df = 3 , P < 0 . 0001 ) and adults ( χ2 = 14 . 200 , df = 3 , P = 0 . 003 ) . Despite uninfected larvae having greater survival times under starvation conditions ( Fig 2 ) , they developed more slowly and pupated less often than Wolbachia-infected larvae , with the wMelPop infection displaying the greatest proportion of larvae reaching adulthood and the most rapid development on average ( Table 1 , S5 Fig ) . This observation is likely due to an earlier availability and greater abundance of conspecific carcasses as a source of nutrition in containers with wMelPop-infected larvae . A second experiment was conducted where Wolbachia-infected and uninfected larvae were held together in the same container under starvation conditions . Control containers , where 48 larvae from each infection type were held separately , had a shorter starved survival period than in the previous experiment despite nearly identical methods , though the relative performance of each infection type was similar ( S2 Fig ) . In each treatment container , the five longest-lived larvae were screened for their infection status to test for differential survival between infected and uninfected larvae when held together at different frequencies . The wAlbB and wMelPop infections were significantly underrepresented in the surviving larvae for all treatments , while for wMel there were no significant deviations from any starting ratio ( Table 2 ) . Less than two percent of larvae from this experiment emerged as adults . Expected ratios of Wolbachia-infected and uninfected adults emerging were based on the initial proportion of larvae in each container . We found no significant deviations from expected proportions of adults for all treatments ( Chi-squared test: all χ2 < 3 . 267 , df = 1 , all P > 0 . 071 ) , except for the wMelPop infection which was significantly underrepresented when larvae were held in the ratio 36:12 ( wMelPop: uninfected ) ( Chi-squared test: χ2 = 24 . 2 , df = 1 , P < 0 . 0001 ) . All adults that emerged from larvae held in groups were measured for wing length to test for effects on body size . Due to low numbers of adults , data were pooled across both experiments as they did not differ significantly ( Student’s t test: P = 0 . 795 ) . Wing length was not associated with development time for either males ( Spearman’s rank-order correlation: ρ = 0 . 071 , P = 0 . 455 , n = 56 ) or females ( ρ = -0 . 009 , P = 0 . 924 , n = 58 ) . As expected , there was a significant effect of sex on wing length ( one-way ANOVA: F1 , 106 = 285 . 910 , P < 0 . 0001 ) , where males ( mean ± SE = 1 . 659 ± 0 . 009 mm ) were considerably smaller than females ( 1 . 973 ± 0 . 015 mm ) . However , we found no effect of Wolbachia infection type ( one-way ANOVA: F3 , 106 = 0 . 360 , P = 0 . 782 ) ; wings of mosquitoes with any infection type were approximately the same size ( Table 3 ) . 25 . 5% and 12 . 3% of larvae across all infection types survived after 15 and 25 days of exposure to starvation conditions respectively . Wolbachia infection type had a significant effect on the number of larvae surviving after both 15 ( one-way ANOVA: F3 , 56 = 4 . 152 , P = 0 . 010 ) and 25 days ( F3 , 26 = 4 . 114 , P = 0 . 016 ) . The wMelPop infection had the lowest survival at both time points ( S1 Fig ) , consistent with other experiments ( Figs 1B and 2 ) . Recovery from food deprivation was assessed by scoring the proportion of surviving larvae that pupated and reached adulthood upon resuming feeding . The majority of surviving larvae were able to recover , though larval and pupal mortality occurred across both treatments for all infection types ( Fig 3 ) . We found a significant effect of treatment ( day of re-feeding ) ( one-way ANOVA: F1 , 52 = 5 . 576 , P = 0 . 022 ) , but not Wolbachia infection type ( F3 , 52 = 1 . 461 , P = 0 . 236 ) , on the proportion of surviving larvae that reached adulthood . Surviving larvae that were deprived of food for 25 days were less likely to reach adulthood than larvae deprived for 15 days , with the percentage surviving of larvae that died after re-feeding averaging 10 . 4% and 22 . 9% respectively . This is , in part , due to an increase in pupal mortality at the later time point ( 2 . 1% for Day 15 , 9 . 0% for Day 25 , Student’s t test: P = 0 . 042 , Fig 3 ) . The proportion of surviving larvae that reached adulthood was less for wMelPop than for other infection types , though this difference was not significant ( Fig 3 ) . Larvae that reached pupation before re-feeding ( 33 . 3% of wMelPop-infected larvae and 3 . 3% of wMel-infected larvae ) were counted as survivors . However , these individuals were excluded from development time and wing length analyses ( see below ) as they pupated before food was provided again ad libitum , and were similar in size to adults emerging from larvae held in groups under starvation conditions ( Table 3 ) . The number of days taken for larvae to reach pupation after re-feeding was significantly affected by infection type ( one-way ANOVA: F3 , 488 = 5 . 377 , P = 0 . 001 ) but not treatment ( day of re-feeding ) ( F1 , 488 = 2 . 128 , P = 0 . 145 ) , though infection types within treatments did not differ significantly from each other ( Table 4 ) . Development times of both male and female adults were unaffected by infection type and treatment ( one-way ANOVA: all P > 0 . 053 ) . Female wing length was significantly affected by treatment ( one-way ANOVA: F1 , 251 = 6 . 696 , P = 0 . 010 ) but not infection type ( F3 , 251 = 1 . 432 , P = 0 . 234 ) . Females re-fed after 25 days of food deprivation were smaller than those fed after 15 days for all infection types , though no pairwise comparisons were significant ( Table 4 ) . Conversely , male wing length was unaffected by both infection type ( F3 , 194 = 0 . 844 , P = 0 . 471 ) and treatment ( F1 , 194 = 0 . 032 , P = 0 . 859 ) . We found no correlation between development time and wing length for both males and females for each treatment ( Pearson correlation: all P > 0 . 175 ) . Males deprived of food for 30 days as larvae and then re-fed were tested for their ability to induce cytoplasmic incompatibility when crossed to uninfected females . All food-deprived and re-fed Wolbachia-infected males exhibited complete cytoplasmic incompatibility , with no viable offspring produced across three gonotrophic cycles ( Table 5 ) . Control crosses using standard laboratory-reared adults were also completely sterile , with the exception that a low proportion of eggs hatched in the wMelPop control cross due to contamination with uninfected males ( Table 5 ) . We also tested maternal transmission rates of Wolbachia when infected females were held under starvation conditions for 30 days as larvae and then re-fed . The wMel , wAlbB and wMelPop infections were transmitted with perfect fidelity by both standard laboratory-reared females ( All infection types: maternal transmission rate = 1 , lower 95% binomial confidence interval = 0 . 976 ) , and females that were food-deprived then re-fed ( All infection types: maternal transmission rate = 1 , lower 95% binomial confidence interval = 0 . 988 ) . Female parents were also measured for their fecundity and wing length . Both Wolbachia infection type ( one-way ANOVA: F3 , 227 = 33 . 011 , P < 0 . 0001 ) and treatment ( F1 , 227 = 8 . 787 , P = 0 . 003 ) had significant effects on fecundity . The food-deprivation treatment reduced the mean fecundity of wMel , wAlbB and wMelPop-infected females by approximately 5–6 eggs relative to the controls , though no pairwise comparisons were significant ( Table 6 ) . All Wolbachia-infected females had considerably reduced fecundity compared with uninfected standard laboratory-reared females , regardless of the rearing treatment ( Table 6 ) . Female wing length was also significantly affected by both Wolbachia infection type ( one-way ANOVA: F3 , 108 = 6 . 935 , P = 0 . 0003 ) and treatment ( F1 , 108 = 8 . 852 , P = 0 . 004 ) . For all infection types , females held under starvation conditions and then re-fed were smaller than standard laboratory-reared females , though only the wAlbB comparison was significant ( Table 6 ) .
We have demonstrated that Wolbachia infection reduces the tolerance of Ae . aegypti larvae to starvation conditions . Because Ae . aegypti larvae survive nutrient-poor conditions primarily by expending their own accumulated energy reserves [59 , 60] , we suspect that Wolbachia reduce survival by increasing the rate at which these reserves are depleted . Wolbachia do not appear to affect the rate at which larvae accumulate reserves because development times are unaffected by infection when larvae are well-fed [26 , 37 , 38] . However , when food is limited , Wolbachia may increase the drain on host reserves due to various nutritional requirements [65–68] . Indeed , Wolbachia increase the metabolism of Ae . aegypti adults , at least for the wMelPop infection [42] , though this remains to be tested in larvae . All three infections negatively affected the survival patterns of nutrient-deprived larvae but differed in their severity; wMelPop was highly costly to survival across all experiments , wMel either had a slightly deleterious or no significant effect relative to uninfected larvae , and wAlbB had an intermediate effect . These relative costs are consistent with their effects on mosquito adults and eggs; wMelPop drastically reduces adult lifespan and quiescent egg viability [21 , 37 , 38] , wMel has relatively minor costs or no detectable effect [22 , 24] , and wAlbB has an intermediate cost to these traits [26] . Here , we demonstrate that infections with higher virulence in these life stages also have greater costs to the survival of larvae under starvation conditions . The differences between Wolbachia infections in terms of their deleterious effects are likely to be attributed to their density in mosquito tissues [80] . High bacterial densities and broad tissue tropisms in host cells are often implicated in increasing fitness costs imposed by Wolbachia infection , both in Ae . aegypti [22 , 26] and other insects [81–84] . We found that as the survival period of larvae increased , the deleterious effects of Wolbachia became clearer . In adults and eggs of Ae . aegypti , the fitness costs of Wolbachia are also enhanced with age; wMelPop has relatively little cost to the reproductive success of young females , but fecundity [38] and rates of successful probing [39 , 40] decline severely with subsequent gonotrophic cycles . Additionally , the wAlbB and wMelPop infections impose increased costs on the viability of quiescent eggs over time [26 , 37] . If these age effects also occur in larvae as suggested by our results , virulent Wolbachia infections could have difficulty invading populations where resources are scarce and thus development times are lengthened . Adults emerging from starvation conditions were small in size , even in comparison with those produced through extreme crowding or nutrient limitation ( e . g . [75 , 85 , 86] ) . Adult sizes were at the lowest end of natural variation found in Australian field populations of Ae . aegypti , from where these mosquitoes were sourced [87 , 88] . Adult body size reflects the feeding history of larvae after reaching a critical weight [54]; therefore adults emerging from starvation conditions likely obtained only the minimum nutritional reserves required for pupation . In contrast , larvae that were deprived of food for extended durations and then fed ad libitum emerged nearly as large as mosquitoes fed ad libitum throughout development , suggesting that they were able to attain a close approximation of their maximum weight despite the long interruption to feeding [52 , 53 , 55 , 58] . We found that Ae . aegypti larvae , regardless of Wolbachia infection type , recover well from long periods of nutrient deprivation . While the ability of larvae to resume their development has been reported previously [54 , 56 , 59] , we show that larvae exhibit low mortality , pupate rapidly and emerge at a large size when fed again after being deprived of food for as long as three weeks . In addition , infected males deprived of food as larvae for one month exhibited complete cytoplasmic incompatibility and females transmitted Wolbachia to their offspring with perfect fidelity despite a greatly extended development time . Maternal transmission rates of Wolbachia also remain high when eggs are held in a quiescent state for several weeks [41] . In insects , the maternal transmission efficiency of Wolbachia [35 , 89–91] and the strength of cytoplasmic incompatibility [36 , 92–95] are known to be affected by bacterial density . Because environmental factors such as temperature [96–98] and nutrition [68 , 90 , 99 , 100] modulate Wolbachia density , extreme stress in the field could lead to changes in host effects derived from Wolbachia . However , the wMel infection of Ae . aegypti established in Australian field populations has so far remained stable in terms of its reproductive effects , fitness costs and dengue blockage [24 , 101] . We acknowledge some limitations of our laboratory study that should be addressed in future experiments . We were somewhat limited in our ability to discern any effects of Wolbachia on larval development time and survival to adulthood when held under starvation conditions , due to low pupation rates . Future experiments testing these traits specifically should use larger cohorts with greater replication . Furthermore , we demonstrated the fitness costs of Wolbachia under rather arbitrary and specific scenarios . Nutrient input in the field is dynamic [102] , but in this study larvae were fed for a single time period before either being deprived of food completely or re-fed at a later point . Breeding containers in the field are often populated by multiple cohorts [45 , 103 , 104] , and Suh and Dobson [43] recently reported differential survival of Wolbachia-infected and uninfected 1st instar Ae . aegypti larvae in the presence of later instars . Because predatory behaviour is more likely to occur under nutrient-poor conditions [64] , future experiments on survival under starvation conditions should also test interactions between larvae of mixed age classes . Our experiments also were conducted over multiple generations , and while all infection types were outcrossed to an uninfected colony , the number of generations spent in the laboratory varied between experiments . Laboratory adaptation can have substantial effects on fitness [6 , 105] , which could explain why larvae in some experiments had reduced survival under similar conditions ( see S2 Fig ) Nevertheless , our study demonstrates consistent deleterious effects of Wolbachia on the survival of Ae . aegypti larvae under starvation conditions . To predict the impact on the invasion dynamics of Wolbachia in highly resource-limited habitats , we estimate changes to the unstable equilibrium frequency , denoted p^ , when this cost to larval viability is considered . For Wolbachia to reach fixation in a population its frequency must reach or exceed p^; larger p^ values thus decrease the likelihood and speed of invasion , and will additionally reduce the potential for spatial spread once established in a population [31 , 106 , 107] . Based on the mean survival time of larvae under starvation conditions ( averaged across all experiments where larvae were held in groups ) , we estimate the relative fitness of the wMel , wAlbB and wMelPop infections to be 92 . 3 , 81 . 3 and 68 . 5% that of uninfected respectively . We detected no significant costs for other traits , thus only the cost to survival patterns under starvation conditions is considered . Following equation 17b of Turelli [108] , this produces a p^ of 0 . 08 , 0 . 19 and 0 . 32 for wMel , wAlbB and wMelPop respectively in the absence of any other fitness costs , assuming complete cytoplasmic incompatibility and no maternal transmission leakage as indicated by our results . Previous laboratory studies have estimated the fitness costs of the wMel , wAlbB and wMelPop infections to be approximately ~24% [22] , ~15% [23 , 26] and ~43% [37 , 108] respectively . Using these estimates , p^ increases to 0 . 30 , 0 . 31 and 0 . 61 for wMel , wAlbB and wMelPop respectively when both the costs to larval viability under starvation conditions and deleterious effects on other life stages are considered . In a more extreme scenario , where larvae are deprived of food for 25 days before being provided access to food ad libitum , the invasive potential of Wolbachia decreases further . Assuming Wolbachia-infected larvae are equally as capable of recovering from food deprivation as suggested by our results , the relative fitness of the wMel , wAlbB and wMelPop infections decrease to 90 . 6 , 73 . 9 and 42 . 5% that of uninfected respectively . This corresponds to increases of p^ to 0 . 31 , 0 . 37 and 0 . 75 when taking into account other fitness costs . The deleterious effects demonstrated here could in part explain why wMelPop was able to establish in semi-field cages [22 , 41] but has had great difficulty invading wild mosquito populations , both in Australia and Vietnam [50] . In semi-field cages , any costs of Wolbachia infection to larval viability under nutrient stress were likely to be masked by the fact that larvae were relatively well-fed . On the other hand , survival of larvae under starvation conditions was likely to be a critical fitness component in the field releases . The deleterious effects of Wolbachia demonstrated here will , therefore , have an impact on the potential for these infections to invade natural mosquito populations where competition for resources is the major limiting factor of population size , particularly for wMelPop .
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Dengue is currently the most important arboviral disease in the world . With no effective treatment or commercial vaccine available , strategies to control dengue focus on its mosquito vectors , primarily Aedes aegypti . A recent effort to reduce the burden of dengue aims to replace native Ae . aegypti with those refractory to the virus . This is achieved by infecting mosquitoes with Wolbachia , bacteria which can invade insect populations by exploiting host reproduction . Some strains of Wolbachia have harmful effects on the mosquito host which can inhibit its ability to spread . While these costs have been characterized comprehensively in the laboratory , we must also consider any impacts when mosquitoes experience stresses that commonly occur in nature . For instance , Ae . aegypti larvae often develop in highly-occupied habitats where food is scarce . We investigated the effects of Wolbachia on mosquito larvae when they develop under extremely nutrient-limited conditions and found costs to survival for all strains . This will translate to a reduced ability of Wolbachia-infected mosquitoes to replace native populations in competitive habitats .
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[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2016
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Costs of Three Wolbachia Infections on the Survival of Aedes aegypti Larvae under Starvation Conditions
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An elevated growth temperature often inhibits plant defense responses and renders plants more susceptible to pathogens . However , the molecular mechanisms underlying this modulation are unknown . To genetically dissect this regulation , we isolated mutants that retain disease resistance at a higher growth temperature in Arabidopsis . One such heat-stable mutant results from a point mutation in SNC1 , a NB-LRR encoding gene similar to disease resistance ( R ) genes . Similar mutations introduced into a tobacco R gene , N , confer defense responses at elevated temperature . Thus R genes or R-like genes involved in recognition of pathogen effectors are likely the causal temperature-sensitive component in defense responses . This is further supported by snc1 intragenic suppressors that regained temperature sensitivity in defense responses . In addition , the SNC1 and N proteins had a reduction of nuclear accumulation at elevated temperature , which likely contributes to the inhibition of defense responses . These findings identify a plant temperature sensitive component in disease resistance and provide a potential means to generate plants adapting to a broader temperature range .
Temperature is a major environmental factor that regulates plant growth and development as well as its interaction with other organisms [1] . Plants respond to small temperature changes and yet temperature signaling is largely unknown in plants [2] . Temperature is known to influence disease resistance to bacteria , fungi , virus , and insects; and different host-pathogen interactions respond differently to different temperature ranges [3] . A high temperature very often inhibits disease resistance or plant immunity [4] , although low temperature also leads to reduced plant defense in some cases [5] . Despite the fact that temperature sensitivity poses a challenge to agriculture in the current global climate change scenario , the molecular basis for the high temperature inhibition of plant immunity is unknown . Plant immunity occurs at multiple levels and can be largely divided into two branches . One is a general resistance responding to common features of pathogens named ‘microbial- or pathogen associated molecular patterns’ ( MAMP or PAMP ) . The second immunity branch responds to pathogen virulent factors or effectors . This cultivar-specific resistance or ETI is induced upon a specific recognition of the pathogen race-specific avirulence ( Avr ) gene by disease resistance ( R ) gene of the host plant . This ‘gene-for-gene’ interaction leads to rapid and efficient defense responses including a form of programmed cell death named hypersensitive response ( HR ) to restrict the growth of pathogens . R proteins of the largest class have ‘nucleotide-binding’ ( NB ) and leucine-rich repeat ( LRR ) domains . The amino-termini of these proteins are either of the Toll and interleukin-1 receptor ( TIR ) type or the coiled-coiled ( CC ) type . Multiple layers of plant immunity reflect a co-evolution of host plants and pathogens . Heat sensitivity of disease resistance has been observed in both basal defense responses and R gene-mediated defense response . For instance , Arabidopsis plants are more susceptible to virulent Pseudomonas syringae pv . tomato ( Pst ) DC3000 at 28°C than at 22°C [6] . Resistance to tobacco mosaic virus ( TMV ) conferred by the N gene is effective at 22°C , but is abolished at 30°C [7] . Resistance to root-knot nematodes conferred by the Mi-1 gene in tomato is inactive above 28°C [8] . HR induced by the Arabidopsis RPW8 gene against powdery mildew is suppressed by temperature above 30°C [9] . Arabidopsis resistance to avirulent Pst DC3000 strains with AvrRpt2 , AvrRps4 , or AvrRpm1 effectors exhibited at 22°C are inhibited at 28°C [6] . Resistance against fungal pathogen Cladosporium fulvum is conferred by Cf4 and Cf9 in tomato , and HR mediated by these two genes can be suppressed at 33°C [10] . A number of mutants with upregulated defense responses are also found to be temperature sensitive . The bon1 mutant exhibits a dwarf phenotype at 22°C but not at 28°C due to a suppression of defense response mediated by SNC1 at elevated temperature [11] . SNC1 is a NB-LRR type of R-like gene closely related to the R genes RPP4 and RPP5 [12] , and the gain-of-function mutant snc1-1 exhibits a temperature-sensitive growth and defense phenotype [11] . Similarly , autoimmune response mediated by R-like genes in F1 hybrids between Arabidopsis accessions could be attenuated by a moderate increase in growth temperature [13] . The temperature effect on defense signaling is sometimes thought to be an indirect physiological change caused by global alterations in metabolism and membrane properties among others . However it is possible that a common mechanism for temperature sensitivity exists for different systems of disease resistance because many of them share similar signaling molecules and use similar signaling cascades . Some of the signaling components are themselves modulated by temperature . For instance , EDS1 and PAD4 , two regulators of both basal and R-mediated disease resistance , have a higher steady expression level at 22°C than at 28°C [11] . Salicylic acid ( SA ) , a signal for systemic acquired resistance , is regulated by temperature [14] , [15] . However , an initial attempt to alter temperature sensitivity by up-regulating EDS1 and PAD4 was not successful [6] , and no systemic study had been carried out to investigate this temperature modulation of disease resistance at the molecular level . Here we report a genetic screen for mutants with enhanced disease resistance at an elevated temperature . We show that the R-like gene SNC1 and the R gene N are the temperature-sensitive components responsible for temperature sensitivity in defense responses they each induce . Alterations in R proteins are sufficient to change temperature sensitivity of plant immune response and confer defense responses at elevated temperature . Furthermore , a high temperature reduces nuclear localization of SNC1 and N proteins , which likely contributes to the repression of defense responses . Therefore , temperature sensitivity of R proteins is an important mechanism underlying temperature modulation of plant immunity .
Wild-type Arabidopsis plants turn off defense responses in the absence of pathogens as these responses usually compromise plant growth and sometimes cause cell death . The Arabidopsis snc1-1 mutant shows constitutive defense response and dwarf phenotype in a temperature-dependent manner , i . e . , the mutant phenotypes are expressed at 22°C but are not at 28°C [11] ( Fig . 1A ) . The growth regulation by temperature in snc1-1 therefore serves as a model for investigating temperature modulation of defense responses . We carried out a screen for mutants that are defective in high-temperature inhibition of disease resistance in the snc1-1 background by EMS mutagenesis . Mutants retaining a dwarf phenotype at 28°C together with snc1-1 were isolated . One such mutant , int ( insensitive to temperature ) 102-1 , had an almost identical dwarf phenotype at both 22°C and 28°C; and it had a similar small size and a curly leaf shape as the snc1-1 mutant grown at 22°C ( Fig . 1A ) . Further analysis revealed that the int102-1 snc1-1 mutant retained enhanced defense responses at 28°C and was resistant to the bacterial pathogen Pst DC3000 at both 22°C and 28°C . At 22°C , the wild-type plants supported a 56-fold increase in bacterial growth after a 3-day inoculation , while snc1-1 and int102-1 snc1-1supported only four- to five-fold increase in bacterial growth ( Fig . 1B ) , indicating that snc1-1 and int102-1 snc1-1 exhibit enhanced resistance at a similar level at normal growth temperature . At 28°C , snc1-1 was as susceptible as the wild type and supported 127-fold increase in bacterial growth . In contrast , there was little growth ( two-fold increase ) of bacteria on int102-1 snc1-1 at 28°C ( Fig . 1B ) . Therefore , the int102-1 snc1-1 mutant indeed has elevated defense responses at both temperatures and the int102-1 mutation confers a temperature-insensitive or heat-stable immune response . We found that the elevated defense response in this mutant is mediated by salicylic acid ( SA ) and PAD4 . The expressions of a defense response marker gene PR1 [16] was higher in int102-1 snc1-1 than in the wild type or snc1-1 at 28°C ( Fig . 1C ) , suggesting an activation of the SA pathway . The nahG transgene coding for SA degradation enzyme [17] was therefore introduced into int102-1 snc1-1 , and this transgene indeed suppressed both the growth and defense gene expression phenotypes of int102 snc1-1 . The int102 snc1-1nahG plants had wild-type morphology and no elevated PR1 expression at either 22°C or 28°C ( Fig . 1D and data not shown ) . Similarly , the pad4 mutant defective in defense responses [18] suppressed the phenotypes of int102-1 snc1-1 ( Fig . 1C and D ) , further demonstrating an upregulation of defense responses in the int102-1 snc1-1 mutant at 28°C . We cloned the INT102 gene based on its tight linkage to SNC1 in a F2 population of int102-1 snc1-1 ( in the Col-0 accession ) crossed to the wild-type Ws-2 accession . Analysis of 240 int-looking plants in this population revealed no recombination between INT102 and SNC1 , indicating a tight linkage of the two genes . Sequencing the SNC1 gene in int102-1 snc1-1 revealed a G to A point mutation causing a change of glutamic acid to lysine ( named snc1-3 ) at amino acid residue 640 in the second LRR motif in the LRR domain of SNC1 ( Fig . 2A , S1 ) . The int102-1 snc1-1 mutant is therefore named snc1-4 , and it contains both the snc1-1 and snc1-3 mutations . The snc1-4 mutation is likely gain-of-function although it is recessive to the wild type and snc1-1 . The same recessive property was observed for the gain-of-function mutation snc1-1 [12] . This recessive nature is likely due to haploid deficiency rather than loss-of-function , and it is demonstrated that the mutant SNC1-1 transgene induced snc1-1 phenotype in transgenic plants [12] . To determine if the snc1-3 mutation is the causal mutation of the int phenotype , we generated transgenic Arabidopsis lines containing different forms of SNC1 . The SNC1 protein was tagged by the green fluorescent protein ( GFP ) at the carboxyl-terminus and expressed under the control of its native promoter ( named as pSNC1::SNC1:GFP ) . Four forms of SNC1 fusions were created: the wild-type ( SNC1WT ) , SNC1-1 , SNC1-3 , and SNC1-4; and they were transformed into the wild-type Col-0 plants respectively . Primary transformants were grown first at 22°C for three weeks before being transferred to 28°C till seed setting and their growth phenotypes were scored both at 22°C and after two weeks at 28°C ( Fig . 2B ) . Transgenic Arabidopsis plants with the same transgene exhibited varying phenotype , mostly likely due to varying expression levels of the transgene . Among the 10 pSNC1::SNC1WT:GFP lines generated , all but one exhibited morphological defects at 22°C indicating an activation of defense responses . This is consistent with the previous finding that the wild-type SNC1 genomic fragment could induce autoactivation due to a higher SNC1 expression level than the endogenous one [19] . Among these , seven lines showed a dwarf phenotype at 22°C but new tissues grown at 28°C did not exhibit visible growth defects and we termed these as rescued at 28°C . The rest two lines showed dwarf phenotype at 22°C but can be partially rescued at 28°C . Among the 9 lines of pSNC1::SNC1-1:GFP , all exhibited a dwarf phenotype . At 28°C , four lines were rescued , two lines were partially rescued , and 3 lines were not rescued . Among the 8 lines of pSNC1::SNC1-3:GFP , one did not exhibit a morphological defect , one had a 28°C rescued dwarf phenotype , one had a partial 28°C rescued phenotype , and 5 had a non-rescued phenotype . Among the 12 lines with pSNC1::SNC1-4:GFP , one did not exhibit a growth defect , three had a 28°C rescued dwarf phenotype , one had a partial rescued and 6 had a 28°C non-rescued phenotype . In sum , the SNC1-3 and SNC1-4 constructs induced a significantly higher percentage of transgenic lines with a dwarf phenotype at both 22°C and 28°C , indicating that the snc1-3 mutation is the causal mutation of int102 and that the snc1-3 mutation alone ( without snc1-1 ) is sufficient to induce defense responses at a higher temperature . Therefore , temperature sensitivity of disease resistance induced by SNC1 is controlled by the R-like gene itself rather than by other regulatory components and that a mutation in the R-like gene is sufficient to confer disease resistance at a high temperature . We explored the possibility of using a transient assay to analyze the SNC1 activity and its protein localization , as we could not detect SNC1:GFP signals by microscope in transgenic Arabidopsis . Because many R genes induce HR-like cell death in N . benthamiana ( Nb ) when co-expressed with their elicitors and some activated forms of R genes induced cell death in the absence of their elicitors , we tested cell death-inducing activities of different forms of SNC1 expressed under a strong 35S promoter by Agrobacterium-mediated infiltration ( agro-infiltration ) in Nb . For agro-infiltration experiments of more than six replicas , we saw a correlation of cell death-inducing activities in Nb with defense response ( dwarf ) -inducing activities in Arabidopsis for different forms of SNC1 genes ( Fig . 2C ) . No visible cell death was observed for p35S::SNC1WT:GFP at 22°C or 28°C . On the other hand , the p35S::SNC1-1:GFP fusion induced visible cell death at 22°C but not at 28°C . In fully infiltrated leaves , small areas ( more than 2 mm in diameter ) underwent cell death manifested by collapsed leave cells . In contrast , both p35S::SNC1-3:GFP and p35S::SNC1-4:GFP induced visible cell death at both temperatures . These results further support the conclusion that snc1-3 mutation is the causal mutation for heat-stable resistance . To further investigate the mechanism underlying temperature sensitivity of defense responses , we carried out a suppressor screen in the heat-stable snc1-4 mutant background . Mutants that regained high-temperature inhibition of defense responses were isolated and named rit ( revertant of int ) . One such mutant rit1 snc1-4 had a snc1-1 like phenotype: dwarf at 22°C but wild-type like at 28°C , suggesting a regaining of temperature sensitivity ( Fig . 3A ) . Correlated with its growth defect , the rit1 snc1-4 mutant has an enhanced disease resistance to virulent pathogen Pst DC3000 at 22°C and this elevated defense is suppressed at 28°C ( Fig . 3B ) . At 22°C , Pst DC3000 had a similar growth reduction in rit1 snc1-4 as in snc1-1 and snc1-4 compared to the wild-type Col ( p = 0 . 24 , 0 . 11 respectively ) . At 28°C , snc1-4 exhibited an inhibition of bacterial growth to a similar extent as at 22°C ( p = 0 . 065 ) . In contrast , rit1 snc1-4 lost the inhibition of bacterial growth at 28°C conferred by snc1-4 ( p = 0 . 0001 ) and supported bacterial growth to a similar extent as snc1-1 ( p = 0 . 064 ) . Therefore rit1 indeed reverses the heat-stable resistance phenotype to the heat-sensitive phenotype . We found that rit1 is an intragenic suppressor of snc1-4 . There is no phenotypic segregation in the F2 progenies of a cross of rit1 snc1-4 with wild-type Col-0 or Ler grown at 28°C indicating that the rit1 mutation is very closely linked to the SNC1 gene . Sequencing the entire SNC1 genomic fragment in the rit1 snc1-4 mutant identified a G to A point mutation resulting in a serine substitution of glycine at amino acid residue 380 ( Fig . 3C , S1 ) . We named this G380S mutation snc1-5 and this new allele with snc1-1 , snc1-3 , and snc1-5 mutations as snc1-6 ( Fig . 3C ) . This glycine residue resides immediately after the putative GxP or GLPL motif in the NB-ARC domain . This motif was previously identified as important for nucleotide binding and mutations in residues close to the motif might compromise activation of NB-LRR proteins [20] . A second intragenic suppressor named rit4 was identified from the same rit screen . This mutant was independent of rit1 as it was isolated from a different mutagenesis pool and had an additional phenotype unrelated to defense . Interestingly , we found the same G to A alteration resulting in a G380S mutation as in rit4 . That two independent but identical mutations result in the same rit phenotype confirms that snc1-5 is indeed the mutation responsible for reverting the temperature insensitivity of snc1-4 . This conclusion is further supported by the SNC1-6 activity in the Nb transient expression system . The snc1-5 mutation was introduced into the p35S::SNC1-4:GFP construct to create p35S::SNC1-6:GFP . While SNC1-4 induced cell death in Nb at both 22°C and 28°C , SNC1-6 induced cell death only at 22°C but not at 28°C ( Fig . 3D ) . Thus snc1-5 mutation appears to be a suppressor of the heat-stable SNC1-4 activity specifically at 28°C and it does not significantly suppress SNC1-4 activity at 22°C . This notion is further supported by the failure of inhibiting the SNC1-1 22°C activity by the snc1-5 mutation at 22°C . When the snc1-5 mutation was introduced into SNC1-1 , the p35S::SNC1-1 , 5:GFP was able to induce cell death at 22°C but not at 28°C similarly to p35S::SNC1-1:GFP ( Fig . 3E ) . With the identification of different forms of SNC1 conferring defense responses of different temperature sensitivity , we conclude that the NB-LRR gene SNC1 is the temperature-sensitive component causing temperature sensitivity of the whole defense responses it induces . An elevated temperature inhibits plant immunity probably through the very early component of the signaling pathway that is the NB-LRR genes . We further investigated the molecular basis underlying the temperature sensitivity of SNC1 . The SNC1 transcript level was higher in snc1-1 than in the wild type at 22°C but not at 28°C , while it was higher in snc1-4 at both temperatures ( Fig . 1C ) . Consistent with previous findings [11] , [19] , regulation of SNC1 at the transcript level is largely due to a feedback amplification through PAD4 , as the snc1-4 pad4 double mutant had the same amount of SNC1 transcript as snc1-1 pad4 ( Fig . 1C ) . Therefore , upregulation of the SNC1 transcript by the snc1-3 mutation at a high temperature is unlikely the primary cause of the heat-stable resistance . Because an extremely high temperature of 37°C was shown to greatly inhibit the accumulation of the NB-LRR type of R protein MLA [21] , we investigated whether temperature sensitivity is due to differential accumulation of SNC1 proteins at 22°C and 28°C . In transgenic Arabidopsis with pSNC1::SNC1:GFP , we were able to detect weak GFP signals by Western although no GFP signals could be visualized by microscopy . Three independent lines with each of the four wild-type and mutant SNC1 transgenes were analyzed by protein blots for GFP expression at 22°C and 28°C ( Fig . S2 , Text S1 ) . No dramatic differences in expression level were observed among different SNC1 versions or between 28°C rescued and 28°C non-rescued lines . There is a slight reduction of SNC1:GFP at 28°C compared to at 22°C in the 28°C rescued lines but not much in the non-rescued lines . Whether this reflects a biologically significant reduction or a dilution of signals by non-transgenic plants from the segregating population is yet to be determined . A significant correlation was observed between the amount of nuclear accumulation of the SNC1 protein and its activity . Because we could not detect GFP signals in transgenic plants , we transiently expressed different forms of p35S::SNC1:GFP in Arabidopsis protoplasts . Despite a low expression efficiency compared to p35S::GFP , three expression patterns were observed for the SNC1:GFP proteins: nucleus-only , ubiquitous ( cytosol , plasma membrane , and nucleus ) , and no nucleus ( cytosol or cytosol and plasma membrane ) ( Fig . 4A , B ) . As it was difficult to distinguish no nucleus signal from background , we scored the expression patterns by the first two categories from 200 to 300 protoplasts for each transformation . For protoplasts transformed with SNC1WT at 22°C , 1 . 8% showed nucleus-only signal and 2 . 7% showed ubiquitous signal . In contrast , SNC1-1 , SNC1-3 , and SNC1-4 exhibited nucleus-only signal but no ubiquitous signal ( Fig . 4B ) . At 28°C , neither SNC1WT nor SNC1-1 exhibited the nucleus-only signal , while the majority of SNC1-3 and SNC1-4 cells exhibited the nucleus-only signal ( Fig . 4B ) . Therefore , there is a general correlation of high SNC1 activity with more nuclear signal . At 22°C , all three mutant forms have a higher nuclear accumulation than the wild-type form . At 28°C , nuclear accumulation of SNC1WT and SNC1-1 but not that of SNC1-3 or SNC1-4 was reduced . Thus an elevated temperature reduces the nuclear accumulation of the SNC1 protein and certain mutations such as snc1-3 could resist this inhibition and induce defense at high temperatures . A similar inhibition of nuclear accumulation by temperature was also observed for SNC1 expressed in Nb ( Fig . 4C ) . A very weak GFP signal was detected for the SNC1WT:GFP fusion protein and the signal was mostly in the cytosol and plasma membrane . On the other hand , mutant fusions proteins of SNC1-1 , SNC1-3 and SNC1-4 had higher fluorescent signals and the signals were mainly localized to the nucleus at 22°C . At 28°C , SNC1-1:GFP was found in the cytosol and plasma membrane and no nucleus localization was observed . In contrast , SNC1-3:GFP and SNC1-4:GFP were both mostly accumulated in the nucleus with SNC1-4 with a stronger signal . We further tested the effect of snc1-5 mutation on the localization of SNC1-4 protein . Correlated with the cell death inducing activity at 22°C but not 28°C , SNC1-6:GFP is localized in the nucleus at 22°C but not 28°C when expressed in Nb ( Fig . 4F ) . Thus , an elevated temperature could reduce the nuclear accumulation of heat-sensitive SNC1-1 and SNC1-6 but not the heat-stable SNC1-3 and SNC1-4 , indicating that nuclear localization of SNC1 might be critical for its activity . The nuclear accumulation of SNC1 appears to be required for the enhanced defense responses at elevated temperatures . A nucleus export signal ( NES ) [22] was added to the SNC1-4:GFP fusion , and it abolished the nuclear localization of SNC1-4:GFP at 28°C and also resulted in a weaker expression of the protein ( Fig . 4E ) . The resulting SNC1-4:NES:GFP could no longer induce cell death at either 22°C or 28°C in contrast to SNC1-4:GFP ( Fig . 4F ) . Similar activity suppression by NES was also observed for the SNC1-3:GFP fusion ( Fig . 4F ) . Thus , nuclear localization at high temperatures is likely critical for the mutant SNC1 proteins to induce heat-stable defense responses . It remains however to be determined if the total amount of SNC1-4:NES:GFP expression is reduced compared to SNC1-4:GFP , and if so whether the SNC1-4 protein becomes less stable outside the nucleus . To assess the relative position of nuclear accumulation of SNC1 in the signaling event of disease resistance at high temperature , we analyzed SNC1-4 localization in a few mutants that suppress the snc1-4 mutant phenotype . In addition to PAD4 and SA , we found that MOS3 and MOS6 , two genes required for snc1-1 activity [23] , [24] , are also required for disease resistance in snc1-4 at high temperature . Both the snc1-4 mos3 and the snc1-4 mos6 double mutants exhibited a largely wild-type growth phenotype at 22°C and 28°C ( Fig . 5A ) . We found that the nuclear accumulation of SNC1-3 and SNC1-4 at 28°C was inhibited by the mos3 and mos6 mutations . No protoplasts showed nucleus-only signal in mos3 or mos6 for SNC1-4 ( Fig . 5B , 4B ) . In contrast , the loss-of-function mutation of PAD4 did not alter the nuclear accumulation of the SNC1 mutant proteins ( Fig . 5B ) although it suppressed the snc1-4 mutant phenotype ( Fig . 1C , D ) . These data suggest that MOS3 and MOS6 mediate SNC1-induced defense responses via an early event influencing the localization of R-like protein . MOS3 encodes a putative nucleoporin Nup96 which could be responsible for RNA transport through nuclear envelope . It is yet to be determined how it influences the SNC1 localization . MOS6 encodes a putative importin α3 , which may affect R protein shuttling directly . That pad4 affects disease resistance but not SNC1 localization in protoplasts further indicates that nuclear localization of the SNC1 protein at high temperature is a critical early event in heat-stable defense responses . To determine if our observation of temperature-sensitive induction of defense responses by the Arabidopsis R-like gene SNC1 is a general phenomenon for NB-LRR type of R genes , we analyzed defense response induced by the R gene N , which confers resistance to tobacco mosaic virus ( TMV ) only at temperatures below 28°C [7] , [25] . A mutation of Y646K corresponding to snc1-3 ( E640K ) was introduced into a genomic fragment of N gene described previously [26] ( Fig . 6A ) . Because the EK640LD motif in SNC1-3 forms a potential sumoylation site , we also made a mutation in the N gene to introduce a N648D change corresponding to D642 of SNC1 so that the double mutant Y646K N648D could potentially provide a sumoylation site in the N protein as in the Arabidopsis SNC1-3 protein ( Fig . 6A ) . When co-expressed with its elicitor p50 in Nicotiana tobaccum ( Nt ) by agro-infiltration , the wild-type N gene triggered HR-like cell death at 22°C but not at 30°C ( Fig . 4B ) . In contrast , mutant N genes containing Y646K , or Y646KN648D mutations , when co-expressed with p50 , induced cell death in Nt at both 22°C and 30°C ( Fig . 6B ) . To our surprise , the N648D mutant N gene also induced cell death at 30°C ( Fig . 6B ) . These mutations do not appear to confer constitutive auto-activities because they did not cause cell death in the absence of p50 . Thus , the N gene is responsible for the temperature sensitivity of HR associated with TMV resistance , indicating that other NB-LRR type of R genes might also function as temperature-sensitive components in defense responses and that temperature sensitivity can be altered by specific mutations in the R proteins to confer heat-stable disease resistance . We determined if temperature sensitivity of the N-mediated defense response is correlated with the N protein localization similarly to that of the SNC1 protein . A fusion protein of N and citrine was shown previously to be localized to the nucleus when expressed together with p50 in N . benthamiana [27] . We found that in contrast to nucleus localization of N:citrine at 22°C when co-expressed with p50 ( Fig . 6C ) , no signal could be detected in the nucleus when infiltrated plants were incubated at 30°C ( Fig . 6C ) . This indicates that nuclear localization of activated wild-type R protein ( s ) is subject to temperature modulation , similar to active form of the mutant R protein SNC1-1 .
Despite the fact that temperature regulates many different growth and developmental processes , the temperature-sensitive components that control this sensitivity are largely unknown in plants . Temperature sensitivity in plant disease resistance is a phenomenon reported as early as 1969 and observed in various plant-pathogen interactions . Through a genetic screen for Arabidopsis heat-stable mutants that would retain defense responses normally inhibited at elevated temperatures , we identified the NB-LRR type of R-like gene SNC1 as a key component responsible for temperature sensitivity . A point mutation in the SNC1 gene is sufficient to induce defense responses at an elevated temperature ( Fig . 1 ) . Through a second genetic screen , we identified a SNC1 mutation that appears to inhibit SNC1 activity specifically at high temperature ( Fig . 5 ) . This finding reinforces the notion that the NB-LRR encoding gene SNC1 is temperature sensitive . Furthermore , a mutation similar to the heat-stable mutation identified in SNC1 was created in the N gene , and the mutant N gene was capable of inducing HR at a higher temperature ( Fig . 6 ) , indicating that the R gene N is responsible for temperature sensitivity of N-mediated defense responses . Thus we uncovered a mechanism for high temperature inhibition of plant immune responses . It is very likely that the NB-LRR type of R genes rather than other signaling components are responsible for temperature sensitivity in many other R-mediated disease resistance . This mechanism may also account for temperature sensitivity in some lesion mimic mutants and hybrid necrosis . In those mutants or hybrids , upregulated defense responses and lesions could have arisen at least in part from R gene activation similarly to upregulation of the R-like genes in the bon1 or bon1bon3 mutants [28] , [29] . Inhibition of R or R-like activity by a higher growth temperature could suppress cell death and defense responses induced by those R or R-like genes . It is not obvious if there is any selective advantage to have a temperature-sensitive immune system . Structural constraints may have prevented the evolution of heat-stable R genes that are also properly regulated . R genes with heat-stable activity could be associated with fitness cost , which may not manifest in the laboratory . Nevertheless , heat-stable resistance does occur in nature . For instance , the tomato Mi-9 gene confers a heat-stable resistance to root-knot nematodes . Though the gene is not yet cloned , it is shown to be a homolog of the heat sensitive Mi-1 gene [30] . It will be interesting to see if any of them arose from changes in the R genes similar to snc1-3 . We propose that temperature sensitivity in defense responses is largely mediated through the NB-LRR coding genes . Plant immunity is triggered when the total R or R-like activity , a multiplication of its amount and its protein activity , is above a threshold ( Fig . 7A ) . For the R or R-like protein , its activity is intrinsically temperature sensitive . Consequently the total R activity would be below the threshold at elevated temperature , resulting in no defense . In contrast , the mutant R or R like proteins like SNC1-3 have reduced temperature sensitivity and therefore could induce heat-stable defense responses . The temperature sensitivity could be intrinsic to the protein itself or could be mediated by R-interacting chaperons whose homeostasis is affected by temperature . An elevated temperature might also reduce the R amount and thus total R activity [21] , [28] . An apparent reduction of SNC1 wild-type proteins at elevated temperature is observed in transient Nb expression system and transgenic Arabidopsis ( Fig . 4C , S2 ) . To what extent this reduction of protein level contributes to the reduced defense responses at elevated temperature is yet to be determined . It is not well understood how the heat-stable mutations such as snc1-3 affect activities of NB-LRR proteins and how snc1-5 reverts the heat-stable activities . Although the snc1-3 mutation appears to induce autoactivation as suggested by the SNC1-3 transgenic plants ( Fig . 2 ) , the Y646K and N648D mutations in N did not induce cell death in the absence of elicitors and therefore are probably not constitutively autoactive . The E640K ( snc1-3 ) mutation in SNC1 and Y646K in N probably do not induce a local post-translational protein modification because the N648D mutation in the N protein cause heat-stable activity as well as Y646K . These mutations do not appear to generate a local nuclear localization signal as shorter versions of the mutant SNC1:GFP proteins did not confer nuclear localization . It has been hypothesized that activation of the NB-LRR R proteins opens the NB domain and possibly allows interaction of the amino-terminal domain with downstream signaling molecules [31] , [32] . We hypothesize that R proteins assume at least one transitional conformation ( named T ) between the OFF state to the ON state ( Fig . 7B ) . Glycine close to the GLPL motif is required for the change from T state to the ON state and high temperature inhibits the change from the T state to ON state . The elicitors and mutations like snc1-1 promotes the change from the OFF state to state T , while mutations like snc1-3 might enhance the transition from state T to ON . Nuclear localization is probably an immediate subsequent event after the R protein assumes the ON form . Regulated nucleo-cytoplasmic partitioning of key components is essential in hormone signaling , light signaling , temperature signaling , and plant-pathogen interactions [33] , [34] , [35] , [36] . Temperature has been shown to influence the localization of regulators of temperature signaling . For instances , HSFA1 , a heat shock transcription factor , is equally distributed between cytoplasm and nucleus at a normal growth temperature and is predominantly nuclear after a heat shock [37] . HOS1 , a negative regulator of cold response , is cytoplasmic at a normal temperature but is nuclear after an exposure to a low temperature [38] . How and to what extent temperature influences nuclear localization of proteins will be an interesting subject to explore further . Other mechanisms for temperature sensitivity must exist in plant immunity . For instance , basal resistance is inhibited by a higher growth temperature [6] , indicating regulatory components other than R genes are modulated by temperature as well . That the NB-LRR R proteins can mediate the temperature sensitivity in disease resistance suggests that plants utilize different temperature sensors for different growth , development , and stress response processes . Future studies should further reveal the molecular basis of temperature sensitivity of R genes and more generally , of temperature modulation of gene expression , protein activities , and protein localization . The current climate change is causing increased range and severity of plant diseases [39] . The knowledge gained from this study will potentially provide us with tools to engineer crop plants with heat-stable disease resistance and with better adaptation to climate change .
The Arabidopsis thaliana plants were grown in soil at 22°C or 28°C under constant light ( approximately 100 µmol m−2 sec−1 ) with a relative humidity between 40% and 60% for morphological phenotypic and gene expression analysis . Plants used for pathogen tests are grown under a photoperiod of 12 hr light/12 hr dark . Arabidopsis seedlings used for protoplast transformation were grown on solid medium with 1/2 MS salts , 2% sucrose , and 0 . 8% agar and under a photoperiod of 8 hr light/16 hr dark . Nicotiana tobaccum ( Nt ) and Nicotiana benthamiana ( Nb ) plants were grown in the greenhouse for three to four weeks before they were acclimated to 22°C on lab bench for at least a day before being used for cell death assay . The snc1-1 seeds were treated with 0 . 25% EMS ( ethane methyl sulfonate ) for 12 hours . Approximately 40 , 000 M2 plants ( derived from 4 , 000 M1 ) were screened at 28°C for int mutants with the 22°C snc1-1 like dwarf phenotype . The snc1-4 seeds were mutagenized similarly . The M2 plants were screened for rit ( revertant of int , i . e . wild-type-looking ) mutants at 28°C . The F2 populations for mapping the int or rit mutations were derived from genetic crossing between the mutants in the Col-0 background to wild-type plants in the Ws-2 background . Bulked segregation analysis was performed on pools of 40 plants with SSLP , CAPS , and dCAPS markers between Col-0 and Ws-2 [40] . For the pSNC1::SNC1:GFP construct , a StuI site was added to the genomic fragment of SNC1 before the stop codon via polymerase chain reaction ( PCR ) . An EcoRI and StuI digested fragment of this product was ligated in frame with GFP to generate a 3′SNC1:GFP construct . The PstI and EcoRV digested fragment of the 5′ region of SNC1 from the BAC clone F5D3 ( from Arabidopsis Biological Resource Center ) and the EcoRV and PstI digested fragment of the 3′SNC1:GFP construct were ligated to the PstI site of pCAMBIA1300 to generate the pSNC1::SNC1:GFP construct . The SNC1-1 , SNC1-3 , and SNC1-4 mutations were introduced into pSNC1::SNC1:GFP through site-directed mutagenesis with the ‘QuikChange’ kit according to manufacture's instruction ( Stratagene ) . For the SNC1:GFP constructs , a 5 . 5 kb fragment containing the coding region and the 3′UTR of SNC1 was isolated from the BAC clone F5D3 . A BamHI restriction site was added to this genomic fragment before the stop codon of SNC1 . The NcoI and BamHI digested fragment containing the SNC1 coding region without the stop codon was inserted into the pSAT-N1 vector [41] to generate the p35S::SNC1:GFP construct . The SNC1-1 , SNC1-3 , SNC1-4 , and SNC1-5 mutations were introduced into p35S::SNC1:GFP through site-directed mutagenesis as described above . The cassettes of various p35S::SNC1:GFP were excised at the PI-PspI restriction sites and cloned into the pHPT binary vector [41] to generate pHPT-SNC1:GFP , pHPT-SNC1-1:GFP , pHPT-SNC1-3:GFP , and pHPT-SNC1-4:GFP constructs . For the SNC1:NES:GFP constructs , two complementary oligonucleotides encoding nuclear export signal ( NES ) LALKLAGLDI were synthesized with a BamHI restriction site added to both ends of the primers . NES-F: 5′GATCCTGGCTTTGAAGTTAGCTGGTTTGGATATCAA 3′ . NES-R: 5′ GATCTTGATATCCAAACCAGCTAACTTCAAAGCCAG 3′ . Synthetic oligo-nucleotides were denatured , annealed , and inserted into the BamHI restriction site between SNC1 and GFP to generate the p35S::SNC1-3:NES:GFP and p35S::SNC1-4:NES:GFP . The two p35S::SNC1:NES:GFP cassettes were cloned into the pHPT binary vector to generate pHPT-SNC1-3:NES:GFP , and pHPT-SNC1:NES:GFP constructs as described above . The wild-type N gene for mutagenesis was described previously [26] . It is a HA tagged genomic fragment of the N gene under the control of a 35S promoter . This N gene was subject to site-directed mutagenesis with the ‘QuikChange’ kit as described above . All primers sequences will be available upon request . Agrobacterium tumefaciens stains of GV3101 ( Koncz and Schell , 1986 ) carrying various SNC1 constructs were used to transform wild-type Col-0 plants via standard floral dipping method [42] . Primary transformants were selected on solid medium containing hygromycin . Protoplast isolation and transformation were carried out as previously described [43] . In brief , protoplasts were generated from wild-type and mutant seedlings grown on plates . After transformation , protoplasts were incubated at specific temperatures and the GFP signals were observed from 12 hours to 48 hours . The binary vectors were transformed into Agrobacterium tumefaciens strain C58C1 [20] for transient expression . Agrobacterial cultures were grown overnight to OD600 of 1 . 0 in liquid LB . Cells were then collected by centrifugation and resuspended in the induction medium ( 10 mM MES , pH 5 . 7 , 10 mM MgCl2 , 200 µM acetosyringone ) to OD600 of 0 . 8 . After incubating at room temperature for 3 hrs , the Agrobacterial cells were infiltrated into the abaxial surface of Nb or Nt leaves using 1 ml needleless syringes . On average , four spots were used to infiltrate one whole Nb leaf . Infiltrated plants were subsequently incubated at 22°C or 28°C before the infiltrated leaves were examined for GFP signals under a microscope ( Zeiss AXIO 2 plus or Leica TCS SP5 ) within a 48 hr period after inoculation . Total RNAs were extracted using Tri Reagent ( Molecular Research , Cincinnati , OH ) from leaves of 3-week-old plants . Twenty micrograms of total RNAs per sample were used for RNA gel blot analysis according to standard procedure [44] . P . syringae pv . tomato DC3000 was grown 2 to 3 days on the KB medium and resuspended at 105 cfu ( colony forming unit ) per ml in a solution of 10mM MgCl2 and 0 . 02% Silwet L-77 . Two- week-old seedlings were dip inoculated with bacteria and kept covered for 1 h . The amount of bacteria in plants was analyzed at 1 h after dipping ( day 0 ) and 3 days after dipping ( day 3 ) . Bacterial growth was determined as described previously [45] .
|
It has been known that temperature modulates plant immune responses , but the molecular mechanisms underlying this modulation are unknown . Our study describes a novel finding that the NB-LRR type of R or R-like protein is the temperature-sensitive component of plant defense responses . R or R-like proteins have ‘receptor’ like functions involved in specific recognition of pathogens . Through genetic screens and targeted mutagenesis , we found that alterations in the R-like gene SNC1 and the R gene N can change temperature sensitivity of defense responses . Further , an elevated temperature reduced the nuclear accumulation of SNC1 , which likely contributes to the inhibition of disease resistance at high temperatures . Our study indicates that NB-LRR proteins mediate temperature sensitivity in plant immune responses .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"plant",
"biology/plant-biotic",
"interactions",
"genetics",
"and",
"genomics",
"plant",
"biology/plant-environment",
"interactions"
] |
2010
|
Temperature Modulates Plant Defense Responses through NB-LRR Proteins
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Cerebral sparganosis is the most serious complication of human sparganosis . Currently , there is no standard for the treatment of inoperable patients . Conventional-dose praziquantel therapy is the most reported treatment . However , the therapeutic outcomes are not very effective . High-dose praziquantel therapy is a useful therapeutic choice for many parasitic diseases that is well tolerated by patients , but it has not been sufficiently evaluated for cerebral sparganosis . This study aims to observe the prognoses following high-dose praziquantel therapy in inoperable patients and the roles of MRI and peripheral eosinophil absolute counts during follow-up . Baseline and follow-up epidemiological , clinical , radiological and therapeutic data related to 10 inoperable patients with cerebral sparganosis that were treated with repeated courses of high-dose praziquantel therapy , with each course consisting of 25 mg/kg thrice daily for 10 days were assessed , followed by analyses of the prognoses , MRI findings and peripheral eosinophil absolute counts . Baseline clinical data: the clinical symptoms recorded included seizures , hemiparesis , headache , vomiting and altered mental status . Peripheral blood eosinophilia was found in 3 patients . The baseline radiological findings were as follows . Motile lesions were observed in 10 patients , including aggregated ring-like enhancements , tunnel signs , serpiginous and irregular enhancements . Nine of the 10 patients had varying degrees of white matter degeneration , cortical atrophy and ipsilateral ventricle dilation . The follow-up clinical data were as follows . Clinical symptom relief was found in 8 patients , symptoms were eliminated in 1 patient , and symptoms showed no change from baseline in 1 patient . Peripheral blood eosinophilia was found in 2 patients . The follow-up radiological findings were as follows . Motile lesions that were transformed into stable , chronic lesions were found in 8 patients , and motile lesions that were eliminated completely were found in 2 patients . High-dose praziquantel therapy for cerebral sparganosis is effective . The radiological outcomes of motile lesions are an important indicator during the treatment process , especially during follow-ups after clinical symptoms have improved . Peripheral eosinophil absolute counts cannot be used as an effective prognostic indicator .
Sparganosis is a rare parasitic disease caused by an infection by the second-stage larvae of Spirometra mansoni , also called “sparganum” . There is a higher prevalence of this disease in East Asian countries , such as China , Thailand , Korea and Japan , although sparganosis also occurs occasionally in Europe , North Americaand South America[1–8] . Although a series of cases have been reported in recent years , the prevalence of sparganosis is still likely underestimated . Because of limited computed tomography ( CT ) and magnetic resonance imaging ( MRI ) technology and limited clinical experience , many cases in developing countries are likely unreported[9] . Sparganum can be found in many soft tissues of the human body , such as subcutaneous tissue , muscles , breast tissue , peritoneum and pleura , which are primarily composed of connective tissue[10–12] . Cerebral sparganosis is defined as a brain infection of sparganum , which is the most serious complication of human sparganosis . By combining CT and MRI data , epidemiological history , enzyme-linked immunosorbent assay ( ELISA ) testing for parasitic antibodies and stereotactic biopsy , most cases of sparganosis can be diagnosed[13] . Currently , it is generally believed that the most effective treatment for cerebral sparganosis is the surgical removal of the sparganum[2 , 3 , 14] . However , the choice of treatment is a challenge for inoperable patients , including those with multifocal and surgical contraindicated lesions . Currently , there is no standard for the treatment of inoperable patients , including anthelmintic treatments . High-dose praziquantel therapy has been applied to cysticercosis , and preliminary results were encouraging[15 , 16] . However , although several case reports in recent years have shown that high-dose praziquantel therapy for cerebral sparganosis has favorable outcomes , there is still insufficient clinical experience , and no systematic follow-up studies have been performed in inoperable patients[17 , 18] . The purpose of this study is to observe the prognoses following high-dose praziquantel therapy for cerebral sparganosis , to investigate the roles of MRI and peripheral eosinophil absolute counts in therapy and to further provide clinical treatment , follow-up evaluation and management experience for cerebral sparganosis , especially for inoperable patients .
The medical records and radiological data for 10 patients , who were registered between 2013 and 2017 , were reviewed retrospectively . These patients included 7 males and 3 females , aged between 7 and 45 years old , with a mean age of 20 . 3 years old . All patients were diagnosed based on a collection of findings , including ingestion history associated with sparganum , clinical manifestations , positive ELISA results of a sparganum antibody in serum and at least two MRI features of cerebral sparganosis ( aggregated ring-like enhancements , serpiginous enhancements , irregular enhancements , tunnel signs or migration signs ) . The presence of antibodies against S . mansoni in serum was assessed by ELISA kits ( # JL 0702193 , Jianlun Biology Technology Co . , LTD , Guangzhou , P . R . China ) . All patients underwent stool examinations . All patients were inoperable due to the following reasons . First , there were multifocal lesions and functional deficiencies . Second , the lesions were in areas where operation was contraindicated . Third , the patients refused invasive treatment . All patients were treated with high-dose praziquantel . The high-dose praziquantel treatment consisted of daily praziquantel at a dose of 75 mg per kilogram of body weight , which was administered in three divided doses , for 10 days . Dexamethasone was administered if symptoms exacerbated with praziquantel therapy . 5 mg was given initially followed by 10 mg/day until the end of the 10 day course and then tapered with 5 mg/ for 3 days and then 4 . 5 mg/day for 5 days . The patients who presented with symptomatic epilepsy were treated with oxcarbazepine . Daily oxcarbazepine at a dose of 10 mg per kilogram of body weight , which was administered in two divided doses , was used to treat parasite-induced symptomatic epilepsy . The anticonvulsive treatment was slowly tapered when the seizures discontinued[1 , 17 , 19 , 20] . Praziquantel was administered in 10 day courses every 3 months until there was clinical improvement and serial MRI studies showed disappearance of the initial enhancing lesions or replaced by stable , chronic lesions . Repeated 10 day courses were also given if new symptoms recurred . At least 3 follow-up MRI assessments were performed for each patient . The first MRI assessment was the baseline examination , performed before antiparasitic treatment . For the purpose of assessing clinical improvements , follow up times were grouped into time periods of 3–6 months , 7–9 months , 10–13 months and greater than 13 months . Peripheral blood eosinophil absolute counts tests were performed prior to steroid treatment to exclude the effects of steroids . Standard stool microscopy for S . stercoralis and other parasites was performed in all patients to exclude their effects on eosinophil counts . Peripheral blood eosinophil absolute counts of more than 0 . 5×109/L were considered diagnostically to be eosinophilia . The patients avoided sparganum infection risk factors after antiparasitic treatments . Therefore , the possibility of reinfection decreased to a minimum level in the follow-up period . All patients underwent conventional and contrast-enhanced MRI scans . The scanning equipment used in this study was a 3 . 0-T superconductive MRI system ( GE Healthcare , Milwaukee , Wisconsin ) . The MRI scanning parameters included a 3-mm slice thickness and a 0–0 . 5-mm interval . The scanning sequences included an axial T2-weighted propeller sequence ( 4 , 600 ms/108 ms [repetition time ( TR ) /echo time ( TE ) ] ) ; an axial T1-weighted fluid-attenuated inversion-recovery ( FLAIR ) sequence ( 2 , 065 ms/22 ms [TR/TE] ) ; and axial diffusion-weighted imaging ( 4 , 000 ms/64 ms [TR/TE] , b = 1 , 000 s/mm2 ) . The enhanced MRI included axial , sagittal , and coronal slices , a T1-weighted FLAIR sequence ( 2 , 065 ms/22 ms [TR/TE] ) , and the contrast agent gadopentetate dimeglumine ( 0 . 1–0 . 2 mmol/kg ) . Our goal during the radiologic evaluations for both baseline and follow-up MRIs was to assess the motile and stable , chronic lesions . The radiological signs of motile lesions included aggregated ring-like enhancements , serpiginous enhancements , irregular enhancements , tunnel signs and migration signs . Aggregated ring-like enhancements , serpiginous enhancements and irregular enhancements were defined as ring-shaped or beaded enhancements , respectively , observed in an enhanced MRI[21] . A tunnel sign was defined as a path-like lesion with an enhanced edge that exhibited hypointensity on T1-weighted image ( T1WI ) and iso-/hyperintensity on T2WI[9] . The appearance of a new lesion in a follow-up MRI or a different location or shape of a lesion compared to the baseline MRI was considered to be a migrated lesions[13] . The stable , chronic lesions included white matter degeneration , cortical atrophy and ipsilateral ventricle dilation . All baseline and follow-up MRI images were reviewed by two experienced radiologists .
The demographic and baseline clinical findings from the 10 patients are shown in S1 Table . The patients came from seven provinces in China , including Heilongjiang , Hubei , Hunan , Jiangsu , Jiangxi , Fujian , and Yunnan provinces . During patient histories , all patients were found to reside in or have traveled to an endemic area and to have a history of ingesting uncooked frog or snake meat or of drinking contaminated water ( contaminated water is defined as uncooked water in an endemic area ) many times[1] . The primary symptoms in the baseline clinical findings included seizures in 7 patients , hemiparesis in 5 patients , headache in 5 patients , vomiting in 1 patient and altered mental status in 1 patient . The median duration of symptoms from onset to definite diagnosis was 45 months ( range of 3 months to 188 months ) . Three patients showed peripheral blood eosinophil absolute counts of greater than 0 . 5×109/L , and the other 7 patients were normal . The high-dose praziquantel therapy was well-tolerated in all patients . One patient received 1 treatment course , 5 received two treatment courses , 2 received three treatment courses , 1 received four treatment courses , and 1 received five treatment courses . Six patients ( patients 1 , 2 , 3 , 5 , 6 , and 7 ) presented with increased frequencies of seizures or the clinical symptoms of transient aggravation either during the course of treatment or 3 to 5 days after treatment . For each patient , at least 3 MRI scans and clinical follow-up studies were performed over periods ranging from 10 to 40 months . The follow-up information on clinical outcomes for the 10 patients are shown in Table 1 and Fig 1 . The results of overall clinical outcomes showed that the symptoms were relieved without the appearance of new symptoms in 8 patients , the symptoms were eliminated completely in 1 patient , and the symptoms were aggravated in the 1st through 3rd follow-ups but were relieved in the last 2 follow-ups in 1 patient . The final clinical outcome of the patient with clinical symptom fluctuations was assessed as no change from baseline clinical symptoms . Two patients showed peripheral blood eosinophil absolute counts greater than 0 . 5×109/L at follow-up . The times taken for the radiological signs of motile lesions to disappear in follow-up MRIs from the 10 patients are summarized in Table 2 . Ten patients showed motile lesions in their baseline MRIs , including aggregated ring-like enhancements ( 80% , 8/10 patients ) , serpiginous enhancements ( 70% , 7/10 patients ) , irregular enhancements ( 90% , 9/10 patients ) and tunnel signs ( 50% , 5/10 patients ) . The patients had varying degrees of white matter degeneration ( 90% , 9/10 patients ) , cortical atrophy ( 80% , 8/10 patients ) and ipsilateral ventricle dilation ( 80% , 8/10 patients ) in their baseline MRIs . In the overall follow-up MRIs , 10 patients showed a disappearance of motile lesions , occurring at 3 , 4 , 6 , 7 , 9 , 10 , 12 , 23 , and 27 months after treatment , 80% ( 8/10 patients ) of motile lesions were transformed into stable , chronic lesions , and 20% ( 2/10 patients ) of motile lesions were eliminated completely ( Fig 2 and Fig 3 ) . All initial stable , chronic lesions were preserved without any changes in follow-up MRIs . Migration signs were found in 2 patients ( patients 4 and 8 ) , at 3 , 7 , 10 , and 17 months after treatment . The distributions of the motile and migrated lesions from the 10 patients are shown in Fig 4 .
This study reports the effect of high-dose praziquantel treatment for cerebral sparganosis through clinical and MRI follow-up evaluations . Most patients achieved satisfactory treatment results , including the relief of clinical symptoms and the disappearance of enhancing lesions . In addition , we also observed that the time to clinical symptom relief did not match the time to enhanced lesion disappearance . Cerebral sparganosis is a rare parasitic infection . Humans are intermediate hosts for Spirometra mansoni . The adult stage of S . mansoni primarily parasitizes the intestines of cats and dogs . The adult S . mansoni produces eggs in the intestines of cats and dogs that are then excreted in feces . Coracidia hatch from the eggs in contaminated water . Then , coracidia are ingested by Cyclops , which are the first intermediate hosts , and develop into procercoids , which are the first-stage S . mansoni larvae . Then , Cyclops is ingested by secondary intermediate hosts including frogs , snakes or humans , and procercoids develop into plerocercoid larvae , which are the second-stage larvae . Finally , the definitive hosts are infected by the second intermediate hosts , and plerocercoid larvae mature into adult S . mansoni[2] . Thus , the cycle of S . mansoni is completed . Sparganum has the general life habits of a parasite: wandering and parasitism . The body of the larvae consists of bundles of longitudinal eosinophilic smooth muscle fibers . The anterior end of the larvae contains a characteristic groove called a scolex[9] . Proteolytic enzymes secreted by the scolex allow the sparganum to hydrolyze proteins and peptides , thus facilitating the penetration of loose connective tissue[22] . The smooth muscle bundles and proteolytic enzymes provide a strong contractile force and penetrability for wandering larvae . Previous animal studies have suggested that the route of sparganum migration into the brain involves the sparganum taking advantage of proteolytic enzymes to move through the gastrointestinal tract to the peritoneum or pleura . The sparganum then penetrates the subcutaneous tissue of the neck and passes through the foramen of the skull base to reach the brain[2] . The risk factors for S . mansoni infection include living in an epidemic area , eating raw/uncooked frog or snake meat , using raw frog or snake meat as a poultice on an open wound , and drinking unfiltered water contaminated with copepods harboring the parasite . The incubation period of sparganosis is not well defined , and the parasite is thought to live in the human body for decades[23] . In our study , an epidemiological history associated with sparganum , including ingesting frog or snake meat ( 70% , 7/10 patients ) or drinking contaminated water ( 40% , 4/10 patients ) many times , was an important factor in the diagnosis of patients with this condition . Several scholars have suggested that children and young people are more prone to cerebral sparganosis , particularly those 5 to 30 years old[2 , 24 , 25] . In this study , the mean age at diagnosis was 20 . 3 years old . Possible reasons for this vulnerability include the following: the immature development of the immune system and the blood-brain barrier can allow the larvae to survive in the human body longer and provide easier access to the nervous system; and young people or children usually play in pools or brooks , which can increase the chances of contacting parasite-contaminated water . Cerebral sparganosis is the most serious human sparganosis complication . Clinical manifestations of cerebral sparganosis vary and often depend on the cerebral region that is infected[18 , 26 , 27] . Because of the more widespread motile or chronic lesions in the white matter and the excessive electrical discharge of brain cells caused by live larvae migrating in the cortex or subcortical areas , seizures are the most commonly observed symptom[2] . In our study , 70% ( 7/10 patients ) of the patients presented with seizures . In addition , our patients showed several nonspecific clinical symptoms including headache ( 50% , 5/10 patients ) , vomiting ( 10% , 1/10 patient ) , hemiparesis ( 50% , 5/10 patients ) and altered mental status ( 10% , 1/10 patient ) . Based on these nonspecific clinical symptoms , it is very difficult to make a diagnosis . However , based on our results , we believe that , if these nonspecific clinical symptoms change frequently in a short time , this could be a useful clue indicating wandering live larvae . The parasite survival status , parasite-induced secondary acute and chronic inflammations , and the responses after treatment are all associated with the pathological basis of imaging findings[28] . The aggregated ring-like enhancements may reflect the cross-sections of small abscesses . The characteristic edema signal may reflect local inflammation . The signs of serpiginous enhancements and irregular enhancements may reflect the slender and twisted forms of the worm’s body . The tunnel signs can be visualized between the primary lesion and the surrounding lesions , suggesting the migration of a live worm and the formation of eosinophilic granulomas . Sometimes , multiple lesions around the worm body suggest the different stages of eosinophilic granulomas . The migration sign is often defined as changes in new lesion locations , shapes , and styles during follow-up MRIs , relative to the initial lesions observed in the baseline MRI , which strongly suggests the movement of live worm larvae[9] . The above radiological signs also indicate that the lesions are motile lesions . Moreover , stable , chronic lesions are related to chronic inflammation[9] . In our results , we noticed that the time to the disappearance of the radiological signs of motile lesions usually lagged behind the time to clinical symptom improvements . Meanwhile , we also observed that the radiological outcomes of motile lesions varied with different courses of treatment . In addition , in our results , most of the motile lesions transformed into stable , chronic lesions after treatment , which often suggests that the clinical symptoms will improve but will not be eliminated . If the follow-up MRI shows that no stable , chronic lesions occur after the motile lesions disappear , this often indicates a good prognosis . We also observed that the frontal , parietal , and temporal lobes and the basal ganglia were often infected with motile and migrated lesions but the occipital lobe and the cerebellum did not present the same trend . Moreover , based on the results of the distribution of the migrated lesions , we noticed that the migrated lesions usually occurred in close proximity to the initial lesion . These results are in accordance with those of a previous study[13] . For migrated lesions , we recommend that , when migration signs appear in a follow-up MRI , the choice of a sequential treatment between praziquantel treatments and surgical resection should be based on the severity , location and number of lesions . Peripheral blood eosinophilia suggests the presence of a parasitic infection . However , only three patients ( 30% , 3/10 patients ) showed peripheral eosinophil absolute counts >0 . 5×109/L in the baseline tests . The follow-up results showed that the peripheral eosinophil absolute counts were often <0 . 5×109/L , regardless of clinical outcomes . Therefore , we believe that there were no direct relationships between peripheral eosinophil absolute counts and clinical outcomes . In contrast , many studies have shown that ELISAs of the serum and cerebral spinal fluid ( CSF ) have a high sensitivity for the diagnosis of cerebral sparganosis[13 , 18 , 24] . Several studies have reported a sensitivity between 80% and 100%[3 , 24] . Therefore , cerebral sparganosis should be diagnosed based on epidemiological history , clinical manifestations , ELISA testing and radiological findings . The sparganum cannot reproduce , but it has a long lifespan ( it can survive for up to 35 years ) in the human body[24] . Therefore , surgical resection or stereotactic surgery is recommended as an effective treatment . However , there is currently no specific treatment for inoperable patients . The pharmacological and toxicological effects of praziquantel on sparganum include two aspects . First , praziquantel can cause spastic paralysis and dissolve the smooth muscle bundles in the worm . Second , praziquantel can result in epidermal erosion by damaging the skin of the worms , which then exposes the surface antigens of the parasite[19] . Moreover , the pharmacodynamics of praziquantel therapy for cerebral sparganosis also has two characteristics . On the one hand , praziquantel reaches a peak plasma concentration 2 hours after administration and decreases rapidly in a short time . On the other hand , although praziquantel penetrates the CSF quickly during the biological half-life of the drug , it enters parasites slowly . Due to the barriers between human bodily fluids and parasites , it is necessary to increase the drug concentration 7–10 times in the CSF or brain parenchyma to effectively kill the parasites[17 , 19] . Previous studies have shown that conventional-dose praziquantel therapy ( 120–200 mg/kg divided over 2–4 days ) is often considered to be less effective [2 , 3 , 14 , 24] . One study reported that 3 patients experienced migrated lesions among 5 patients who received conventional-dose praziquantel therapy , and only one patient presented with clinical symptom improvement among the 5 patients[14] . However , there have been several case reports that have shown that high-dose praziquantel therapy ( 500–750 mg/kg divided over 7–10 days ) is effective for cerebral sparganosis[17 , 18] . One case report discussed a single adult patient who received high-dose praziquantel therapy , was followed for 3 years and presented with significantly improved clinical symptoms and MRI findings[17] . Therefore , based on the pharmacokinetics of praziquantel and the viewpoints of Gonzenbach RR et al . , we believe that high doses and multiple doses ( daily praziquantel at a dose of 75 mg per kilogram of body weight , which was administered in three divided doses ) over a relatively long course ( 10 days per course ) of treatment could be more conducive to allow praziquantel to cross the blood-brain barrier and enter parasites to achieve pharmacological effects[17] . Even at this dose , praziquantel is generally well tolerated , as supported by the treatment of other parasites[29] . In our study , 8 patients ( 80% , 8/10 patients ) showed gradual improvements after multiple high-doses of praziquantel treatments . Compared with baseline data , the decreased seizure frequency was found in 6 patients ( 75% , 6/8 patients ) and neurological deficit improved such as limb weakness relieving , and level of muscle power increasing was observed in 4 patients ( 50% , 4/8 patients ) at end of follow-up . One patient ( 10% , 1/10 patient ) recovered with normal life after treatment accompanied by the elimination of clinical symptoms . Two of the above 9 patients showed motile lesions that disappeared completely in MRI tests . In addition , one patient ( 10% , 1/10 patient ) experienced fluctuations in clinical symptoms and radiological findings . However , relative to baseline data , the patient did not show significant decreased seizure frequency or mental status improvements . We speculate that these responses could be related to the incomplete immune system development at the young age of onset and the abnormal immune response caused by a longer infection period . Moreover , during the process of treatment or the post treatment period , we found that 6 patients ( 60% , 6/10 patients ) showed clinical symptoms of transient aggravation . We observed that the prognoses of these patients were often improved at follow-up; 1 patient recovered , and 5 patients obtained relief . We speculate that this type of post treatment reaction may be associated with the disintegration of the worm’s body caused by the drug effects , which usually associated with the good long-term prognosis . Furthermore , we found that the clinical improvements primarily occurred within 9 months after the first treatment for most patients . However , the improvements of the few patients with poor initial treatment outcomes occurred during 10–30 months . Finally , we acknowledge some limitations of the present study . First , the follow-up intervals of this study are relatively long , which makes more subtle therapeutic changes difficult to observe . Second , because of the retrospective nature of this study , is no comparative trial was designed , although previous studies that have reported conventional-dose praziquantel therapy for cerebral sparganosis have been applied as a comparison . Third , also due to limitations of a retrospective study , the baseline and follow-up information for each patient cannot cover all required examination items , such as head CT scan and specific antibody detection in the CSF by ELISA . Further comparative studies will be conducted in the future . This work provides evidence to suggest that high-dose praziquantel therapy for cerebral sparganosis is effective and is a promising alternative for the treatment of cerebral sparganosis , particularly for inoperable patients . Peripheral eosinophil absolute counts cannot be used as an effective prognosis indicator . The radiological outcomes of motile lesions are an important indicator of the process of the treatment . Motile lesions will generally become stable , chronic lesions after treatment , and timely and effective treatment may eliminate chronic lesions in the brain . Because MRI improvements may lag behind clinical symptom improvements , it is necessary to perform MRI follow-ups 1–3 times after clinical symptoms show improvements . We hope that our findings may bring some inspiration to the treatment and management of cerebral sparganosis .
|
Sparganosis is a rare parasitic disease with a high prevalence in East Asia . Because of limited radiological technology and clinical experience , the prevalence of cerebral sparganosis is likely underestimated in developing countries . Cerebral sparganosis is the most serious complication of human sparganosis . Currently , it is generally believed that the most effective treatment for cerebral sparganosis is surgical treatment . However , the choice of treatment is a challenge for inoperable patients , including those with multifocal lesions or lesions in deep structures or important functional areas and those refusing invasive treatment due to personal willingness . Currently , there is no standard for the treatment of inoperable patients . In addition , anthelmintic treatment for sparganosis has rarely been reported in the literature . High-dose praziquantel therapy is a useful therapeutic choice for many cerebral parasitic diseases , including neurocysticercosis , and is well tolerated for patients , but it has not been sufficiently evaluated for the treatment of cerebral sparganosis . This study aims to describe the clinical , radiological and therapeutic data following high-dose praziquantel therapy for ten inoperable patients . All patients reached clinical cure after one to five courses . These results suggest that high-dose praziquantel therapy for cerebral sparganosis could achieve favorable outcomes and that MRI plays an important role in follow-up , especially when clinical symptoms have improved .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
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2019
|
Follow-up study of high-dose praziquantel therapy for cerebral sparganosis
|
When collecting large amounts of neuroimaging data associated with psychiatric disorders , images must be acquired from multiple sites because of the limited capacity of a single site . However , site differences represent a barrier when acquiring multisite neuroimaging data . We utilized a traveling-subject dataset in conjunction with a multisite , multidisorder dataset to demonstrate that site differences are composed of biological sampling bias and engineering measurement bias . The effects on resting-state functional MRI connectivity based on pairwise correlations because of both bias types were greater than or equal to psychiatric disorder differences . Furthermore , our findings indicated that each site can sample only from a subpopulation of participants . This result suggests that it is essential to collect large amounts of neuroimaging data from as many sites as possible to appropriately estimate the distribution of the grand population . Finally , we developed a novel harmonization method that removed only the measurement bias by using a traveling-subject dataset and achieved the reduction of the measurement bias by 29% and improvement of the signal-to-noise ratios by 40% . Our results provide fundamental knowledge regarding site effects , which is important for future research using multisite , multidisorder resting-state functional MRI data .
Acquiring and sharing large amounts of neuroimaging data have recently become critical for bridging the gap between basic neuroscience research and clinical applications , such as the diagnosis and treatment of psychiatric disorders ( Human Connectome Project [HCP] [1] [http://www . humanconnectomeproject . org/]; Human Brain Project [https://www . humanbrainproject . eu/en/]; UK Biobank [http://www . ukbiobank . ac . uk/]; and Strategic Research Program for Brain Sciences [SRPBS] [2] [https://bicr . atr . jp/decnefpro/] ) [3–5] . When collecting large amounts of data associated with psychiatric disorders , it is necessary to acquire images from multiple sites because it is nearly impossible for a single site to collect a large amount of neuroimaging data from many participants ( Connectomes Related to Human Disease [CRHD] [https://www . humanconnectome . org/disease-studies] , Autism Brain Imaging Data Exchange [ABIDE] , and SRPBS ) [2 , 6–8] . In 2013 , the Japan Agency for Medical Research and Development ( AMED ) organized the Decoded Neurofeedback ( DecNef ) Project . The project determined a unified imaging protocol on 28 February 2014 ( https://bicr . atr . jp/rs-fmri-protocol-2/ ) and has collected multisite resting-state functional magnetic resonance imaging ( rs-fMRI ) data using 14 scanners across eight research institutes for the last 5 y . The collected dataset encompasses 2 , 239 participants and five disorders and is publicly shared through the SRPBS multisite multidisorder database ( https://bicr-resource . atr . jp/decnefpro/ ) . This project has enabled the identification of resting-state functional connectivity MRI ( rs-fcMRI ) -based biomarkers of several psychiatric disorders that can be generalized to completely independent cohorts [2 , 8–10] . However , a multisite dataset with multiple disorders raises difficult problems that are not present in a single site–based dataset ( e . g . , HCP and UK Biobank ) . That is , our experience in the SRPBS database demonstrated difficulties in differences due to scanner type , imaging protocol , and patient demographics [10–13] , even when a unified protocol was determined . Moreover , unpredictable differences in participant population can often exist between sites . Therefore , researchers must work with heterogeneous neuroimaging data . In particular , site differences represent a barrier when extracting disease factors by applying machine-learning techniques to such heterogeneous data [14] because disease factors tend to be confounded with site factors [2 , 8 , 10–13 , 15] . This confounding occurs because a single site ( or hospital ) is apt to sample only a few types of psychiatric disorders ( e . g . , primarily schizophrenia [SCZ] and autism spectrum disorder [ASD] from sites A and B , respectively ) . Although robust generalization across sites could be possible as long as the pattern of the disease factors is sufficiently different from the pattern due to the site differences [11] , these factors depend on dataset and type of disease . To properly manage these heterogeneous data , it is important for them to be harmonized between sites [16–19] . Moreover , a deeper understanding of these site differences is essential for efficient harmonization of the data . Site differences consist of two types of biases: engineering bias ( measurement bias ) and biological bias ( sampling bias ) . Measurement bias includes differences in the properties of MRI scanners—such as imaging variables , field strength , MRI manufacturers , and scanner models—whereas sampling bias refers to differences in participant groups between sites . In this study , we used the word “bias” to indicate a systematic shift from the global population at a given site or with given imaging variables . Previous studies have investigated the effect of measurement bias on resting-state functional connectivity by using a traveling-subject design [20] , wherein multiple participants travel to multiple sites to assess measurement bias [7] . By contrast , researchers to date have only speculated about sampling bias . For example , differences in the clinical characteristics of patients examined at different sites are presumed to underlie the stagnant accuracy of certain biomarkers , even after combining data from multiple sites [12] . Furthermore , to the best of our knowledge , no study has mathematically defined sampling bias or conducted quantitative analyses of its effect size , which is likely because the decomposition of site differences into measurement bias and sampling bias is a complex process . To achieve this aim , we combined a separate traveling-subject rs-fMRI dataset with the SRPBS multidisorder dataset . Simultaneous analysis of the datasets enabled us to divide site differences into measurement bias and sampling bias and quantitatively compare their effect sizes on resting-state functional connectivity with those of psychiatric disorders . Furthermore , our detailed analysis of measurement and sampling biases enabled us to investigate the origin of each bias in multisite datasets for the first time . For measurement bias , we quantitatively compared the magnitude of the effects between different imaging variables , fMRI manufacturers , and the number of channels per coil in each fMRI scanner . We further examined two alternative hypotheses regarding the mechanisms underlying sampling bias: one hypothesis assumes that each site samples subjects from a common population . In this situation , sampling bias occurs because of the random sampling of subjects , which results in incidental differences in the patients’ characteristics among the sites . The second hypothesis assumes that each site samples subjects from different subpopulations . In this situation , sampling bias occurs because of sampling from subpopulations with different characteristics . For example , assume multiple sites plan to collect data from the same population of patients with major depressive disorder ( MDD ) . Subtypes of MDD exist within the population , such as atypical depression and melancholic depression [21 , 22]; therefore , one subpopulation may contain a large proportion of patients with atypical depression , whereas another subpopulation may contain a large proportion of patients with melancholic depression . Therefore , in some instances , atypical depression may be more frequent among patients at site A , whereas melancholic depression may be more frequent among patients at site B . The basic protocol for collecting large-scale datasets differs between these two hypotheses; thus , it is necessary to determine the hypothesis that most appropriately reflects the characteristics of the SRPBS dataset . In the former situation , one would simply need to collect data from a large number of individuals , even with a small number of sites . In the latter situation , a larger number of sites would be required to obtain truly representative data . To overcome the limitations associated with site differences , we developed a novel harmonization method that enabled us to subtract only the measurement bias by using a traveling-subject dataset . We investigated the extent that our proposed method could reduce measurement bias and improve the signal-to-noise ratio . We compared its performance to those of other commonly used harmonization methods . All data utilized in this study can be downloaded publicly from the DecNef Project Brain Data Repository at https://bicr-resource . atr . jp/decnefpro/ .
We used two rs-fMRI datasets: ( 1 ) the SRPBS multidisorder dataset and ( 2 ) a traveling-subject dataset . We computed the region of interest ( ROI ) -based pairwise correlations as a measure of functional connectivity . For each participant , the temporal correlations of rs-fMRI blood-oxygen-level dependent ( BOLD ) signals between pairs of ROIs were computed after averaging each voxelwise BOLD signal in each ROI . There are some candidates for the measure of functional connectivity such as the tangent method and partial correlation [11 , 23]; however , we used Pearson’s correlation coefficients because they have been the most commonly used values in previous studies . Functional connectivity was defined based on a functional brain atlas consisting of 268 nodes ( regions ) covering the whole brain , which has been widely utilized in previous studies [20 , 24–26] . The Fisher’s z-transformed Pearson’s correlation coefficients between the preprocessed BOLD signal time courses of each possible pair of nodes were calculated and used to construct 268 × 268 symmetrical connectivity matrices in which each element represents a connection strength , or edge , between two nodes . We used 35 , 778 connectivity values [ ( 268 × 267 ) /2] of the lower triangular part of the connectivity matrix . To briefly investigate any site effect on functional connectivity , we applied a one-way ANOVA with Site ( 9 sites ) as a factor to the functional connections in the SRPBS multidisorder dataset and recorded the number of significant differences between sites . We set the threshold to p < 0 . 05 , after Bonferroni correction . As a result , >30% of all connections ( 11 , 888/35 , 778 ) were significantly different between sites . To quantitatively investigate the site differences in the rs-fcMRI data , we identified measurement biases , sampling biases , and disorder factors . We defined measurement bias for each site as a deviation of the connectivity value for each functional connection from its average across all sites . We assumed that the sampling biases of the HCs and patients with psychiatric disorders differed from one another . Therefore , we calculated the sampling biases for each site separately for HCs and patients with each disorder . Disorder factors were defined as deviations from the HC values . Sampling biases were estimated for patients with MDD and SCZ because only these patients were sampled at multiple sites . Disorder factors were estimated for MDD , SCZ , and ASD because patients with OCD were not scanned using the unified protocol . It is difficult to separate site differences into measurement and sampling biases using the SRPBS multidisorder dataset alone because these two types of bias covaried across sites . Different samples ( participants ) were scanned using different variables ( scanners and imaging protocols ) . In contrast , the traveling-subject dataset included only measurement bias because the participants were fixed . By combining the traveling-subject dataset with the SRPBS multidisorder dataset , we simultaneously estimated measurement bias and sampling bias as different factors are affected by different sites . We utilized a constrained linear regression model to assess the effects of both types of bias and disorder factors on functional connectivity , as follows . In the regression model for the SRPBS multidisorder dataset , the connectivity values of each participant in the SRPBS multidisorder dataset were composed of the sum of the average connectivity values across all participants and all sites at baseline , measurement bias , sampling bias , and disorder factors . The combined effect of participant factors ( individual difference ) and scan-to-scan variations was regarded as noise . In the regression model for the traveling-subject dataset , the connectivity values of each participant for a specific scan in the traveling-subject dataset were composed of the sum of the average connectivity values across all participants and all sites , participant factors , and measurement bias . Scan-to-scan variation was regarded as noise . For each participant , we defined the participant factor as a deviation of connectivity values from the average across all participants . We estimated all biases and factors by simultaneously fitting the aforementioned two regression models to the functional connectivity values of the two different datasets . For this regression analysis , we used data from participants scanned using a unified imaging protocol in the SRPBS multidisorder dataset and from all participants in the traveling-subject dataset . In summary , each bias or each factor was estimated as a vector that included a dimension reflecting the number of connectivity values ( 35 , 778 ) . Vectors included in our further analyses are those for measurement bias at 12 sites , sampling bias of HCs at six sites , sampling bias for patients with MDD at three sites , sampling bias for patients with SCZ at three sites , participant factors of nine traveling subjects , and disorder factors for MDD , SCZ , and ASD . Note that since patients with ASD were scanned at one site , we could not estimate the sampling bias of ASD . Thus , the sampling bias was included in the disorder factor of ASD . For each connectivity , the regression model can be written as follows: Connectivity=xmTm+xshcTshc+xsmddTsmdd+xssczTsscz+xdTd+xpTp+const+e , where m represents the measurement bias ( 12 sites × 1 ) , shc represents the sampling bias of HCs ( 6 sites × 1 ) , smdd represents the sampling bias of patients with MDD ( 3 sites × 1 ) , sscz represents the sampling bias of patients with SCZ ( 3 sites × 1 ) , d represents the disorder factor ( 3 × 1 ) , p represents the participant factor ( 9 traveling subjects × 1 ) , const represents the average functional connectivity value across all participants from all sites , and e∼N ( 0 , γ−1 ) represents noise . xm , xshc , xsmdd , xsscz , xd , xp are vectors represented by 1-of-K binary coding ( more details are reported in “Estimation of biases and factors” in the Methods section ) . To eliminate the uncertainty of the constant term , we estimated measurement bias and each sampling bias by imposing constraints so that their average across sites would be 0 . To quantitatively evaluate the magnitude of the effect of measurement and sampling biases on functional connectivity , we compared the magnitudes of both types of bias ( m , shc , smdd , and sscz ) with the magnitudes of psychiatric disorders ( d ) and participant factors ( p ) . For this purpose , we investigated the magnitude distribution of both biases , as well as the effects of psychiatric disorders and participant factors on functional connectivity over all 35 , 778 elements in a 35 , 778-dimensional vector ( see S1 Text , S1A and S1B Fig ) . To quantitatively summarize the magnitude of the effect of each factor , we calculated the first , second , and third statistical moments of each distribution ( Fig 2A ) . Based on the mean values and cube roots of the third moments , all distributions could be approximated as bilaterally symmetric with a mean of zero . Thus , distributions with larger squared roots of the second moments ( standard deviations ) affect more connectivities with larger effect sizes ( Fig 2B ) . The value of the standard deviation was largest for the participant factor ( 0 . 0662 ) , followed by these values for the measurement bias ( 0 . 0411 ) , the SCZ factor ( 0 . 0377 ) , the MDD factor ( 0 . 0328 ) , the ASD factor ( 0 . 0297 ) , the sampling bias for HCs ( 0 . 0267 ) , sampling bias for patients with SCZ ( 0 . 0217 ) , and sampling bias for patients with MDD ( 0 . 0214 ) . To compare the sizes of the standard deviation of the magnitude distribution between participant factors and measurement bias , we evaluated the variance of each distribution . All pairs of variances were analyzed using Ansari–Bradley tests . Our findings indicated that the variances of magnitude distributions in 10 of 12 measurement biases were significantly larger than in the MDD factor; the variances of magnitude distributions in seven of 12 measurement biases were significantly larger than in the SCZ factor; and the variances of all magnitude distributions in measurement biases were significantly larger than the variance of the MDD factor ( S6 Table ) . The largest variance of magnitude distribution in the sampling bias was significantly larger than in the MDD factor ( S7 Table ) . Variances of magnitude distributions in all participant factors were significantly larger than that in all measurement biases ( 9 participant factors × 12 measurement biases = 108 pairs; W*: mean = –59 . 80 , maximum = –116 . 81 , minimum = –3 . 69; p-value after Bonferroni correction: maximum = 0 . 011 , minimum = 0 , n = 35 , 778 ) . The standard deviation of the magnitude distribution in the participant factor was approximately twice that in the SCZ , MDD , and ASD factors . To investigate similarity in the patterns of effect on functional connectivity between the measurement bias and the disorder factors , we next calculated Pearson’s correlation coefficients between the 12 measurement biases and the factors of three diseases . As a result , we found a significant correlation ( mean = 0 . 13 ± 0 . 08 [1SD] , one-sample t test applied to absolute correlation value: t = 9 . 26 , p < 1 . 0×10−10 , df = 35 ) , and maximum value was |r| = 0 . 31 ( between the MDD factor and the measurement bias of Showa University [SWA] ) . This result indicates that the pattern of the measurement bias on functional connectivity was not sufficiently different from the patterns of disorder factors in our dataset . Furthermore , to quantitatively verify the magnitude relationship among factors , we calculated and compared the contribution size to determine the extent to which each bias type and factor explains the variance of the data in our linear model ( Fig 2C ) . After fitting the model , the b-th connectivity from subject a can be written as follows: Connectivitya , b=xmaTmb+xshcaTshcb+xsmddaTsmddb+xssczaTssczb+xdaTdb+xpaTpb+const+e . For example , the contribution size of measurement bias ( i . e . , the first term ) in this model was calculated as Contributionsizem=1Nm1Ns*N∑a=1Ns∑b=1N ( xmaTmb ) 2 ( xmaTmb ) 2+ ( xshcaTshcb ) 2+ ( xsmddaTsmddb ) 2+ ( xssczaTssczb ) 2+ ( xdaTdb ) 2+ ( xpaTpb ) 2+e2 , in which Nm represents the number of components for each factor , N represents the number of connectivities , Ns represents the number of subjects , and Contribution sizem represents the magnitude of the contribution size of measurement bias . This formula was used to assess the contribution sizes of individual factors . The results were consistent with the analysis of the standard deviation ( Fig 2A , middle ) . These results indicated that the effect size of the measurement bias on functional connectivity is smaller than that of the participant factor but is mostly larger than the disorder factors , which suggested that measurement bias represents a serious limitation in research regarding psychiatric disorders . Furthermore , the effect sizes of the sampling biases were comparable with those of the disorder factors . This finding indicates that sampling bias also represents a major limitation in psychiatric research . In addition , the effect size of the participant factor was much greater than that among patients with SCZ , MDD , or ASD . Such relationships make the development of rs-fcMRI-based classifiers of psychiatric or developmental disorders very challenging . If the disorder factor and site factor are confounded in functional connections , to develop robust and generalizable classifiers across multiple sites , we have to select disorder-specific and site-independent abnormal functional connections [2 , 8–10 , 15] . To evaluate the spatial distribution of the two types of bias and all factors in the whole brain , we utilized a previously described visualization method [27] to project connectivity information to anatomical ROIs . First , we quantified the effect of a bias or a factor on each functional connectivity as the median of its absolute values across sites or participants . Thus , we obtained 35 , 778 values , each of which was associated with one connectivity and represented the effect of a bias or factor on the connectivity . Next , we summarized these effects on connectivity for each ROI by averaging the values of all connectivities connected with the ROI ( see “Spatial characteristics of measurement bias , sampling bias , and each factor in the brain” in the Methods section ) . The average value represents the extent the ROI contributes to the effect of a bias or factor . By repeating this procedure for each ROI and coding the averaged value based on the color of an ROI , we were able to visualize the relative contribution of individual ROIs to each bias or factor in the whole brain ( Fig 3 ) . Consistent with the findings of previous studies , the effect of the participant factor was large for several ROIs in the cerebral cortex , especially in the prefrontal cortex , but small in the cerebellum and visual cortex [24] . The effect of measurement bias was large in inferior brain regions where functional images are differentially distorted depending on the phase-encoding direction [28 , 29] . Connections involving the medial dorsal nucleus of the thalamus were also heavily affected by both MDD , SCZ , and ASD . Effects of the MDD factor were observed in the dorsomedial prefrontal cortex and superior temporal gyrus , in which abnormalities have also been reported in previous studies [22 , 30 , 31] . Effects of the SCZ factor were observed in the left inferior parietal lobule , bilateral anterior cingulate cortices , and left middle frontal gyrus , in which abnormalities have been reported in previous studies [32–34] . Effects of the ASD factor were observed in the putamen , the medial prefrontal cortex , and the right middle temporal gyrus , in which abnormalities have also been reported in previous studies [10 , 11 , 35] . The effect of sampling bias for HCs was large in the inferior parietal lobule and the precuneus , both of which are involved in the default-mode network and the middle frontal gyrus . Sampling bias for disorders was large in the medial dorsal nucleus of the thalamus , left dorsolateral prefrontal cortex , dorsomedial prefrontal cortex , and cerebellum for MDD [22] and in the prefrontal cortex , cuneus , and cerebellum for SCZ [33] . We next investigated the characteristics of measurement bias . We first examined whether similarities among the estimated measurement bias vectors for the 12 included sites reflect certain properties of MRI scanners such as phase-encoding direction , MRI manufacturer , coil type , and scanner type . We used hierarchical clustering analysis to discover clusters of similar patterns for measurement bias . We used “correlation” as a distance metric for hierarchical clustering . This method has previously been used to distinguish subtypes of MDD , based on rs-fcMRI data [22] . As a result , the measurement biases of the 12 sites were divided into phase-encoding direction clusters at the first level ( Fig 4A ) . They were divided into fMRI manufacturer clusters at the second level and further divided into coil type clusters , followed by scanner model clusters . Furthermore , we quantitatively verified the relationship magnitude among factors by using the same model to assess the contribution of each factor ( Fig 4B; see “Quantification of site differences” in the Results section or “Analysis of contribution size” in the Methods section ) . The contribution size was largest for the phase-encoding direction ( 0 . 0391 ) , followed by the contribution sized for fMRI manufacturer ( 0 . 0318 ) , coil type ( 0 . 0239 ) , and scanner model ( 0 . 0152 ) . These findings indicate that the main factor influencing measurement bias is the difference in the phase-encoding direction , followed by fMRI manufacturer , coil type , and scanner model , respectively . We investigated two alternative models for the mechanisms underlying sampling bias . In the single-population model , which assumes that participants are sampled from a common population ( Fig 5A ) , the functional connectivity values of each participant were generated from a Gaussian distribution , with a mean of 0 and variance of ξ2 for each connectivity value . Then , the functional connectivity vector for participant j at site k can be described as cjk∼N ( 0 , ξ2I ) . In the different-subpopulation model , which assumes that sampling bias occurs partly because participants are sampled from different subpopulations at each site ( Fig 5B ) , we assumed that the functional connectivity values of each participant were generated from a different independent Gaussian distribution , with an average of βk and a variance of ξ2 depending on the population of each site . In this situation , the functional connectivity vector for participant j at site k can be described as cjk∼N ( βk , ξ2I ) . Here , we assume that the average of the population βk is sampled from an independent Gaussian distribution with an average of 0 and a variance of σ2 . It is necessary to determine which model is more suitable for collecting big data across multiple sites: If the former model is correct , then the data can be used to represent a population by increasing the number of participants , even if the number of sites is small . If the latter model is correct , data should be collected from many sites , as a single site does not represent the true grand population distribution , even with a very large sample size . For each model , we first investigated how the number of participants at each site determined the effect of sampling bias on functional connectivity . We measured the magnitude of the effect , based on the variance values for sampling bias across functional connectivity ( see the “Quantification of site differences” section ) . We used variance instead of the standard deviation to simplify the statistical analysis , although there is essentially no difference based on which value is used . We theorized that each model represents a different relationship between the number of participants and the variance of sampling bias . Therefore , we investigated which model best represents the actual relationships in our data by comparing the corrected Akaike information criterion ( AICc ) [36 , 37] and Bayesian information criterion ( BIC ) . Moreover , we performed leave-one-site-out cross-validation evaluations of predictive performance in which all but one site was used to construct the model , and the variance of the sampling bias was predicted for the remaining site . We then compared the predictive performances between the two models . Our results indicated that the different-subpopulation model provided a better fit for our data than the single-population model ( Fig 5C; different-subpopulation model: AICc = –108 . 80 and BIC = –113 . 22; single-population model: AICc = –96 . 71 and BIC = –97 . 92 ) . Furthermore , the predictive performance was significantly higher for the different-subpopulation model than for the single-population model ( one-tailed Wilcoxon signed rank test applied to absolute errors: Z = 1 . 67 , p = . 0469 , n = 6; Fig 5D and 5E ) . This result indicates that sampling bias is caused not only by random sampling from a single grand population , depending on the number of participants among sites , but also by sampling from among different subpopulations . Sampling biases thus represent a major limitation in attempting to estimate a true single distribution of HC or patient data based on measurements obtained from a finite number of sites and participants . Since our results indicated that the patterns of the measurement bias on functional connectivity were not sufficiently different from the patterns of disorder factors , we need harmonization to properly subtract the measurement bias . Therefore , we next developed a novel harmonization method that enabled us to subtract measurement bias alone using the traveling-subject dataset . Using a constrained linear regression model , we estimated measurement bias separately from sampling bias ( see “Bias estimation” in the Methods section ) . Thus , we could remove the measurement bias from the SRPBS multidisorder dataset ( i . e . , traveling-subject method , see “Traveling-subject harmonization” in the Methods section ) . To visualize the site differences and disorder effects in the SRPBS multidisorder dataset while maintaining its quantitative properties , we first performed an unsupervised dimension reduction of the raw rs-fcMRI data using a principal component analysis ( PCA ) . All participant data in the SRPBS multidisorder dataset were plotted on two axes consisting of the first two principal components ( PCs ) ( Fig 6A , small , light-colored symbols ) . The first two PCs could explain approximately 6% of the variance in the whole data ( Fig 6B , 3 . 5% and 2 . 5% for the first and second PC , respectively ) . Dark-colored markers indicated the averages of projected data across HCs in each site and the average within each psychiatric disorder in the subspace spanned by the two components . For the raw data , there was a clear separation of the Hiroshima University Hospital ( HUH ) site for PC1 , which explained most of the variance in the data . To visualize the effects of the harmonization process , we plotted the data after subtracting only the measurement bias from the SRPBS multidisorder dataset ( Fig 6C ) . In Fig 6C , the differences among sites represent the sampling bias . Relative to the result of raw data , which reflects the data before harmonization , the HUH site moved much closer to the origin ( i . e . , grand average ) and showed no marked separation from the other sites . This result indicated that the separation of the HUH site observed in Fig 6A was caused by measurement bias , which was removed by the harmonization . Furthermore , harmonization was effective in distinguishing patients and HCs scanned at the same site . Since patients with ASD were only scanned at the SWA site , the averages for these patients ( ▲ ) and HCs ( blue ● ) scanned at this site were projected to nearly identical positions ( Fig 6A ) . However , the two symbols are clearly separated from one another in Fig 6C . The effect of a psychiatric disorder ( ASD ) could not be observed in the first two PCs without harmonization but became detectable following the removal of measurement bias . Finally , to visualize the measurement bias in the SRPBS multidisorder dataset , we plotted the data after subtracting only the sampling bias from the SRPBS multidisorder dataset ( Fig 6D ) . Relative to the harmonized data results , the HUH site showed marked separation from the other sites , which was similar to the raw data ( Fig 6A ) . To correct difference among sites , there are three commonly used harmonization methods: ( 1 ) a ComBat method [16 , 17 , 19 , 38] , a batch-effect correction tool commonly used in genomics , site difference was modeled and removed; ( 2 ) a generalized linear model ( GLM ) method , site difference was estimated without adjusting for biological covariates ( diagnosis ) [16 , 18 , 22]; and ( 3 ) an adjusted GLM method , site difference was estimated while adjusting for biological covariates [16 , 18] ( see “Harmonization procedures” in the Methods section ) . However , all these methods estimate site difference without separating it into measurement and sampling biases and subtracting the site difference from the data . Therefore , existing harmonization methods might have pitfalls that eliminate both biologically meaningless measurement bias and biologically meaningful sampling bias . Here , we tested whether the traveling-subject harmonization method indeed removes only the measurement bias and whether the existing harmonization methods simultaneously remove the measurement and sampling biases . Specifically , we performed 2-fold cross-validation evaluations in which the SRPBS multidisorder dataset was partitioned into two equal-size subsamples ( fold1 data and fold2 data ) with the same proportions of sites . Between these two subsamples , the measurement bias is common , but the sampling bias is different , because the scanners are common and participants are different . We estimated the measurement bias ( or site difference including the measurement bias and the sampling bias for the existing methods ) by applying the harmonization methods to the fold1 data and subtracting the measurement bias or site difference from the fold2 data . Next , we estimated the measurement bias in the fold2 data . For the existing harmonization methods , if the site difference estimated using fold1 contained only the measurement bias , the measurement bias estimated in fold2 data after subtracting the site difference should be smaller than without subtracting the site difference ( Raw ) . To separately estimate measurement bias and sampling bias in both subsamples while avoiding information leaks , we also divided the traveling-subject dataset into two equal-size subsamples with the same proportions of sites and subjects . We concatenated one subsample of traveling-subject dataset to fold1 data to estimate the measurement bias for traveling-subject method ( estimating dataset ) and concatenated the other subsample of traveling-subject dataset to fold2 data for testing ( testing dataset ) . That is , in the traveling-subject harmonization method , we estimated the measurement bias using the estimating dataset and removed the measurement bias from the testing dataset . By contrast , in the other harmonization methods , we estimated the site difference using the fold1 data ( not including the subsample of traveling-subject dataset ) and removed the site difference from the testing dataset . We then estimated the measurement bias using the testing dataset and evaluated the standard deviation of the magnitude distribution of measurement bias calculated in the same way as described in the “Quantification of site differences” section . To verify whether important information , such as participant and disorder factors , remained in the testing dataset , we also estimated these factors and calculated the ratio of the standard deviation of the magnitude distribution of the measurement bias to each participant and disorder factor as signal-to-noise ratios . This procedure was performed again by exchanging the estimating dataset and the testing dataset ( see “Twofold cross-validation evaluation procedure” in the Methods section ) . Fig 7 shows the standard deviation of the magnitude distribution of the measurement bias and the ratio of the standard deviation of the magnitude distribution of the measurement bias to that of participant factor and disorder factor in both fold data for the four harmonization methods and without harmonization ( Raw ) . Our results show the highest reduction of the standard deviation of the magnitude distribution of the measurement bias from the Raw in the traveling-subject method when compared with all other methods ( 29% versus 3% in the ComBat method ) . Moreover , improvements in the signal-to-noise ratios were also highest in our method for the participant factor ( 41% versus 3% in the ComBat method ) and disorder factor ( 39% versus 3% in the ComBat method ) . These results indicated that the traveling-subject harmonization method removed measurement bias and improved the signal-to-noise ratios .
In the present study , by acquiring a separate traveling-subject dataset and the SRPBS multidisorder dataset , we separately estimated measurement and sampling biases for multiple sites , which we then compared with the magnitude of disorder factors . Furthermore , we investigated the origin of each bias in multisite datasets . Finally , to overcome the problem of site differences , we developed a novel harmonization method that enabled us to subtract the measurement bias by using a traveling-subject dataset and achieved the reduction of the measurement bias by 29% and improvement of signal-to-noise ratios by 40% . Previous studies have focused on measurement bias and compared its magnitude to the participant factor by using a traveling-subject design in a finger-tapping task fMRI [39] and rs-fMRI [20] . These studies revealed the magnitude of measurement bias is smaller than the participant factor . Although such a result was also obtained in this study , the novelty of this study exists in that we separately estimated measurement and sampling biases and then compared them with the magnitude of disorder factors . Our findings indicated that measurement bias exerted significantly greater effects than disorder factors , whereas sampling bias was comparable with ( or even larger than ) the disorder effects ( Fig 2 ) . Although our effect-size analysis was univariate , it is important to take perspective of multivariate pattern analysis into account because biomarker construction is based on the multivariate pattern of functional connectivity . From this view point , if the effect of the measurement bias is orthogonal to that of the psychiatric disorders , robust generalization across sites might be possible . Actually , previous research suggested this [11] . However , the orthogonality between the pattern of the disease factors and that of the measurement bias depends on dataset and type of disease . Since our results indicated that the pattern of the measurement bias was not sufficiently different from the patterns of disorder factors , harmonization was important to properly subtract the measurement bias . This result is a very important finding for future studies that collect rs-fMRI data from multiple sites and for consideration when constructing biomarkers of psychiatric disorders based on multisite data in the clinical field . Our results indicate that it is important to consider site differences when we investigate disorder factors using multisite rs-fMRI data . However , we did not control for variations in disease stage and treatment in our dataset . Although controlling for such heterogeneity may increase the effect size of disorder factors , such control is not feasible when collecting big data from multiple sites . Therefore , it is important to appropriately remove measurement bias from heterogeneous patient data to identify relatively small disorder effects . This issue is essential for investigating the relationships among different psychiatric disorders because disease factors could be confounded by site differences . As previously mentioned , it is common for a single site to sample only a few types of psychiatric disorders ( SCZ and ASD from sites A and B , respectively ) . This issue is also essential for constructing biomarkers of psychiatric disorders because classification of subjects can be achieved by using the information of nonbiological site difference . In this situation , it is critical to dissociate disease factors from site differences . This dissociation can be accomplished by subtracting only the measurement bias , which is estimated using the traveling-subject dataset . Our results indicated that measurement bias is primarily influenced by differences in the phase-encoding direction , followed by differences in fMRI manufacturer , coil type , and scanner model ( Fig 4 ) . These results are consistent with our finding of large measurement biases in the inferior brain regions ( Fig 3 ) , the functional imaging of which is known to be influenced by the phase-encoding direction [28 , 29] . Previous studies have reported that the effect caused by the difference in the phase-encoding direction can be corrected using the field map obtained at the time of imaging [28 , 40–42] . The field map was acquired in parts of the traveling-subject dataset; therefore , we investigated the effectiveness of field map correction by comparing the effect size of the measurement bias and the participant factor between functional images with and without field map correction ( see S2 Text ) . Our prediction was as follows: if field map correction is effective , the effect of measurement bias will decrease , whereas that of the participant factor will increase following field map correction . Field map correction using SPM12 ( http://www . fil . ion . ucl . ac . uk/spm/software/spm12 ) reduced the effect of measurement bias in the inferior brain regions ( whole brain: 3% reduction in the standard deviation of the magnitude distribution of the measurement bias ) and increased the effect of the participant factor in the whole brain ( 3% increase in the standard deviation of the magnitude distribution of the participant factor; S2A and S2B Fig ) . However , the effect of measurement bias remained large in inferior brain regions ( S2A Fig ) , and hierarchical clustering analysis revealed that the clusters of the phase-encoding direction remained dominant ( S2C Fig ) . These results indicate that , even with field map correction , it is largely impossible to remove the influence of differences in phase-encoding direction on functional connectivity . Thus , harmonization methods are still necessary to remove the effect of these differences and other measurement-related factors . However , some distortion correction methods have been developed , such as the top-up method and symmetric normalization [43 , 44] , and further studies are required to verify the efficacy of these methods . Our data supported the different-subpopulation model rather than the single-population model ( Fig 5 ) , which indicates that sampling bias is caused by sampling from among different subpopulations . Furthermore , these findings suggest that , during big data collection , it is better to sample participants from several imaging sites than to sample many participants from a few imaging sites . These results were obtained only by combining the SRPBS multidisorder database with a traveling-subject dataset ( https://bicr . atr . jp/decnefpro/ ) . To the best of our knowledge , the present study is the first to demonstrate the presence of sampling bias in rs-fcMRI data , the mechanisms underlying this sampling bias , and the effect size of sampling bias on resting-state functional connectivity , which was comparable to that of psychiatric disorders . We analyzed sampling bias among HCs only , because the number of sites was too small to conduct an analysis of patients with psychiatric diseases . We developed a novel harmonization method using a traveling-subject dataset ( i . e . , traveling-subject method ) , which was then compared with existing harmonization methods . Our results demonstrated that the traveling-subject method outperformed other conventional GLM-based harmonization methods and the ComBat method . The traveling-subject method achieved reduction in the measurement bias by 29% , compared with 3% in the second highest value for the ComBat method , and improvement in the signal-to-noise ratios by 40% , compared with 3% in the second highest value for the ComBat method . This result indicates that the traveling-subject dataset helps us to properly estimate measurement bias and harmonize the rs-fMRI data across imaging sites toward development of a wide range of final applications . As one example of such applications , we constructed biomarkers for psychiatric disorders based on rs-fcMRI data , which distinguishes between HCs and patients , and a regression model to predict participants’ age based on rs-fcMRI data using the SRPBS multidisorder dataset ( see “Classifiers for MDD and SCZ , based on the four harmonization methods” in S5 Text and “Regression models of participant age based on the four harmonization methods” in S6 Text ) . We quantitatively evaluated the harmonization method to investigate the generalization performance to independent validation dataset , which was not included in the SRPBS multidisorder dataset . Although the ComBat method achieved the highest performance for the MDD classifier and regression model of age , it was inferior to the raw method for the SCZ classifier . By contrast , the traveling-subject harmonization method always improved the generalization performance compared with when no harmonization was performed . This result also indicates that the pattern of the measurement bias on functional connectivity was not sufficiently different from the patterns of disorder factors in our dataset . These results indicate that the traveling-subject dataset also helps with constructing a prediction model based on multisite rs-fMRI data . Future work should improve the traveling-subject method by incorporating a hierarchical model , such as ComBat . The present study possesses some limitations of note . The accuracy of measurement bias estimation may be improved by further expanding the traveling-subject dataset . This can be achieved by increasing the number of traveling participants or sessions per site . However , as mentioned in a previous traveling-subject study [20] , it is costly and time-consuming to ensure that numerous participants travel to every site involved in big database projects . Thus , the cost-performance tradeoff must be evaluated in practical settings . The numbers of traveling participants and MRI sites used in this study ( 9 and 12 , respectively ) were larger than those used in a previous study ( 8 and 8 , respectively ) [20] , and the number of total sessions in this study ( 411 ) was more than three times larger than that used in the previous study ( 128 ) [20] . Furthermore , although we estimated the measurement bias for each connectivity , hierarchical models of the brain ( e . g . , ComBat ) may be more appropriate for improving the estimates of measurement bias . Regarding the number of sites in the data with psychiatric disorders , we believe that uniqueness of our study exists in the datasets of multiple disorders and multiple sites with traveling-subject data rather than the number of sites for a single disorder . For example , although ABIDE [6 , 11] collected the data from patients with ASD from 17 sites , it significantly differs from our study because it does not use a unified protocol for data collection and does not include a traveling-subject dataset . In this study , we have collected the data using a unified protocol with HCs from six sites , patients with MDD from three sites , patients with ASD from one site , patients with SCZ from three sites , patients with OCD from one site , and a traveling-subject dataset from 12 sites . These datasets enabled us to compare the magnitude of the effect between site differences ( measurement or sampling bias ) and multiple disorder factors , which is the key point of our study . To the best of our knowledge , such a multisite , multidisorder rs-fMRI dataset has not existed so far . In summary , by acquiring a separate traveling-subject dataset and the SRPBS multidisorder database , we revealed that site differences were composed of biological sampling bias and engineering measurement bias . The effect sizes of these biases on functional connectivity were greater than or equal to the effect sizes of psychiatric disorders , and the pattern of the measurement bias was not sufficiently different from the patterns of disorder factors , highlighting the importance of controlling for site differences when investigating psychiatric disorders . Furthermore , using the traveling-subject dataset , we developed a novel traveling-subject method that harmonizes the measurement bias only by separating sampling bias from site differences . Our findings verified that the traveling-subject method outperformed conventional GLM-based harmonization methods and ComBat method . These results suggest that a traveling-subject dataset can help to harmonize the rs-fMRI data across imaging sites .
All participants in all datasets provided written informed consent . All recruitment procedures and experimental protocols were approved by the institutional review boards of the principal investigators’ respective institutions ( Advanced Telecommunications Research Institute International [ATR] [approval numbers: 13–133 , 14–133 , 15–133 , 16–133 , 17–133 , and 18–133] , Hiroshima University [E-38] , Kyoto Prefectural University of Medicine [KPM] [RBMR-C-1098] , SWA [B-2014-019 and UMIN000016134] , the University of Tokyo [UTO] Faculty of Medicine [3150] , Kyoto University [C809 and R0027] , and Yamaguchi University [H23-153 and H25-85] ) and conducted in accordance with the Declaration of Helsinki . We used two rs-fMRI datasets for all analyses: ( 1 ) the SRPBS multidisorder dataset , which encompasses multiple psychiatric disorders , and ( 2 ) a traveling-subject dataset . The SRPBS multidisorder dataset contains data for 805 participants ( 482 HCs from nine sites , 161 patients with MDD from five sites , 49 patients with ASD from one site , 65 patients with OCD from one site , and 48 patients with SCZ from three sites ) ( Table 1 ) . Data were acquired using a Siemens TimTrio scanner at ATR ( ATT ) , a Siemens Verio scanner at ATR ( ATV ) , a Siemens Verio at the Center of Innovation in Hiroshima University ( COI ) , a GE SignaHDxt scanner at HUH , a Siemens Spectra scanner at Hiroshima Kajikawa Hospital ( HKH ) , a Philips Achieva scanner at KPM , a Siemens Verio scanner at SWA , a Siemens TimTrio scanner at Kyoto University ( KUT ) , and a GE MR750W scanner at the UTO . Each participant underwent a single rs-fMRI session for 5–10 min . The rs-fMRI data were acquired using a unified imaging protocol at all but three sites ( Table 2; https://bicr . atr . jp/rs-fmri-protocol-2/ ) . During the rs-fMRI scans , participants were instructed as follows , except at one site: “Please relax . Don’t sleep . Fixate on the central crosshair mark and do not think about specific things . ” At the remaining site , participants were instructed to close their eyes rather than fixate on a central crosshair . In the traveling-subject dataset , nine healthy participants ( all male participants; age range 24–32 y; mean age 27 ± 2 . 6 y ) were scanned at each of 12 sites in the SRPBS consortium , producing a total of 411 scan sessions . Data were acquired at the sites included in the SRPBS multidisorder database ( i . e . , ATT , ATV , COI , HUH , HKH , KPM , SWA , KUT , and UTO ) and three additional sites: Kyoto University ( KUS; Siemens Skyra ) and Yaesu Clinic 1 and 2 ( YC1 and YC2; Philips Achieva ) ( S1 Table ) . Each participant underwent three rs-fMRI sessions of 10 min each at nine sites , two sessions of 10 min each at two sites ( HKH and HUH ) , and five cycles ( morning , afternoon , next day , next week , and next month ) consisting of three 10-min sessions each at a single site ( ATT ) . In the latter situation , one participant underwent four rather than five sessions at the ATT site because of a poor physical condition . Thus , a total of 411 sessions were conducted [8 participants × ( 3 × 9 + 2 × 2 + 5 × 3 × 1 ) + 1 participant × ( 3 × 9 + 2 × 2 + 4 × 3 × 1 ) ] . During each rs-fMRI session , participants were instructed to maintain a focus on a fixation point at the center of a screen , remain still and awake , and to think about nothing in particular . For sites that could not use a screen in conjunction with fMRI ( HKH and KUS ) , a seal indicating the fixation point was placed on the inside wall of the MRI gantry . Although we attempted to ensure imaging was performed using the same variables at all sites , there were two phase-encoding directions ( P→A and A→P ) , three MRI manufacturers ( Siemens , GE , and Philips ) , four different numbers of channels per coil ( 8 , 12 , 24 , and 32 ) , and seven scanner types ( TimTrio , Verio , Skyra , Spectra , MR750W , SignaHDxt , and Achieva ) ( S1 Table ) . The rs-fMRI data were preprocessed using SPM8 implemented in MATLAB ( R2016b; Mathworks , Natick , MA , USA ) . The first 10 s of data was discarded to allow for T1 equilibration . Preprocessing steps included slice-timing correction , realignment , coregistration , segmentation of T1-weighted structural images , normalization to Montreal Neurological Institute ( MNI ) space , and spatial smoothing with an isotropic Gaussian kernel of 6 mm full-width at half-maximum . For the analysis of connectivity matrices , ROIs were delineated according to a 268-node gray matter atlas developed to cluster maximally similar voxels [26] . The BOLD signal time courses were extracted from these 268 ROIs . To remove several sources of spurious variance , we used linear regression with 36 regression parameters [45] such as six motion parameters , average signals over the whole brain , white matter , and cerebrospinal fluid . Derivatives and quadratic terms were also included for all parameters . A temporal band-pass filter was applied to the time series using a first-order Butterworth filter with a pass band between 0 . 01 Hz and 0 . 08 Hz to restrict the analysis to low-frequency fluctuations , which are characteristic of rs-fMRI BOLD activity [45] . Furthermore , to reduce spurious changes in functional connectivity because of head motion , we calculated framewise displacement ( FD ) and removed volumes with FD > 0 . 5 mm , as proposed in a previous study [46] . The FD represents head motion between two consecutive volumes as a scalar quantity ( the summation of absolute displacements in translation and rotation ) . Using the aforementioned threshold , 5 . 4% ± 10 . 6% volumes ( mean [approximately 13 volumes] ± 1 SD ) were removed per 10 min of rs-fMRI scanning ( 240 volumes ) in the traveling-subject dataset; 6 . 2% ± 13 . 2 volumes were removed per rs-fMRI session in the SRPBS multidisorder dataset . If the number of volumes removed after scrubbing exceeded the average of –3 SD across participants in each dataset , the participants or sessions were excluded from the analysis . As a result , 14 sessions were removed from the traveling-subject dataset , and 20 participants were removed from the SRPBS multidisorder dataset . Furthermore , we excluded participants for whom we could not calculate functional connectivity at all 35 , 778 connections , primarily because of the lack of BOLD signals within an ROI . As a result , 99 participants were further removed from the SRPBS multidisorder dataset . The participant factor ( p ) , measurement bias ( m ) , sampling biases ( shc , smdd , sscz ) , and psychiatric disorder factor ( d ) were estimated by fitting the regression model to the functional connectivity values of all participants from the SRPBS multidisorder dataset and the traveling-subject dataset . In this instance , vectors are denoted by lowercase bold letters ( e . g . , m ) , and all vectors are assumed to be column vectors . Components of vectors are denoted by subscripts such as mk . To represent participant characteristics , we used a 1-of-K binary coding scheme in which the target vector ( e . g . , xm ) for a measurement bias m belonging to site k is a binary vector with all elements equal to zero—except for element k , which equals 1 . If a participant does not belong to any class , the target vector is a vector with all elements equal to zero . A superscript T denotes the transposition of a matrix or vector , such that xT represents a row vector . For each connectivity , the regression model can be written as follows: Connectivity=xmTm+xshcTshc+xsmddTsmdd+xssczTsscz+xdTd+xpTp+const+e , suchthat∑j9pj=0 , ∑k12mk=0 , ∑k6shck=0 , ∑k3smddk=0 , ∑k3ssczk=0 , d1 ( HC ) =0 , in which m represents the measurement bias ( 12 sites × 1 ) , shc represents the sampling bias of HCs ( 6 sites × 1 ) , smdd represents the sampling bias of patients with MDD ( 3 sites × 1 ) , sscz represents the sampling bias of patients with SCZ ( 3 sites × 1 ) , d represents the disorder factor ( 3 × 1 ) , p represents the participant factor ( 9 traveling subjects × 1 ) , const represents the average functional connectivity value across all participants from all sites , and e∼N ( 0 , γ−1 ) represents noise . For each functional connectivity value , we estimated the respective parameters using regular ordinary least squares regression with L2 regularization , as the design matrix of the regression model is rank-deficient ( see S3 Text ) . We used the “quadprog” function in MATLAB ( R2016b ) for estimation . When regularization was not applied , we observed spurious anticorrelation between the measurement bias and the sampling bias for HCs and spurious correlation between the sampling bias for HCs and the sampling bias for patients with psychiatric disorders ( S3A Fig , left ) . These spurious correlations were also observed in the permutation data in which there were no associations between the site label and data ( S3A Fig , right ) . This finding suggests that the spurious correlations were caused by the rank-deficient property of the design matrix . We tuned the hyperparameter lambda to minimize the absolute mean of these spurious correlations ( S3C Fig , left ) . To quantitatively verify the magnitude relationship among factors , we calculated and compared the contribution size to determine the extent to which each bias type and factor explains the variance of the data in our linear model ( Fig 2C ) . After fitting the model , the b-th connectivity from subject a can be written as follows: Connectivitya , b=xmaTmb+xshcaTshcb+xsmddaTsmddb+xssczaTssczb+xdaTdb+xpaTpb+const+e . For example , the contribution size of measurement bias ( i . e . , the first term ) in this model was calculated as Contributionsizem=1Nm1Ns*N∑a=1Ns∑b=1N ( xmaTmb ) 2 ( xmaTmb ) 2+ ( xshcaTshcb ) 2+ ( xsmddaTsmddb ) 2+ ( xssczaTssczb ) 2+ ( xdaTdb ) 2+ ( xpaTpb ) 2+e2 , in which Nm represents the number of components for each factor , N represents the number of connectivities , Ns represents the number of subjects , and Contribution sizem represents the magnitude of the contribution size of measurement bias . These formulas were used to assess the contribution sizes of individual factors related to measurement bias ( e . g . , phase-encoding direction , scanner , coil , and fMRI manufacturer: Fig 4B ) . We decomposed the measurement bias into these factors , after which the relevant parameters were estimated . Other parameters were fixed at the same values as previously estimated . To evaluate the spatial characteristics of each type of bias and each factor in the brain , we calculated the magnitude of the effect on each ROI . First , we calculated the median absolute value of the effect on each functional connection among sites or participants for each bias and participant factor . We then calculated the absolute value of each connection for each disorder factor . The uppercase bold letters ( e . g . , M ) and subscript vectors ( e . g . , mk ) represent the vectors for the number of functional connections: M=mediank ( |mk| ) , Shc=mediank ( |shck| ) , Smdd=mediank ( |smddk| ) , Sscz=mediank ( |ssczk| ) , D2=|d2| , D3=|d3| , P=medianj ( |pj| ) . We next calculated the magnitude of the effect on ROIs as the average connectivity value between all ROIs , except for themselves . Effect_on_ROIn=1NROI−1∑v≠nNROIEffect_on_connectivityn , v , in which NROI represents the number of ROIs , Effect_on_ROIn represents the magnitude of the effect on the n-th ROI , and Effect_on_connectivityn , v represents the magnitude of the effect on connectivity between the n-th ROI and v-th ROI . We calculated the Pearson’s correlation coefficients between measurement biases mk ( N × 1 ) , where N is the number of functional connections ) for each site k , and performed a hierarchical clustering analysis based on the correlation coefficients across measurement biases . To visualize the dendrogram ( Fig 4A ) , we used the “dendrogram , ” “linkage , ” and “optimalleaforder” functions in MATLAB ( R2016b ) . We investigated whether sampling bias is caused by the differences in the number of participants among imaging sites or by sampling from different populations among imaging sites . We constructed two models and investigated which model provides the best explanation of sampling bias . In the single-population model , we assumed that participants were sampled from a single population across imaging sites . In the different-population model , we assumed that participants were sampled from different populations among imaging sites . We first theorized how the number of participants at each site affects the variance of sampling biases across connectivity values as follows: In the single-population model , we assumed that the functional connectivity values of each participant were generated from an independent Gaussian distribution , with a mean of 0 and a variance of ξ2 for each connectivity value . Then , the functional connectivity vector for participant j at site k can be described as cjk∼N ( 0 , ξ2I ) . Let ck be the vector of functional connectivity at site k averaged across participants . In this model , ck represents the sampling bias and can be described as ck=1Nk∑j=1Nkcjk∼N ( 0 , ξ2NkI ) , in which Nk represents the number of participants at site k . The variance across functional connectivity values for ck is described as Vk=1N∑i=1N ( cki−ck¯ ) 2=1NckT ( I−1N11′ ) T ( I−1N11′ ) ck≈1NckTck , in which 1 represents the N × 1 vector of ones and I represents the N × N identity matrix . Since N equals 35 , 778 and 135 , 778 is sufficiently smaller than 1 , we can approximate I−1N11′≈I . Then , the expected value of variance across functional connectivity values for sampling bias can be described as E[Vk]≈1NE[ckTck]=1NTr ( ξ2NkI ) =ξ2Nk . In the different-population model , we assumed that the functional connectivity values of each participant were generated from a different independent Gaussian distribution , with an average of βk and a variance of ξ2 depending on the population of each site . In this situation , the functional connectivity vector for participant j at site k can be described as cjk∼N ( βk , ξ2I ) . Here , we assume that the average of the population βk is sampled from an independent Gaussian distribution with an average of 0 and a variance of σ2 . That is , βk is expressed as βk∼N ( 0 , σ2I ) . The vector of functional connectivity for site k averaged across participants can then be described as ck∼N ( 0 , ( ξ2Nk+σ2 ) I ) . The variance across functional connectivity values for ck can be described as E[Vk]≈ξ2Nk+σ2 . In summary , the variance of sampling bias across functional connectivity values in each model is expressed by the number of participants at a given site as follows: single‐populationmodel:yk=−xk+2log10ξ different‐populationmodel:yk=−log10 ( ξ210−xk+σ2 ) , in which yk = log10 ( vk ) , vk represents the variance across functional connectivity values for shck , shck represents the sampling bias of HCs at site k ( N × 1: N is the number of functional connectivity ) , xk = log10 ( Nk ) , and Nk represents the number of participants at site k . We estimated the parameters ξ and σ using the MATLAB optimization function “fminunc . ” To simplify statistical analyses , sampling bias was estimated based on functional connectivity in which the average across all participants was set to zero . We aimed to determine which model provided the best explanation of sampling bias in our data by calculating the AICc ( under the assumption of a Gaussian distribution ) for small-sample data [36 , 37] , as well as BIC: AICc=∑k=16lnφk2+2q+2q ( q+1 ) ( 6−q−1 ) , BIC=∑k=16lnφk2+q*log ( 6 ) , in which φk=vk−vk^ , vk^ represents the estimated variance , and q represents the number of parameters in each model ( 1 or 2 ) . To investigate prediction performance , we used leave-one-site-out cross-validation in which we estimated the parameters ξ and σ using data from five sites . The variance of sampling bias was predicted based on the number of participants at the remaining site . This procedure was repeated to predict variance values for sampling bias at all six sites . We then calculated the absolute errors between predicted and actual variances for all sites . We compared four different harmonization methods for the removal of site differences , as described in the main text . We developed bivariate scatterplots of the first two PCs based on a PCA of functional connectivity values in the SRPBS multidisorder dataset ( Fig 6A ) . All participant data in the SRPBS multidisorder dataset were plotted on two axes consisting of the first two PCs ( Fig 6A , small , light-colored symbols ) . The first two PCs could explain approximately 6% of the variance in the whole data ( Fig 6B , 3 . 5% and 2 . 5% for the first and second PC , respectively ) . Dark-colored markers indicated the averages of the projected data across HCs at each site and the average within each psychiatric disorder in the subspace spanned by the two components . To visualize whether most of the variation in the SRPBS multidisorder dataset was still associated with imaging site after harmonization , we performed a PCA of functional connectivity values in the harmonized SRPBS multidisorder dataset ( Fig 6C ) . We used the traveling-subject method for harmonization , as described in the following section . Finally , to visualize the measurement bias in the SRPBS multidisorder dataset , we performed a PCA of functional connectivity values in the SRPBS multidisorder data after subtracting only the sampling bias ( Fig 6D ) . We compared four different harmonization methods for the removal of site difference or measurement bias by 2-fold cross-validation , as described in the main text . In the traveling-subject harmonization method , we estimated the measurement bias by applying the regression model written in Eq 1 in the “Harmonization procedures” section to the estimating dataset . Thus , the harmonized functional connectivity values in testing dataset were set as follows: connectivitytestingdatasetTraveling−subject=Connectivitytestingdataset−xmTm^estimatingdataset , in which m^estimatingdataset represents the estimated measurement bias using the estimating dataset . By contrast , in the other harmonization methods , we estimated the site differences by applying the regression models written in Eqs 2–4 in the “Harmonization procedures” section to the estimating dataset ( fold1 data ) . Thus , the harmonized functional connectivity values in testing dataset were set as follows: connectivitytestingdatasetGLM=Connectivitytestingdataset−xsTsGLM^fold1 , connectivitytestingdatasetAdj=Connectivitytestingdataset−xsTsAdj^fold1 , connectivitytestingdatasetComBat=Connectivitytestingdataset−xsTsComBat^fold1 , in which sGLM^fold1 , sAdj^fold1 , sComBat^fold1 represents the estimated site differences using fold1 data . We then estimated the measurement bias , participant factor , and disorder factors by applying the regression model written in Eq 1 to the harmonized functional connectivity values in the testing dataset . Finally , we evaluated the standard deviation of the magnitude distribution of measurement bias calculated in the same way as described in the “Quantification of site differences” section among the harmonization methods . This procedure was done again by exchanging the estimating dataset and the testing dataset .
|
Recently , the importance of acquiring and sharing large amounts of resting-state functional magnetic resonance imaging ( rs-fMRI ) data from multiple geographical locations or sites has increased . However , differences in the data acquired from multiple sites create heterogeneities that present a barrier to the analysis . To properly manage these heterogeneous multisite data , it is important to have a deeper understanding of the origin of these between-site differences and to harmonize rs-fMRI data among sites . In this study , we demonstrate that site differences are composed of biological sampling bias ( differences between the participant groups ) and engineering measurement bias ( differences in the properties of the MRI scanners used ) . We found that the effects of both types of bias on rs-fMRI functional connectivity were greater than or equal to those driven by psychiatric disorders . Furthermore , our results identified the specific properties of MRI scanners that affect the rs-fMRI connectivity . To overcome the limitations associated with site differences , we used a traveling-subject dataset , wherein multiple participants travel to multiple sites to assess measurement bias by controlling for participant effects between sites . Our results indicated that the traveling-subject dataset can help the proper harmonization of rs-fMRI data between sites .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"diagnostic",
"radiology",
"functional",
"magnetic",
"resonance",
"imaging",
"data",
"acquisition",
"engineering",
"and",
"technology",
"pervasive",
"developmental",
"disorders",
"social",
"sciences",
"neuroscience",
"developmental",
"psychology",
"telecommunications",
"magnetic",
"resonance",
"imaging",
"brain",
"mapping",
"autism",
"spectrum",
"disorder",
"neuroimaging",
"mood",
"disorders",
"research",
"and",
"analysis",
"methods",
"meta-research",
"article",
"computer",
"and",
"information",
"sciences",
"imaging",
"techniques",
"schizophrenia",
"mental",
"health",
"and",
"psychiatry",
"psychology",
"radiology",
"and",
"imaging",
"diagnostic",
"medicine",
"biology",
"and",
"life",
"sciences",
"depression"
] |
2019
|
Harmonization of resting-state functional MRI data across multiple imaging sites via the separation of site differences into sampling bias and measurement bias
|
A fundamental question in biology is the following: what is the time scale that is needed for evolutionary innovations ? There are many results that characterize single steps in terms of the fixation time of new mutants arising in populations of certain size and structure . But here we ask a different question , which is concerned with the much longer time scale of evolutionary trajectories: how long does it take for a population exploring a fitness landscape to find target sequences that encode new biological functions ? Our key variable is the length , of the genetic sequence that undergoes adaptation . In computer science there is a crucial distinction between problems that require algorithms which take polynomial or exponential time . The latter are considered to be intractable . Here we develop a theoretical approach that allows us to estimate the time of evolution as function of We show that adaptation on many fitness landscapes takes time that is exponential in even if there are broad selection gradients and many targets uniformly distributed in sequence space . These negative results lead us to search for specific mechanisms that allow evolution to work on polynomial time scales . We study a regeneration process and show that it enables evolution to work in polynomial time .
Our planet came into existence 4 . 6 billion years ago . There is clear chemical evidence for life on earth 3 . 5 billion years ago [1] , [2] . The evolutionary process generated procaria , eucaria and complex multi-cellular organisms . Throughout the history of life , evolution had to discover sequences of biological polymers that perform specific , complicated functions . The average length of bacterial genes is about 1000 nucleotides , that of human genes about 3000 nucleotides . The longest known bacterial gene contains more than nucleotides , the longest human gene more than . A basic question is what is the time scale required by evolution to discover the sequences that perform desired functions . While many results exist for the fixation time of individual mutants [3]–[15] , here we ask how the time scale of evolution depends on the length of the sequence that needs to be adapted . We consider the crucial distinction of polynomial versus exponential time [16]–[18] . A time scale that grows exponentially in is infeasible for long sequences . Evolutionary dynamics operates in sequence space , which can be imagined as a discrete multi-dimensional lattice that arises when all sequences of a given length are arranged such that nearest neighbors differ by one point mutation [19] . For constant selection , each point in sequence space is associated with a non-negative fitness value ( reproductive rate ) . The resulting fitness landscape is a high dimensional mountain range . Populations explore fitness landscapes searching for elevated regions , ridges , and peaks [20]–[27] . A question that has been extensively studied is how long does it take for existing biological functions to improve under natural selection . This problem leads to the study of adaptive walks on fitness landscapes [15] , [20] , [21] , [28] , [29] . In this paper we ask a different question: how long does it take for evolution to discover a new function ? More specifically , our aim is to estimate the expected discovery time of new biological functions: how long does it take for a population of reproducing organisms to discover a biological function that is not present at the beginning of the search . We will discuss two approximations for rugged fitness landscapes . We also discuss the significance of clustered peaks . We consider an alphabet of size four , as is the case for DNA and RNA , and a nucleotide sequence of length . We consider a population of size , which reproduces asexually . The mutation rate , , is small: individual mutations are introduced and evaluated by natural selection and random drift one at a time . The probability that the evolutionary process moves from a sequence to a sequence , which is at Hamming distance one from , is given by , where is the fixation probability of sequence in a population consisting of sequence . In the special case of a flat fitness landscape , we have , and . Thus we have an evolutionary random walk , where each step is a jump to a neighboring sequence of Hamming distance one .
The regeneration process formalizes the role of several existing ideas . First , it ties in with the proposal that gene duplications and genome rearrangements are major events leading to the emergence of new genes [43] . Second , evolution can be seen as a tinkerer playing around with small modifications of existing sequences rather than creating entirely new ones [44] . Third , the process is related to Gillespie's suggestion [29] that the starting sequence for an evolutionary search must have high fitness . In our theory , proximity in fitness value is replaced by proximity in sequence space . However , our results show that proximity alone is insufficient to break the exponential barrier , and only when combined with the process of regeneration it yields polynomial discovery time with high probability . Our process can also explain the emergence of orphan genes arising from non-coding regions [45] . Section 12 of the Text S1 discusses the connection of our approach to existing results . There is one other scenario that must be mentioned . It is possible that certain biological functions are hyper-abundant in sequence space [21] and that a process generating a large number of random sequences will find the function with high probability . For example , Bartel & Szostak [46] isolated a new ribozyme from a pool of about random sequences of length . While such a process is conceivable for small effective sequence length , it cannot represent a general solution for large . Our theory has clear empirical implications . The regeneration process can be tested in systems of in vitro evolution [47] . A starting sequence can be generated by introducing point mutations in a known protein encoding sequence of length . If these point mutations destroy the function of the protein , then the expected discovery time of any one attempt to find the original sequence should be exponential in . But only polynomially many searches in are required to find the target with high probability in polynomially many steps . The same setup can be used to explore whether the biological function can be found elsewhere in sequence space: the evolutionary trajectory beginning with the starting sequence could discover new solutions . Our theory also highlights how important it is to explore the distribution of biological functions in sequence space both for RNA [20] , [21] , [35] , [46] and in the protein universe [48] . In summary , we have developed a theory that allows us to estimate time scales of evolutionary trajectories . We have shown that various natural processes of evolution take exponential time as function of the sequence length , . In some cases we have established strong dichotomy results for precise boundary conditions . We have proposed a mechanism that allows evolution in polynomial time scales . Some interesting directions of future work are as follows: ( 1 ) Consider various forms of rugged fitness landscapes and study more refined approximations as compared to the ones we consider; and then estimate the expected discovery time for the refined approximations . ( 2 ) While in this paper we characterize the difference between exponential and polynomial for the expected discovery time , more refined analysis ( such as efficiency for polynomial time , like cubic vs quadratic time ) for specific fitness landscapes using mechanisms like recombination is another interesting problem .
Our results are based on a mathematical analysis of the underlying stochastic processes . For Markov chains on the one-dimensional grid , we describe recurrence relations for the expected hitting time and present lower and upper bounds on the expected hitting time using combinatorial analysis ( see Text S1 for details ) . We now present the basic intuitive arguments of the main results .
For a single broad peak , due to symmetry we can interpret the evolutionary random walk as a Markov chain on the one-dimensional grid . A sequence of type is steps away from the target , where is the Hamming distance between this sequence and the target . The probability that a type sequence mutates to a type sequence is given by . The stochastic process of the evolutionary random walk is a Markov chain on the one-dimensional grid . Consider a Markov chain on the one-dimensional grid , and let denote the expected hitting time from to . The general recurrence relation for the expected hitting time is as follows: ( 1 ) for , with boundary condition . The interpretation is as follows . Given the current state , if , at least one transition will be made to a neighboring state , with probability , from which the hitting time is . Theorem 1 is derived by obtaining precise bounds for the recurrence relation of the hitting time ( Equation 1 ) . Consider that for all ( i . e . , progress towards state is always possible ) , as otherwise is never reached from . We show ( see Lemma 2 in the Text S1 ) that we can write as a sum , , where is the sequence defined as: ( 2 ) The basic intuition obtained from Equation 2 is as follows: ( i ) If , for some constant , then the sequence grows at least as fast as a geometric series with factor . ( ii ) On the other hand , if and for some constant , then the sequence grows at most as fast as an arithmetic series with difference . From the above case analysis the result for Theorem 1 is obtained as follows: If , then for all , we have for some , and hence the sequence grows geometrically for a linear length in . Then , for all states ( i . e . , for all sequences outside of the target set ) . This corresponds to case 1 of Theorem 1 . On the other hand , if , then it is , and case 2 of Theorem 1 is derived ( for details see Corollary 2 in Text S1 ) . The basic intuition for the result is as follows: consider a single search for which the expected hitting time is exponential . Then for the single search the probability to succeed in polynomially many steps is negligible ( as otherwise the expectation would not have been exponential ) . In case of independent searches , the independence ensures that the probability that all searches fail is the product of the probabilities that every single search fails . Using the above arguments we establish Theorem 2 ( for details see Section 8 in Text S1 ) . For this result , it is first convenient to view the evolutionary walk taking place in the sequence space of all sequences of length , under no selection . Each sequence has neighbors , and considering that a point mutation happens , the transition probability to each of them is . The underlying Markov chain due to symmetry has fast mixing time , i . e . , the number of steps to converge to the stationary distribution ( the mixing time ) is . Again by symmetry the stationary distribution is the uniform distribution . If , then from Theorem 1 we obtain that the expected time to reach a single broad peak is exponential . By union bound , if , the probability to reach any of the broad peaks within steps is negligible . Since after the first steps the Markov chain converges to the stationary distribution , then each step of the process can be interpreted as selection of sequences uniformly at random among all sequences . Using Hoeffding's inequality , we show that with high probability , in expectation such steps are required before a sequence is found that belongs to the target set . Thus we obtain the result of Theorem 3 ( for details see Section 9 in Text S1 ) . An important aspect of our work is that we establish our results using elementary techniques for analysis of Markov chains . The use of more advanced mathematical machinery , such as martingales [49] or drift analysis [50] , [51] , can possibly be used to derive more refined results . While in this work our goal is to distinguish between exponential and polynomial time , whether the techniques from [49]–[51] can lead to a more refined characterization within polynomial time is an interesting direction for future work .
|
Evolutionary adaptation can be described as a biased , stochastic walk of a population of sequences in a high dimensional sequence space . The population explores a fitness landscape . The mutation-selection process biases the population towards regions of higher fitness . In this paper we estimate the time scale that is needed for evolutionary innovation . Our key parameter is the length of the genetic sequence that needs to be adapted . We show that a variety of evolutionary processes take exponential time in sequence length . We propose a specific process , which we call ‘regeneration processes’ , and show that it allows evolution to work on polynomial time scales . In this view , evolution can solve a problem efficiently if it has solved a similar problem already .
|
[
"Abstract",
"Introduction",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"mutation",
"natural",
"selection",
"genetics",
"biology",
"and",
"life",
"sciences",
"evolutionary",
"adaptation",
"evolutionary",
"biology",
"evolutionary",
"processes"
] |
2014
|
The Time Scale of Evolutionary Innovation
|
Positional identities along the anterior–posterior axis of the vertebrate nervous system are assigned during gastrulation by multiple posteriorizing signals , including retinoic acid ( RA ) , fibroblast growth factors ( Fgfs ) , and Wnts . Experimental evidence has suggested that RA , which is produced in paraxial mesoderm posterior to the hindbrain by aldehyde dehydrogenase 1a2 ( aldh1a2/raldh2 ) , forms a posterior-to-anterior gradient across the hindbrain field , and provides the positional information that specifies the locations and fates of rhombomeres . Recently , alternative models have been proposed in which RA plays only a permissive role , signaling wherever it is not degraded . Here we use a combination of experimental and modeling tools to address the role of RA in providing long-range positional cues in the zebrafish hindbrain . Using cell transplantation and implantation of RA-coated beads into RA-deficient zebrafish embryos , we demonstrate that RA can directly convey graded positional information over long distances . We also show that expression of Cyp26a1 , the major RA-degrading enzyme during gastrulation , is under complex feedback and feedforward control by RA and Fgf signaling . The predicted consequence of such control is that RA gradients will be both robust to fluctuations in RA synthesis and adaptive to changes in embryo length during gastrulation . Such control also provides an explanation for the fact that loss of an endogenous RA gradient can be compensated for by RA that is provided in a spatially uniform manner .
The anterior–posterior ( A–P ) axis of vertebrate embryos is patterned by multiple signals . In the gastrula-stage embryo , at least three types of diffusible , extracellular molecules—retinoic acid ( RA ) , fibroblast growth factors ( Fgfs ) , and Wnts—together promote posterior and suppress anterior fates [1–6] . There is evidence for both hierarchical and parallel relationships among their signaling pathways . For example , RA appears to act downstream of Fgfs and Wnts in promoting posterior identities in the neurectoderm , but not in the suppression of anterior cell fates [6] . RA , Fgfs , and Wnts are all produced at the posterior of the embryo , and might therefore be expected to form posterior-to-anterior gradients ( for Fgf8 this has been demonstrated directly; [7] ) . It is not clear which of these gradients is ultimately responsible for providing the positional cues that specify where cell fate boundaries form . This is an important question because the signaling pathways that drive expression of cell fates need not be the same as those that convey positional information . For example , it has recently been suggested that RA acts solely as a permissive factor in hindbrain patterning , required for the adoption of posterior fates , but downstream of signals that determine where such fates are specified [8] . Support for this view comes largely from observations that , in RA-deficient mouse , rat , quail , or zebrafish embryos , hindbrain pattern can be rescued by exposure of the embryo to uniform extracellular concentrations of RA [8–15] . Despite such observations , other experiments point to a role for RA that is more like that of a classical , graded morphogen . For example , embryos deficient in vitamin A ( the dietary precursor of RA ) , or loss-of-function mutants in the RA synthetic enzyme aldehyde dehydrogenase 1a2 ( aldh1a2 , also known as raldh2 ) , show loss of posterior hindbrain segments ( rhombomeres ) 5–7 ( r5–7 ) , and expansion of more anterior ones ( r2–4 ) [9–11 , 15 , 16] . Treatment of chick embryos with an RA receptor ( RAR ) antagonist also causes progressive , concentration-dependent anteriorization [17] . Conversely , exposure of embryos to exogenous RA leads to a concentration-dependent loss of the forebrain and eyes , and progressive posteriorization of ( rhombomere ) identities [12 , 18–21] . Loss of cyp26a1—a cytochrome p450 enzyme that oxidizes RA to more polar metabolites , promoting its removal from tissues—causes r5–7 to expand anteriorly in mice and zebrafish [22–24] , and the combined depletion of Cyp26a1 and related enzymes Cyp26b1 and Cyp26c1 results in severe posteriorization of the zebrafish hindbrain [8] . Efforts to understand the role of RA in hindbrain patterning have recently been fostered by the recognition that localized degradation is critical in controlling RA signaling . Sirbu et al . [25] suggested that degradation creates shifting boundaries of RA activity and that duration of exposure to RA rather than concentration controls rhombomere fate . In contrast , Maves et al . [12] argued that pattern is determined by an RA morphogen gradient that is not fixed , but grows steeper with time , specifying rhombomeres sequentially from anterior to posterior . Recently , Hernandez et al . [8] proposed a “gradient-free” model in which discrete zones of local RA degradation are progressively induced over time , in coordination with changes in cell responsiveness to RA . Since this model does not explain how such zones are placed in their correct positions , it reverts to an essentially permissive role for RA ( i . e . , positional information must still come from signals other than RA , such as Fgfs or Wnts ) . Here we propose a new view that reconciles both existing and new data . In it , local degradation of RA plays a central role , yet long-range RA gradients directly provide graded positional cues . This model is motivated by experimental data on the control of expression of Cyp26 enzymes , and by computational analysis of feedback and feedforward interactions between RA and Fgf signaling that are revealed by these data . We argue that this makes the RA gradient resistant to fluctuations ( robust ) in RA synthesis rate and stable over an expanding field of cells . We also show that one characteristic of such a morphogen gradient system based on regulated degradation is the ability to produce gradients of relatively normal shape even when RA is applied globally to an embryo . The model also suggests a mechanism by which the incorporation of retinoids into patterning systems may have taken place during chordate evolution .
Studies in a variety of vertebrate embryos support the view that RA diffuses through tissues [26] , can act at long range , and has graded effects on patterning [27–29] . However , direct evidence that diffusible RA sets up a gradient of RA response , which in turn assigns different fates to cells at different levels of RA signaling , is still lacking . To investigate this question , we utilized a zebrafish line carrying a yellow fluorescent protein ( yfp ) transgene under the control of retinoic acid response elements ( rare:yfp [30] ) . In transgenic embryos , expression of YFP was readily detectable in the developing spinal cord and posterior hindbrain at 22–24 hours post-fertilization ( hpf ) ( up to the r6/r7 boundary; Figure 1A ) , and was clearly graded from posterior to anterior in the posterior hindbrain ( Figure 1B ) . Expression was completely lost in RA-deficient embryos injected with an aldh1a2 morpholino ( aldh1a2-MO; Figure 1C ) , and could be restored by the prior transplantation of cells overexpressing aldh1a2 and targeted to somitic mesoderm , where aldh1a2 is normally expressed ( Figure 1D ) . In most cases , rescue spanned the width of the neural tube ( approximately six cell diameters [∼70 μm]; Table 1 ) . Similarly , ion-exchange beads soaked in 10–100 μM RA and implanted just anterior to the first somite at 19 hpf also strongly induced rare:yfp ( Figure 1E–1G ) . YFP expression was observed at a greater distance—up to 300 μm—when beads were soaked in 100 μM RA ( Table 1 ) . Interestingly , responses to low ( 10 μM ) doses of RA were markedly asymmetrical ( Figure 1F and 1H ) , extending up to 300 μm posteriorly but truncated anteriorly near the r6/7 boundary . This asymmetry was eliminated with MOs directed against cyp26b1 and cyp26c1 ( Figure 1I ) , which are highly expressed just anterior to the r6/7 boundary [8] . From these results , we infer that RA signaling in the neural ectoderm is graded in vivo , that signaling is dose dependent , and that long-range effects of RA can be mediated across territories in which RA cannot be synthesized ( ruling out a relay-type model of RA action at a distance ) . In addition , asymmetries in the response to RA signaling arise in a manner consistent with localized effects of Cyp26 enzymes on RA responsiveness , as previously proposed [8] The rare:yfp reporter appears primarily to read out sustained , high-level RA signaling . For example , YFP was not normally detected in transgenic gastrula- or early somite-stage embryos , when RA is known to act , but could be induced by beads soaked in very high levels of RA ( 10–25mM; Figure S1A–S1C ) . As a more sensitive readout of early RA signaling , we monitored two direct RA target genes , hoxb4 and hoxb5a [31] . Normally , hoxb4 has an anterior limit at the r6/7 boundary , whereas hoxb5a is expressed further posteriorly in the spinal cord ( Figure 2A ) . Both genes require RA signaling for their early expression ( Figure 2B ) and both possess functional , phylogenetically conserved RAREs within their promoters [32 , 33] . Since hoxb4 is expressed more anteriorly , one might expect it to be activated by lower RA concentrations than hoxb5a . Consistent with this , beads coated in 100 μM RA and implanted into embryos treated with 10 μM diethylaminobenzaldehyde ( [DEAB ) , which inhibits class 1 aldehyde dehydrogenases and thereby blocks RA synthesis [12 , 14 , 34 , 35] , induced expression of both hox genes ( Figure 2C ) at early neurula stages ( 10–11 hpf ) . Induction was detectable within 60 min of bead implantation ( Figure 2D–2F ) , and consistently extended further from the bead for hoxb4 ( 94 . 86 ± 17 . 18 μm; n = 4 ) than for hoxb5a ( 38 . 44 ± 6 . 27 μm; n = 5 ) . These results suggest that there are multiple thresholds of RA response , with more anteriorly expressed target genes exhibiting lower expression thresholds . Together , the results in Figures 1 and 2 imply that RA can establish long-range gradients that directly specify the location of expression of genes that control A–P identity . In contrast to the loss of asymmetry in the RA response at 24 hpf caused by reducing cyp26b1 and cyp26c1 function ( Figure 1H and 1I ) , the range of hoxb4 and hoxb5a responses at 13–14 hpf was more than doubled by inhibiting cyp26a1 function ( Figure 2G–2L ) . This was unexpected because , at the stage of bead implantation for these experiments , cyp26a1 is not normally expressed within the hindbrain ( Figure 3A ) . This suggested that RA beads were themselves inducing cyp26a1 . Indeed , in situ hybridization revealed marked cyp26a1 induction up to 53 μm from the bead ( Figure 3B ) . In contrast , RA beads only weakly induced cyp26b1 expression ( in cranial mesoderm; Figure 3C , arrowhead ) , and did not induce cyp26c1 at all ( Figure 3D ) , findings that are consistent with studies in chick [36] . Whole-embryo treatment with high RA levels ( 1 μM ) also failed to induce cyp26b1 or cyp26c1 , suggesting that these enzymes do not simply have lower sensitivities to RA than cyp26a1 ( unpublished data ) . If anything , RA appeared to have a short-range inhibitory effect on cyp26c1 expression ( Figure 3D ) . Although it has been observed that exogenous RA induces cyp26a1 in the hindbrain [36 , 37] , loss of RA signaling does not affect endogenous cyp26a1 expression [8 , 25 , 37] . This has led to the view that cyp26a1 induction occurs only at supra-physiological levels of RA , possibly as a form of protection against teratogenicity [8] . Yet in Figure 3 , the range over which cyp26a1 is induced implies that its expression is triggered by levels of RA similar to those that induce hoxb4 and hoxb5a . This result is even more curious in view of the fact that these hox genes and cyp26a1 are not normally coexpressed in the neurectoderm . To try to make sense of these observations , we reexamined endogenous patterns of cyp26a1 expression in embryos , starting at gastrula stage ( 4–9 hpf; Figure 4 ) . At this stage , cyp26a1 is reported to be restricted anteriorly to the animal pole in the presumptive forebrain and midbrain , and posteriorly to the involuting margin [6 , 8] . Close inspection , however , revealed the presence of previously unrecognized , low-level cyp26a1 expression in between these two sites ( Figure 4A and 4C ) . This was observed at mid-gastrula stages ( 75% epiboly ) , predominantly in the hypoblast ( future mesendoderm; lower panel , Figure 4A ) and extended approximately halfway between the anterior and marginal cyp26a1 expression domains ( arrow to arrowhead on Figure 4A ) . By late gastrula ( 90% epiboly ) , weak cyp26a1 expression covered the entire region between the anterior and marginal domains ( arrow to arrowhead on Figure 4C ) and was observed in both the epiblast ( future ectoderm ) and hypoblast ( lower panel , Figure 4C ) , encompassing the entire presumptive hindbrain field . Because this expression was weak , it might easily have been overlooked were it not for the fact that it completely disappeared upon treatment with 10 μM DEAB ( Figure 4B and 4D ) . In contrast ( and as previously reported ) , the strong expression of cyp26a1 in the anterior central nervous system and posterior margin was RA-independent [37] . Similar RA-dependent , weak cyp26a1 expression in the presumptive hindbrain can be seen in a recent study of vitamin A–deficient quails ( see Figure 3B of [36] ) . These data indicate that expression of the major RA-degrading enzyme , cyp26a1 , in cells within or adjacent to the presumptive hindbrain during gastrulation is under the control of RA signaling . This suggests a more subtle role for RA degradation than simply creating boundaries beyond which RA cannot spread . Given that expression at such locations is weak , the question arises as to whether it is of any physiological consequence . To address this , we used mosaic analysis , transplanting cyp26a1 morphant cells at 5 hpf ( 50% epiboly ) into wild-type embryos and assaying the expression of hoxb1b at 9–10 hpf ( 90% epiboly–tailbud; Figure 5A and 5D ) . When located within the endogenous hoxb1b domain , Cyp26a1-deficient cells down-regulated hoxb1b ( Figure 5B–5C′ ) , Cyp26a1-deficient cells also often caused an increase in expression in adjacent cells ( Figure 5E–5F′ ) . These results indicate that the low-level expression of cyp26a1 within and near the presumptive hindbrain is , indeed , functionally relevant . Moreover , because the observed effects were limited either to the transplanted cells themselves or cells nearby , the results imply that cyp26a1 normally influences RA signaling only over very short ranges ( the significance of this point will be discussed later ) . If low-level cyp26a1 expression in the future hindbrain of the gastrula-stage embryo is induced by RA signaling , why does such expression decline from anterior to posterior ( Figure 4A–4D ) , a direction along which RA signaling presumably increases ? This observation suggests that there must be additional , position-specific inputs into cyp26a1 . To clarify the nature of such inputs , we implanted RA beads into different A–P locations in DEAB-treated embryos at mid-gastrula ( 8 hpf ) . This rapidly induced cyp26a1 expression ( ≤30 min ) , but the pattern of induction depended on the precise bead position ( Figure 6A–6D ) . Beads placed within 100 μm of the anterior domain of high-level cyp26a1 expression in the brain induced cyp26a1 anterior to the bead—in a pattern that merged with endogenous cyp26a1—but not posteriorly ( Figure 6C ) . Beads placed further posteriorly at mid-trunk levels ( Figure 6D ) , failed to induce cyp26a1 expression near the bead at all ( where RA levels should be highest ) , but only far anteriorly ( white arrowhead ) and near the posterior margin ( white arrow ) . These results suggest that the capacity of cells to induce cyp26a1 in response to RA declines from anterior to posterior , rising again near the posterior end of the embryo . A similar phenomenon can be seen if DEAB-treated embryos are treated with increasing concentrations of exogenous RA ( Figure S2A–S2D ) . An attractive candidate for a factor controlling such positional effects is Fgf . Eliminating all Fgf signaling is known to lead to cyp26a1 expression along the entire A–P axis [6] , which suggests that one role of Fgfs may be to suppress cyp26a1 expression . We examined this possibility by treating embryos with various concentrations of the Fgf receptor tyrosine kinase inhibitor SU5402 . As shown in Figure 6E–6G , in the presence of SU5402 , the border of high-level cyp26a1 in the neurectoderm shifted , in a concentration-dependent fashion , progressively toward the posterior . Interestingly , the expansion of cyp26a1 expression upon blockade of Fgf signaling was prevented by simultaneous administration of DEAB ( Figure 6H ) . Thus , Fgf appears to act indirectly , by inhibiting RA-mediated activation of cyp26a1 expression . From the information presented above , we infer that RA gradients , at least during gastrulation , should be influenced by interacting feedback ( RA signaling inducing RA degradation ) and feedforward ( Fgf signaling repressing RA degradation ) effects . To identify the consequences of such effects , we turned to quantitative modeling , using partial differential equations to describe the spatial dynamics of RA diffusion and degradation ( see Materials and Methods ) . Because RA is a lipophilic molecule that can pass directly through cell membranes , we initially ignored cell boundaries , modeling RA as a substance that moves freely through tissue , being degraded at any location in proportion to the amount of Cyp26 enzyme expressed by cells there . However , the observations in Figure 5 made us realize that this could not be the case . The fact that the effects of blockade of cyp26a1 in transplanted cells were nearly cell autonomous tells us that RA molecules that enter cells with even low levels of cyp26a1 expression get degraded before they have an opportunity to exit the cell . The only way that RA can act over long distances , yet effectively never leave cells , is if transport precedes cell entry , i . e . , RA moves a long way within the extracellular space before it is taken up . In fact , this makes sense when one considers that RA binds proteins tightly , and must dissociate from them to cross a plasma membrane . It has been shown in cell cultures that the presence of physiological levels of albumin results in very slow RA uptake by cells [38] . Given that RA bound to a soluble protein carrier could be expected to diffuse several hundred micrometers in a matter of minutes , we reasoned that a realistic model of RA dynamics needs to include separate , interconverting pools of extracellular protein-bound and intracellular RA , with RA signaling being solely a function of the latter . We developed such a model for the early gastrula-stage embryo ( see Materials and Methods ) , when RA is known to be required for hindbrain patterning , and its behavior was analyzed over a wide range of parameter choices ( Figure 7 ) . The model readily generates patterns of RA signaling and cyp26a1 expression ( Figure 7A ) that , for reasonable parameter choices , fit experimental expectations . As a result of exploration of the model , three observations were made: First , feedback regulation of RA degradation makes the intracellular RA gradient robust to changes in the rate of RA synthesis . In the absence of any controls on cyp26a1 expression , a change in the rate of RA synthesis would be expected to produce a similar change in the amplitude of the RA gradient . Figure 7B illustrates the effect of a 2-fold decrease in RA synthesis rate on the ( intracellular ) RA signaling gradient . Note the large shift in the locations at which RA signaling levels cross threshold values . In contrast , by allowing for strong feedback regulation of cyp26a1 , the model produces a much more robust gradient ( Figure 7C ) . The idea that “self-enhanced degradation” could render morphogen gradients robust to fluctuations in synthesis rates was first shown in theoretical work on Wingless and Hedgehog gradients in Drosophila [39] . Since rates of RA synthesis in vivo are likely to be highly labile to environmental factors ( e . g . , the availability of its dietary precursor , vitamin A ) , exploitation of such a mechanism would seem especially advantageous for RA gradients . Second , feedforward effects of Fgfs couple the shape of the RA gradient to that of the Fgf gradient . Previous work has shown that the Fgf gradient is formed by diffusion and receptor-mediated destruction [7] , so its shape should be determined by a balance between Fgf synthesis and receptor binding and dynamics [40 , 41] . During gastrulation , the source of Fgf expression in the posterior marginal zone moves continuously away from the anterior of the embryo , so the Fgf gradient itself would need to undergo continual change in order to maintain stable levels of Fgf at constant positions . The easiest way to accomplish this would be for Fgf expression to increase as gastrulation proceeds ( Figure 7D ) . Were the RA gradient not coupled to the Fgf gradient through the latter's effects on RA degradation , elongation of the embryo would continually shift the RA gradient toward the posterior ( Figure 7E , dotted line ) , unless RA synthesis were continually increased by just the right amount . In contrast , because of Fgf's influence on cyp26a1 induction , increased Fgf production automatically results in an increase in RA signaling that compensates for axis elongation ( Figure 7E , dashed line ) . In effect , the RA gradient becomes entrained to the Fgf gradient . Third , even an embryo that makes no RA and is bathed in uniform RA will generate a relatively normal RA signaling gradient . A remarkable consequence of the entrainment of the RA gradient to the Fgf gradient is that replacement of the endogenous RA source at the posterior with a spatially uniform influx of RA into the extracellular space still produces a marked posterior-to-anterior gradient of intracellular RA ( Figure 7F ) . At appropriate doses of exogenous RA , the RA signaling gradient will closely resemble the endogenous one , especially at anterior sites ( Figure 7G ) . This effect nicely reconciles data showing that RA acts in a concentration-dependent manner [12 , 17–21] with observations that uniform administration of RA can effectively rescue pattern in RA-deficient embryos [8–12] . Furthermore , because of the feedback effect of RA on its own degradation , the exogenous gradient can be expected to be as robust to RA dosage as the endogenous one ( compare Figure 7H and 7C ) . Indeed , marked robustness of rescue by exogenous RA can be demonstrated experimentally ( Figure S3 ) : when DEAB-treated embryos were exposed to exogenous RA starting at 4 hpf , relatively normal hindbrain patterning was restored over an 8-fold range ( 0 . 625–5 nM ) of RA concentrations . Hernandez et al . [8] reported similar rescue over a 20-fold RA range ( 0 . 5–10 nM ) , using embryos treated at slightly later stages ( 5 . 3 hpf ) . We interpret such robustness not as an indication that RA acts in a concentration-independent manner , but rather that the distribution and concentration of RA within cells is controlled through RA- and Fgf-signaling–dependent control processes , such as those described here .
The classical view of a morphogen is a molecule with a graded distribution that acts in a concentration-dependent fashion to assign positional identities to a field of cells [42] . The question of whether RA is a morphogen has been controversial in a number of contexts , including patterning of the limb , heart , and brain [8 , 12 , 43–47] . Because RA so often acts together with other signaling molecules—such as Fgfs , Hedgehogs , and Bmps—to influence patterning , the issue of whether RA plays an instructive or merely permissive role in conveying positional information is a continual source of debate . Here , we suggest that the key to resolving this debate—in the hindbrain at least—is to treat RA and Fgf as a single , integrated morphogen system , in which an RA gradient assigns positional identities , but the shape of the RA gradient is established collaboratively by RA and Fgf through their control of cyp26a1 expression ( Figure 8 ) . This view reconciles several observations: It agrees with experimental evidence that RA and Fgf both promote posterior identity , with RA acting downstream of Fgf [6] . It accommodates data that RA acts over long range to produce graded outputs ( Figures 1 and 2 ) , and that both Fgf and RA control the distribution of RA-degradative enzymes ( Figures 3–5 ) . Perhaps most importantly , it explains how administration of uniform , exogenous RA can rescue patterning that is normally driven by RA synthesized in a localized fashion ( Figure 7 ) . This view also provides a mechanism by which the RA gradient can grow over time during gastrulation ( Figure 8B ) —a key element of the hindbrain patterning model of Maves and Kimmel [12]—without the need for complex regulation of RA synthesis . Although this still requires Fgf signaling to increase during gastrulation , the known coupling of Fgf signaling to gastrulation movements provides a means by which such increases may be coordinated [48 , 49] . Central to the mechanism proposed in this study is the regulation of RA degradation in a manner that provides for robustness to uncertain levels of RA synthesis . Evidence that RA synthesis is highly susceptible to fluctuations in levels of dietary precursors includes the fact that high doses of vitamin A readily produce retinoid teratogenicity in mammals [50] . Moreover , in the zebrafish embryo , even modest increases in intracellular levels of the RA precursor retinal lead to strong posteriorization if Cyp26a1 function is blocked [8] . Although the latter effect has been interpreted as evidence that Cyp26a1 exists to protect embryos from teratogenicity , it is also a consequence of our model in which Cyp26a1 plays a central role in controlling the shape of an RA gradient . Surprisingly , this role is not one in which Cyp26a1 simply acts as a sink for extracellular RA , since removing Cyp26a1 function in transplanted cells has nearly cell autonomous effects ( Figure 5 ) . This suggests that RA degradation within a cell does not greatly affect the concentration of RA within a neighboring cell and that RA must act over long distances by traveling within the extracellular space before it enters cells . Indeed , our results agree with those of Sirbu et al . [25] , Hernandez et al . [8] , and others in emphasizing the importance of localized degradation in controlling RA signaling in the developing hindbrain . Unlike Hernandez et al . [8] , however , we find no need to reject the idea of an RA gradient in favor of a model driven by the sequential appearance of sharp boundaries of cyp26b1 and cyp26c1 expression . In fact , an RA gradient primarily controlled by early , smoothly graded Cyp26a1 activity better explains the relatively mild hindbrain phenotypes of cyp26b1/cyp26c1 double morphants , and the requirement for cyp26a1 to be functional for global RA treatments to rescue RA-deficient embryos . In the present study , a mathematical model was used to explore the consequences of feedforward and feedback effects of Fgf and RA on hindbrain patterning . Such a model is not intended to represent a complete or accurate picture of the early embryo , but simply to elucidate how interacting signaling pathways might work together to achieve useful ends . In fact , there is evidence that the regulatory interactions in this system go well beyond those explicitly modeled here ( Figure 8A ) . For example , there is evidence that RA signaling inhibits expression of the RA biosynthetic enzyme aldh1a2 [37] , which is likely to further decrease the sensitivity to RA synthesis to RA precursor levels . RA may affect the expression of Fgfs , and Fgf signaling is required for the expression of aldh1a2 [51] . RA signaling also appears to have complex effects on the expression of Fgf receptors [51] . Determining whether these effects contribute to robustness , to the coupling between RA and Fgf gradients , or to the performance of other tasks , awaits further experimental and computational work . It should also be noted that , whereas the present model treats the RA gradient as a system operating near its steady state , the rapidity with which gastrulation proceeds in the zebrafish raises the possibility that patterning occurs under pre–steady-state conditions . Interestingly , recent theoretical work suggests that responses to morphogen gradients in the pre–steady-state regime can be much more robust than those at steady state [52] , and preliminary calculations confirm that this is true for the RA gradient model described here ( unpublished data ) . Thus , the sources of robustness in RA gradients may be many and varied . Collaboration between RA and Fgfs appears to be emerging as a common motif in vertebrate pattern formation , not only in the nervous system ( e . g . , hindbrain , forebrain , ventral retina , and tailbud ) , but elsewhere ( e . g . , somites , heart , pancreas , and limbs ) . Intriguingly , the details of the interactions between RA and Fgfs are often quite different in different systems . For example , RA and Fgf gradients with opposite , rather than parallel , orientation are thought to play key roles in the patterning of somites , and recent modeling studies have provided insights into the tasks such an arrangement can accomplish [53 , 54] . It will be interesting to see whether or not there are common regulatory loops and control mechanisms in all RA/Fgf systems . It will also be interesting to see whether or not analogous collaborative relationships exist among other morphogens that pattern animal embryos . The roles of Fgf and RA in A–P patterning appear to have arisen in the deuterostome lineage , as protostomes ( e . g . , nematodes and arthropods ) apparently lack both RARs and the enzymes that synthesize RA [55] . Yet nuclear hormone receptors , including Retinoid X receptors ( RXR ) s , are expressed throughout the animal kingdom and serve in the detection of both endogenous hormones and environmental compounds [56 , 57] . Invertebrates also use cytochrome p450 enzymes to oxidize a wide variety of endogenous and environmental compounds [58 , 59] . Our results suggest that the initial step in the evolution of the RA gradient system may have occurred when one of these enzymes happened to fall under the control of an ancestral A–P patterning system , so that its expression in embryos became graded from posterior to anterior . As a result , any environmental substance that it degraded would automatically form a gradient within the embryo , the shape of which would be read out at the transcriptional level through RXR-mediated signaling . In this way , a primitive RA-like gradient system could be established that was later refined , e . g . , by developing ways to store the precursor of the compound , and produce an active form of it at one end of the embryo . Although we can only speculate as to the advantage gained by incorporating RA into a preexisting A–P patterning system , we note that Kerszberg [60] showed that gradients of nuclear hormone receptor ligands have an intrinsic ability to form domains of gene expression with multiple sharp boundaries . Bringing RA into A–P patterning may thus have been critical in the evolution of the highly subdivided , rhombomeric organization of the vertebrate hindbrain .
Embryos were obtained in natural crosses and staged according to Kimmel et al . [61] . rare:yfp ( Tg ( RARE-gata2:NTD-eYFP ) —ZDB-LOCUS-051209–1 ) fish [30] were a kind gift of E . Linney . Antisense MOs were designed to translation start sites in cyp26b1 ( 5′-TCAAAACTCTCGAAGAGCATGGCTG-3′ ) and cyp26c1/d1 ( 5′-AAATCGTGCCCGAACATCTCGAACG-3′ ) . aldh1a2- and cyp26a1-MOs have been described [11 , 24] . MOs were dissolved in Danieau buffer; 0 . 5 nl of solution was injected at the one-cell stage . rare:yfp experiments . Wild-type donor embryos were injected at the one-cell stage with a mixture of 1 . 5% TRITC-dextran ( neutral , 10 , 000 Mr ) , 1 . 5% biotinylated-dextran ( lysine-fixable , 10 , 000 Mr ) , and 2 ng of aldh1a2 RNA , and treated with 10 nM retinol ( vitamin A ) . rare:yfp host embryos were injected with 1 ng of aldh1a2-MO . At sphere stage , embryos were mounted in 3% methylcellulose , and 30–50 cells were transplanted from the margins of labeled donor embryos to the margins of unlabeled hosts . Post-gastrula transplants were performed by mounting embryos in 0 . 75% low-melt agarose , and cells were transplanted from the somites of donor embryos into the forming somites of host embryos . Transplanted embryos were mounted in 1 . 5% agarose for analysis by confocal microscopy ( using a Zeiss LSM510Meta ) . Images were processed , and the fluorescence intensity graph was produced using ImageJ software ( http://rsb . info . nih . gov/ij/ ) . cyp26a1-MO experiments . Wild-type donor embryos were injected at the one-cell stage with a mixture of 1 . 5% Fluor-ruby-dextran ( lysine-fixable , 10 , 000 Mr ) , 1 . 5% biotinylated-dextran ( lysine-fixable , 10 , 000 Mr ) , and 2 ng of cyp26a1-MO . At 30%–50% epiboly ( 4 . 7–5 . 3 hpf ) , 30–50 cells were transplanted from the margins of labeled donor embryos to the margins of unlabeled wild-type hosts . Transplanted embryos were fixed at 90% epiboly ( 9 hpf ) and stained for hoxb1b expression by in situ hybridization . The following stock solutions were stored in DMSO at −80 °C: 10 mM 4- ( diethylamino ) -benzaldehyde ( DEAB; Acros organics ) , 10 mM all-trans retinoic acid ( RA; Sigma ) , and 10 mM SU5402 ( CalBiochem ) . These were diluted in Embryo Medium ( EM ) for treatments . Sibling controls were incubated in corresponding dilutions of DMSO . All incubations were done in the dark . AG 1-X8 ion-exchange beads ( formate form; BIORAD ) were soaked in different concentrations of all-trans RA in EM for 1 h ( control beads were incubated in similar amounts of DMSO in EM ) . Beads were rinsed in EM for 5 min before implantation . Embryos were mounted in 3% methylcellulose , a small slit was cut in the epidermis with a tungsten needle , and an RA/DMSO-coated bead ( 40–60 μm in diameter ) was inserted through the slit . Embryos were then incubated at 28 . 5 °C in the dark prior to fixation . In situ hybridization was carried out as described previously [62] . Probes included hoxb4 , hoxb5a , and hoxd4a [31] , cyp26a1 [6] , cyp26b1 ( IMAGEclone3722563; EcoRI , SP6 ) , cyp26c1/d1 [63] , hoxb1b [64] , krox-20/egr2b [65] , and pax2a [66] . Images were taken and measurements were made using Openlab software ( http://www . improvision . com/products/openlab/ ) . Ranges of hox gene induction ( e . g . , assayed as the distance from the edge of the bead to the most distant hox-expressing cell ) were compared using a two-tailed Student t-test . The RA gradient in the gastrula was modeled as a one-dimensional reaction diffusion system ( cf . [41 , 67 , 68] ) represented by the following equations . [RA]out and [RA]in represent extracellular and intracellular concentrations , respectively , of RA; RAsignal represents the strength of the RA signal ( which may or may not vary linearly with [RA]in , in accordance with exponent n ) ; and [Cyp] represents the intracellular concentration of Cyp26a1 . D is the effective extracellular diffusion coefficient of RA; V ( x ) is the rate of production of RA at position x , typically taken to be a constant in a fixed posterior domain and zero elsewhere; kp is a first-order permeability coefficient that lumps together the processes of RA dissociation from protein , association with and diffusion through the plasma membrane , and reassociation with protein on the other side of the membrane; and kdeg is a degradation constant for intracellular RA . Effects of [RA]in and Fgf on cyp26a1 expression are captured through multiplication of the parameter kdeg by the expression containing γ , f0 , λ , and xf . Here γ is sensitivity to RAsignal-mediated feedback; f0 represents the sensitivity of feedback to Fgf; λ is the length constant of the Fgf gradient ( the inverse of the distance over which it drops to e−1 of its initial value ) ; and xf is the location along the Fgf gradient where [Fgf] = f0 . The parameter β is introduced to allow the effective rate constant for flux of RA out of the extracellular space to be higher than for flux into the intracellular space ( to capture the fact that some endogenous RA should leave the extracellular space entirely , e . g . , by diffusing out of the embryo , or into long-term capture in yolk ) . The x-axis is taken to represent the A–P axis of the gastrula-stage embryo , with x = 0 corresponding to the posterior border of an anterior domain of high cyp26a1 expression ( approximately the r2/r3 boundary ) , and x = xf at the posterior margin . The boundary of a second domain of high cyp26a1 expression is placed 40 μm from the posterior margin . The value of [Cyp] in both of these domains is fixed at kmax . A no-flux boundary condition is placed at x = −200 ( to represent the fact that the problem could be viewed as symmetrical about the anterior-most point of the embryo ) . At the posterior margin , it was assumed that extracellular RA could only leave the embryo by diffusing through cells , so a “leaky” posterior boundary condition was used , in which the parameter kp captured the rate constant of leakage . Steady-state solutions to the equations were obtained numerically using Mathematica software by solving the time-dependent equations for sufficiently long times that results no longer changed significantly ( typically after a few hours ) . In some cases , steady-state calculations were done directly using a Matlab boundary value ODE Solver . Parameter values were manually explored over several orders of magnitude . In all panels of Figure 7 , parameter values were D = 18 μm2sec−1 , n = 2 , λ = 0 . 019 μm−1 , kp = 10−4sec−1 , and γ = 2 . 5 × 10−5 . ( Other parameter values are given in the figure legend . ) The choice of modest cooperativity in signaling ( n = 2 ) acknowledges the intrinsic capabilities of nuclear hormone receptors [60] , but is not , in fact , required to produce the qualitative findings of this study . It should be noted that the length scales of the RA gradients produced in Figure 7 are determined largely by the length scale of the Fgf gradient ( 1/λ ) , which was chosen arbitrarily , and the diffusion coefficient D of protein-bound RA , which is not known accurately . Accordingly the micrometer values on the abscissa should be taken as essentially arbitrary and easily adjustable to be several fold smaller or larger through modest changes in these parameter values .
The GenBank ( http://www . ncbi . nlm . nih . gov/Genbank ) accession numbers for the genes discussed in this paper are aldh1a2 ( NM_131850 ) , cyp26a1 ( NM_131146 ) , cyp26b1 ( NM_212666 ) , cyp26c1 ( NM_001029951 ) , hoxb1b ( NM_131142 ) hoxb4 ( NM_131118 ) , hoxb5a ( NM_131101 ) , hoxd4a ( NM_130757 ) , krox-20/egr2b ( NM_130997 ) , and pax2a ( NM_131184 ) . The ZFIN ( Zebrafish Model Organism database; http://www . zfin . org ) ID numbers are aldh1a2 ( ZDB-GENE-011010–3 ) , cyp26a1 ( ZDB-GENE-990415–44 ) , cyp26b1 ( ZDB-GENE-990415–44 ) , cyp26c1 ( ZDB-GENE-050714–2 ) , hoxb1b ( ZDB-GENE-980526–290 ) , hoxb4 ( ZDB-GENE-990415–105 ) , hoxb5a ( ZDB-GENE-980526–70 ) , hoxd4a ( ZDB-GENE-980526–214 ) krox-20/egr2b ( ZDB-GENE-980526–283 ) , and pax2a ( ZDB-GENE-990415–8 ) .
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The formation of gradients of morphogens , signaling molecules that determine cell fates in a concentration-dependent manner , is a fundamental process in developmental biology . Several morphogens pattern the anterior–posterior ( head to tail ) axis of the vertebrate nervous system , including the vitamin A derivative , retinoic acid ( RA ) and fibroblast growth factors ( Fgfs ) . However , it remains unclear how the activities of such morphogen gradients are coordinated . We have addressed this question by combining genetic experiments in zebrafish and computational analyses . We show that RA acts as a graded signal over long distances and that its gradient is shaped , to a large extent , by local control of RA degradation . In particular , RA promotes and Fgf suppresses RA degradation , thereby linking the shapes of RA and Fgf gradients . Computational models suggest that this linkage helps make RA-mediated patterning robust to changes in the rate at which RA is synthesized ( which may vary with levels of dietary vitamin A ) as well as in the size and shape of the embryo during development . Analogous regulatory loops may be used for similar purposes in other tissues in which RA and Fgfs interact , as well as in other morphogen systems .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods",
"Supporting",
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[
"developmental",
"biology",
"computational",
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2007
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Complex Regulation of cyp26a1 Creates a Robust Retinoic Acid Gradient in the Zebrafish Embryo
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The maintenance of pluripotency and specification of cellular lineages during embryonic development are controlled by transcriptional regulatory networks , which coordinate specific sets of genes through both activation and repression . The transcriptional repressor RE1-silencing transcription factor ( REST ) plays important but distinct regulatory roles in embryonic ( ESC ) and neural ( NSC ) stem cells . We investigated how these distinct biological roles are effected at a genomic level . We present integrated , comparative genome- and transcriptome-wide analyses of transcriptional networks governed by REST in mouse ESC and NSC . The REST recruitment profile has dual components: a developmentally independent core that is common to ESC , NSC , and differentiated cells; and a large , ESC-specific set of target genes . In ESC , the REST regulatory network is highly integrated into that of pluripotency factors Oct4-Sox2-Nanog . We propose that an extensive , pluripotency-specific recruitment profile lends REST a key role in the maintenance of the ESC phenotype .
Differentiation of pluripotent embryonic stem cells ( ESC ) is accompanied by wholesale changes in the transcriptome and epigenome [1–4] . Conversely , an intricate and integrated network of transcriptional regulators is responsible , under the correct conditions , for maintaining ESC in their unique , undifferentiated state . Several key transcription factors required for maintaining pluripotency have been identified and include Oct4 , Sox2 , and Nanog . The scale of the transcriptional regulation governed by these factors is apparent from recent genome-wide chromatin immunoprecipitation ( ChIP ) studies , which have identified thousands of genomic binding sites for Oct4 [2 , 5] , Sox2 [2] , Nanog [5] , and c-Myc [6] . However , ChIP studies reveal only occupancy and cannot , by themselves , indicate the functionality of any bound transcription factor . Nor is it is known how the occupancy and efficacy of any particular transcription factor vary across different cell types . These issues are particularly germane to pluripotent and multipotent stem cells , where expression of individual transcription factors can lead to differentiation and wholesale changes in the cellular transcriptome . For instance , the HMG protein Sox2 is required both for maintenance of pluripotency in ESC [7] and for maintenance of the undifferentiated state in neural progenitors [8 , 9] . Further evidence for diversity of function can be seen with Oct4: Although primarily associated with pluripotency , forced expression of Oct4 can also promote neurogenesis [10] . In parallel to maintaining the undifferentiated state , there is also a strict requirement for both pluripotent and tissue-specific stem cells to suppress expression of inappropriate lineage-specific genes . In ESC , this is manifested as silencing of all lineage-specific genes , whereas in committed neural stem cells ( NSC ) , precocious expression of differentiated neuronal genes must be prevented . In both cases , neuronal gene expression must be suppressed . One factor that is responsible for this function in both ESC and NSC is REST ( RE1-silencing transcription factor ) . REST ( also called NRSF ) is expressed throughout early development where it represses expression of neural genes in both ESC [11] and NSC [12 , 13] . However , REST appears to have quite different roles in the two cell types . Whereas REST does not appear to be necessary for differentiation of the blastocyst into the three germ layers or for formation of the neural plate and early neural tube [14] , down-regulation of REST is required , and in some cases is sufficient , for neuronal differentiation [2 , 5 , 11 , 13 , 15 , 16] . The observation that REST is directly regulated by Oct4 , Sox2 , and Nanog [5] provides an intriguing direct linkage between suppression of neuron-specific genes and pluripotency . A direct interaction between REST and Nanog proteins also links these two regulatory networks [17] . However , it remains unknown whether these cell-specific transcription programs are underwritten by interaction of REST with distinct sets of target genes in ESC and NSC . Bioinformatic studies [17–20] have identified several thousand potential REST binding sites in the human and mouse genomes , while numerous ChIP studies have shown that REST is present at distinct sites in different cell types [20] . For instance , REST is present at the RE1 site of the Bdnf gene in chromatin from mouse forebrain , but cannot be detected at the same locus in mouse liver [21] . However , recent genome-wide ChIP sequencing studies have shown that REST can be detected at most RE1 sites in a human T cell line and a mouse kidney cell line [22 , 23] . Although the precise reasons for this apparent disparity are not clear , a strong possibility is that the widespread occupancy detected by ChIP-SACO ( serial analysis of chromatin occupancy ) and ChIP-seq is a reflection of the increased sensitivity of these deep-sequencing–based methodologies and their ability to detect low-level or transient interactions . In this study , we have taken a three-pronged approach to compare and contrast the REST regulatory network in mouse ESC and NSC . Firstly , we have used an array-based chromatin immunoprecipitation microarray ( ChIP-chip ) approach to examine differences in target gene occupancy by REST in both ESC and NSC , as well as in differentiated fibroblasts . Secondly , we have used an unbiased , genome-wide approach to identify novel REST binding sites by applying a deep sequencing chromatin immunoprecipitation paired-end tag ( ChIP-PET ) strategy . Thirdly , we have identified those genes whose transcription is regulated by REST by comparing the transcriptomes of ESC and NSC before and after blocking REST activity with a specific dominant-negative construct . We find that REST binding sites can be classified into cell-type–independent loci , which are bound in all cell types we examined , and an ESC-specific set , which appear to regulate genes involved in signaling and transcriptional regulation related to pluripotency . Inhibition of REST function by overexpression of a dominant-negative construct revealed that the genes regulated by REST in ESC and NSC are almost completely different . Thus , although there is an extensive common core of occupied REST sites in ESC and NSC , the REST regulatory networks operate distinctly in the two cell types . This is , to our knowledge , the first study that has compared the regulatory network of any transcription factor in pluripotent ESC and multipotent NSC , and it lends novel insights to lineage specific regulation of gene expression .
To broadly interrogate in vivo the occupancy of the REST transcriptional repressor across the genome of mouse stem cells , a DNA microarray was developed to be used in combination with chromatin immunoprecipitation ( ChIP-chip ) . The microarray was spotted with 1 , 095 oligonucleotide probes ( Dataset S1 ) representing RE1 sites computationally predicted by the 21-bp RE1 position-specific scoring matrix ( PSSM ) [18] . A unique probe was designed within a 200-bp window centered on each site , excluding the actual RE1 site to avoid cross-hybridization . The ChIP-chip microarray also included 92 probes from intergenic and coding regions of the genome that are distal from any known RE1 , to serve as negative controls . To optimize and monitor performance of our microarray , multiple probes were tiled across RE1 sites associated with the REST target genes Nppa [24 , 25] and Syt4 [20] . We applied this ChIP-chip methodology to map REST occupancy in genetically identical mouse ESC and NSC . ChIP DNA was isolated from undifferentiated pluripotent ESC ( Figure 1A–1C and Figure S1 ) and multipotent NSC ( Figure 1D and Figure S2 ) and hybridized to the RE1 ChIP-chip . Statistically significant enrichments were determined by a combination of Gaussian Mixture Model and Single Array Error Model [26] analyses . The false discovery rate ( FDR ) was selected to optimize sensitivity and selectivity , by comparing both statistical and experimental evaluations of the microarray . The enrichments detected over the tiled regions of Nppa and Syt4 were specific and restricted to small regions around the predicted RE1 sites ( Figure 1E ) . To validate the ChIP-chip performance , quantitative PCR ( qPCR ) was carried out on a randomly selected set of RE1 sites ( Figure 1F and Figure S3 ) . Taking qPCR as the standard , the ChIP-chip correctly called REST occupancy , or lack thereof , at 108 out of 126 RE1 sites ( 86% ) . Importantly , 87% of negative control probes were correctly called by the ChIP-chip , indicating a false positive rate of around 13% as expected from the statistical model . With the statistical parameters used in our studies , we found that the ChIP-chip method accurately assessed REST occupancy at the 1 , 095 predicted RE1 sites . The focused REST ChIP-chip thus provides a robust and rigorous means to compare occupancy of REST across many cell types and under various stages of differentiation , and thereby reveals a more complete and dynamic view of the REST transcriptional regulatory network . We used RE1 ChIP-chip to compare genome-wide REST occupancy across different cell types . It has been established that REST acts by directly binding and recruiting corepressors to RE1s associated with target genes [27] . It is intriguing that REST is expressed in both lineage-restricted neuronal progenitors [12 , 14 , 28] and pluripotent ESC [11 , 13] , given their distinct differentiation potentials . We sought to understand better the role that REST plays in these distinct contexts by applying our ChIP-chip method to compare REST occupancies in these two cell types . The E14 ESC line and the NS5 NSC line [29] , which was derived from E14 by in vitro differentiation , provided a genetically matched and developmentally linked pair of stem cell types to compare . One major advantage of the ChIP-chip platform is the ability to perform numerous replicate experiments , quickly and affordably , which thereby provides statistically rigorous results . ChIP material was prepared from five independent biological replicates of both ESC and NSC . These experiments identified 810 and 679 RE1 sites that showed statistically significant binding in ESC and NSC , respectively ( Figure 2A ) . There were 647 sites commonly occupied in both cell types , 163 sites occupied only in ESC , and 32 sites occupied only in NSC . We performed qPCR validation on randomly selected RE1s ( Figure 2B ) . The rate of validation was 8/10 ( 80% ) for commonly bound sites ( Figure S4 ) . Seven out of 13 ( 54% ) of ESC-specific RE1s were correctly called by the ChIP-chip ( Figure S5 ) . In contrast , we tested all 32 NSC-only sites and found just one that was NSC-specific: the remainder had either detectable recruitment in ESC or no recruitment in NSC ( Figure 2B and Figure S6 ) . Statistically , this is not unexpected given the small number of NSC-only sites detected ( 32 out of 879 total sites or 4 . 7% ) , and is consistent with an overall FDR of 10% for the ChIP-chip data . In summary , 60% of all RE1 sites on the ChIP-chip were occupied in both ESC and NSC , approximately 7% were occupied in ESC but not NSC , and there were very few , if any , sites occupied only in NSC . We conclude that the RE1 sites occupied in the NSC are a subset of those occupied in ESC . We also used the ChIP-chip method to profile REST occupancy in a differentiated cell type , NIH3T3 fibroblasts ( 3T3 ) ( Figure 2C ) . A total of 606 REST binding sites were detected; of the 27 RE1s reported to be 3T3-specific , we found none that were validated as such by qPCR ( Figure S7 ) . It is interesting that a core set of 519 ( 44% ) RE1 sites was occupied in all three cell types . Hierarchical clustering was performed to group the three cell types based on the overall similarity of their REST recruitment profile ( Figure 2D ) . It is apparent that the occupancy levels at these common sites are unique for each cell type . Overall , the occupancies were greatest in ESC , although many sites displayed the highest degree of occupancy in NSC or 3T3 cells . The clustering determined that the 3T3 and NSC REST binding profiles are more similar to each other than to ESC , suggesting a pluripotency-associated REST recruitment profile . Thus , although there are many REST binding sites commonly occupied , these data reveal a cell-type specific REST occupancy signature . The RE1 ChIP-chip approach described above interrogated high-quality RE1 motifs that had a strong match to the RE1 PSSM . However , recent reports have identified noncanonical RE1-like motifs that can effectively recruit REST in vivo [22 , 23 , 30] . To extend our study to the unbiased identification of REST binding sites , we generated a genome-wide map of REST binding in ESC using ChIP-PET technology , which combines ChIP with deep DNA sequencing [31] . Briefly , a library was prepared from ChIP DNA in a manner that produced paired-end tags ( PETs ) for each DNA fragment . The PETs were sequenced and mapped to the mouse genome to define the chromosomal locations where REST is bound in vivo . This ChIP-PET technique has been used previously to map binding sites for several transcription factors in ESC , including Oct4 and Nanog , and has been shown to be accurate and sensitive [5 , 31] . ChIP-PET mapping of REST in ESC generated 713 , 713 nonredundant PETs that clustered ( i . e . , overlapped at the same genomic location ) into 2 , 460 high-confidence REST binding sites ( Figure 3A ) . A confidence threshold was set at PET clusters of five or more unique and overlapping members ( PET5+ ) based upon ChIP-PCR validation of sets of 20 genomic loci , each randomly selected from clusters of size three to ten ( PET3–PET10 ) ( Figure S8 ) . Seventy-five percent ( 39/52 ) of PET5–PET7 clusters we tested had detectable REST binding by qPCR . Further evidence for the efficacy of the method is demonstrated by the fact that PET5+ clusters overlap 91% ( 649/714 ) of the RE1s identified by ChIP-chip in ESC ( Figure S9 ) . Closer analysis of the 2 , 460 high-confidence clusters found that 665 of the PET sites corresponded to 716 of the predicted RE1 sites ( the numerical differences are due to instances where multiple RE1 sites map within the span of a single PET cluster ) . Thus , there were 1 , 795 additional PET clusters that did not map to computationally predicted RE1 sites . The sequences of the 2 , 460 PET clusters were analyzed for the presence of common motifs that might represent novel REST binding sites , using the Weeder algorithm [32] . Consistent with previous reports [22 , 23] , several classes of DNA sequences related to the known RE1 were discovered in the REST PET sequences ( Figure 3B ) . As expected , many of the binding sites matched closely to the consensus RE1 motif ( 70% ) , albeit with a weaker homology than those sequences included on the RE1 ChIP-chip . Other binding sites were composed of two half sites that matched the RE1 consensus , but with altered spacing between the half sites ( 5% ) . The distance between the half sites was found most often to be shortened to none or one base pair , or expanded by 7–11 base pairs ( Figure 3C ) . In other instances , a left or right half site was found with no corresponding partner ( 5% and 10% , respectively ) , or had the paired half-sites arranged in atypical orientations ( 5% ) . Thus , the RE1 motif can accommodate flexible spacing between its half sites ( Figure 3B ) . However , this new consensus RE1 motif does not account for all the REST binding sites that we mapped by ChIP-PET , since 201 ( 8% ) had no resemblance to an RE1 consensus motif or half site . We found no alternative consensus motif among these atypical sites . Nonetheless , these are true REST binding sites as their recruitment of REST was validated by qPCR ( Figure S10 ) . This recruitment may be direct , through noncanonical REST-DNA interactions , or indirect through recruitment of REST by other DNA-bound factors . We also performed ChIP-PET in NSC; this generated 630 , 849 PETs that cluster into 857 high-confidence REST binding sites ( Figure 3A ) . Unexpectedly , there were about 3-fold fewer high-confidence REST binding sites identified in NSC compared with ESC . This difference cannot be explained by the depth or quality of the sequencing as the total number of sequenced and mapped PETs was similar for the two samples . qPCR validated all of the randomly selected , commonly bound PET clusters we tested ( Figure S11 ) . Thus the ChIP-PET method effectively identified REST binding sites in both ESC and NSC . As before , a de novo motif search was carried out on NSC PET clusters , with similar results as for ESC ( Figure 3B ) ( A comprehensive set of all RE1 motifs identified in ESC and NSC PET clusters can be found in Datasets S2 and S3 ) . These results are in accordance with findings from kidney and lymphoblastoma cells [22 , 23] . REST binding profiles in ESC and NSC , as determined by ChIP-PET , were compared to discover cell-type specific binding sites . Of the 2 , 460 high-confidence sites ( PET5+ ) found in ESC , 1 , 365 were also identified in NSC . Thus , by this comparative PET analysis there were 1 , 095 sites ( 45% ) occupied uniquely in ESC ( Figure 4A ) . Conversely , among the 857 high-confidence sites found in NSC , only ten were not also found in ESC . If the stringency of our cutoffs was raised to PET10+ , then for ESC we found 153 sites ( 19% ) that were not in enriched in NSC , and there were no sites enriched only in NSC . To assess the most appropriate threshold for a comparative analysis , the degrees of PET overlaps from the two libraries were cumulatively assessed at each cutoff from PET2+ to PET10+ ( Figure 4B ) . For a threshold above PET5+ in the NSC , there was complete overlap with ESC PETs . The converse was not true: even at the most stringent cutoff , PET10+ , only 80% of ESC PET10+ sites have an equivalent in NSC . qPCR carried out on randomly selected sites confirmed the common ( 19/19 , 100%; Figure S11 ) and ESC-specific binding sites ( 13/20 , 65%; Figure S12 ) . Thus , the ChIP-PET results were consistent with the results obtained in our ChIP-chip experiments , which indicated that up to 30% of REST binding sites are selectively bound in ESC relative to NSC and that there were few , if any , sites exclusively bound in NSC . To verify the pluripotency-specific nature of the ESC-specific PETs , we compared our data with a previous whole-genome study of REST binding in mouse kidney cells [22] ( Figure 4C ) . Consistent with our hypothesis , we observed a robust and statistically significant lack of REST binding at ESC-specific loci in kidney cells . These ESC-specific PETs are unlikely to be some kind of statistical noise , since the majority contain full-length RE1 motifs , albeit at a lower rate than the commonly bound PET set ( Figure 4C ) . The ChIP results indicated that there were highly overlapping and yet distinct patterns of REST occupancy in ESC and NSC , two cell types that have unique developmental potential . REST is known to be a repressor of neuronal gene expression in non-neuronal cells and RE1 sites preferentially map to neuronally expressed genes [18 , 19 , 33] . Our data show that there are substantially more sites that bind REST than previously predicted , so we asked what the nearest potential target genes are among the expanded repertoire of binding sites . Full lists of target genes can be found in Dataset S4 . The expanded number of binding sites for REST in ESC led us to ask whether REST controls an ESC-specific repertoire of target genes . To test this , we compared the ESC-specific and ESC-NSC common target sets by gene ontology analysis [34] . Robust statistical filtering yielded several gene ontology terms that were significantly different in their association with the two gene sets . Gene categories relating to neuronal function and development were depleted among the ESC-specific set compared with the common genes , although it is important to note that such terms remain highly enriched in the ESC-specific set when compared with the set of all genes . In contrast , a number of ontology categories were significantly enriched in the ESC-specific set , even following Bonferroni correction; among these were genes mediating the Wnt signaling pathway , in addition to integrins , kinases and chromatin binding proteins ( Figure 4D; see Dataset S6 ) . In addition to differential gene targeting , ESC-specific and ESC-NSC common PET clusters have distinct sequence properties: the former tend to exhibit weaker sequence conservation ( Figure 4E ) . Common ESC/NSC clusters have higher quality RE1 motifs , as measured by their RE1 PSSM score ( Figure 4F ) , resulting in elevated levels of in vivo REST recruitment ( Figure 4G ) . Our mapping of REST binding sites in ESC and NSC indicated that there were distinct patterns of occupancy in the two cell types . We next asked which genes are transcriptionally repressed by REST in these cells . To this end , we profiled gene expression in ESC and NSC in which the activity of REST was blocked by a dominant-negative form of REST ( DN:REST ) . DN:REST comprises the eight zinc fingers of the REST DNA binding domain , but lacks the N and C termini; it thus derepresses transcription of REST target genes [28] . An adenovirus was used to efficiently deliver DN:REST to the NSC . After 48 h of REST derepression , global changes in gene expression were measured by DNA microarray analysis . We detected expression of ∼21 , 000 genes , of which 911 genes were significantly altered ( p < 0 . 01 ) in NSC in the presence of DN:REST ( Dataset S5 ) . Overall , 635 ( 3 . 0% ) and 276 ( 1 . 3% ) of the expressed genes showed statistically significant up- and down-regulation , respectively ( Figure 5A ) . Given that REST is a transcriptional repressor , it is anticipated that , in response to the inhibition by DN:REST , direct target genes would be up-regulated . Thus , down-regulated genes are likely to be downstream , indirect targets of REST . We asked whether the most differentially expressed genes had evidence for genomic occupancy by REST . The gene nearest to each REST binding site was identified . There were 33 genes with expression elevated 3-fold or greater , of which 24 ( 73% ) had an associated binding site ( Figure 5C ) . Given that this number is far lower than the total number of expressed genes associated with a REST PET cluster , these data indicated that only a small proportion of bound genes are actually derepressed by DN:REST in NSC . However , among the derepressed genes , those that were most responsive to REST knock-down did have an associated REST binding site . We also investigated the transcriptional response of ESC to DN:REST over-expression . Unlike NSC , ESC cannot be infected by adenovirus , so instead they were transfected with a DN:REST construct and enriched by fluorescently activated cell sorting ( FACS ) to select for those strongly expressing DN:REST . After 48 h of DN:REST expression , gene expression profiling was carried out on these cells , showing that 441 genes were significantly differentially expressed in response to DN:REST: 395 ( 1 . 9% ) were down-regulated , but only 46 ( 0 . 22% ) were up-regulated ( Figure 5B ) . The 20 most up-regulated genes ( ≥3-fold ) show a strong bias for direct recruitment of REST: 40% are associated with a REST PET cluster ( Figure 5D ) . The distinct methodologies we used for DN:REST delivery preclude a rigorous comparison of transcriptional response in ESC and NSC . It is possible that different levels of DN:REST expression and temporal induction of expression lead to differential gene responses in the two cell systems . Of the 441 and 911 genes that responded to DN:REST in ESC and NSC , respectively , only 17 ( 1 . 3% ) were similarly altered in both experiments , of which 11 were associated with a REST PET cluster . This was rather unexpected given that there was such a high concordance ( 80% ) of REST sites occupied in ESC and NSC . Among the genes commonly elevated in ESC and NSC were Celsr3 , Snap25 , and Unc13a , which were occupied by REST and among the most highly induced in both cell types . Unc13a encodes a synaptic vesicle protein and was not a computationally predicted target of REST: its REST binding site , which we identified 53 bp upstream of the transcriptional start sites ( TSS ) , does not closely match the canonical RE1 motif , though it does match the RE1 PSSM when similarity constraints are relaxed . Tandem canonical RE1 motifs were mapped in close proximity ( <1 kb ) to Snap25 , a regulator of neural transmitter release [20] , and Cels3r , a G-protein-coupled receptor , which plays a role in neuronal development . All the other most responsive genes had either a canonical or noncanonical RE1 motif . It was somewhat surprising that the vast majority of the genes with an associated REST binding site were not derepressed by DN:REST . We noted that the most up-regulated genes have sites in very close proximity to their TSS , often within 1 kb ( Figure 5C and 5D ) . To investigate this further , we plotted the fold change in expression for each gene versus the distance of that gene to the nearest mapped RE1 site ( Figure S13 ) . This analysis clearly indicated a strong bias for the most differentially expressed genes to have an RE1 site proximal to its TSS . It is noteworthy that this bias for RE1 sites was associated only with the genes up-regulated , but not those down-regulated , by DN:REST , which is consistent with the expectation that REST acts as a repressor ( Figure 6 ) . As expected , many of the genes that contain REST binding sites in NSC and showed elevated expression in response to DN:REST encode proteins that have neuronal functions , such as: neurotransmitter receptor subunits Chrnb2 , Gria2 , Gabrb3; neuronal adhesion-associated molecules Ina , Cspg3 , Nxph1 , Mmp24; and molecules associated with secretory functions Chrnb2 , Scg2 , Trim9 , Trim67 , Cplx2 . This was expected as the NSC are poised to differentiate exclusively toward the neural lineage . In ESC , DN:REST also induced genes linked to neuronal function , in particular , synaptic vesicle biology: Syt4 , Snap25 , Unc13a , Cplx1 , and Chga . These genes are also associated with neuroendocrine secretion , perhaps indicating a core requirement in both ESCs and NSCs to ensure active repression of gene products involved in vesicular secretion . REST had been implicated previously in the transcriptional regulatory networks that regulate ESC pluripotency , as the Rest gene is a target of Oct4 , Sox2 , and Nanog binding [2 , 5] . We explored more fully the connection between REST and the pluripotency transcription factors Oct4 , Sox2 , and Nanog . Recently our colleagues completed a detailed mapping of binding sites for Oct4 , Sox2 , and Nanog in ESC [35] . Very deep sequencing of ChIP DNA from ESC identified 1 , 834 , 1 , 765 , and 3 , 317 genes with binding sites for Oct4 , Sox2 , and Nanog , respectively , either within the gene itself or not more than 10 kb upstream of the target gene . These data confirm and extend earlier studies [2] in which these three transcription factors represent a core regulatory complex in ESC . By comparing the list of REST target genes with those of Nanog , Oct4 , and Sox2 , we found a statistically significant integration of target gene repertoires ( Figure 7A ) . Of the 1 , 287 REST target genes , 270 ( 21% ) , 238 ( 18% ) , and 399 ( 31% ) of these were also targets of Oct4 , Sox2 , and Nanog , respectively . In addition , 107 genes were targets of all four factors in ESC , including Rest itself ( having three REST PET clusters ) , and several transcription factors implicated in ESC self renewal , such as Nanog [36 , 37] and Zfp206 [5 , 23] ( Figure 7B ) . We also noted that the gene of the reprogramming factor Lin28 [38] contains a REST PET cluster in the proximal upstream region ( Figure 7B ) . Using conventional ChIP-qPCR , we validated REST recruitment to Nanog , Zfp206 , Zfp281 , Esrrb , and Lin28 , in addition to the Rest gene itself ( Figure 7C ) . Furthermore , expression of Zfp206 , Zfp281 , and Lin28 was induced by DN:REST ( Figure S14 ) . Surprisingly , we could find no evidence for recruitment of REST to the gene for the microRNA mir-21 , as was reported recently [39] ( Figure 7C ) . Oct4 , Sox2 , and Nanog have been shown to form an autoregulatory circuit where every factor regulates its own gene and that of the other two [5 , 40] . Our mapping data indicate that REST also forms such an autoregulatory circuit . These results support the hypothesis that REST is a component of the pluripotency network that includes Oct4 , Sox2 , and Nanog , which together control differentiation and pluripotency in ESC .
A central aim of biology is to reconstruct the transcriptional circuitries governing cell identity and differentiation during embryonic development . ESC and NSC lines have emerged as tractable and meaningful in vitro models in which to use high-throughput genomic techniques to map such circuitries . Given the wholesale and rapid changes in transcriptional activity that accompany differentiation , it is imperative to understand how transcriptional regulatory networks change during development . An obvious model , therefore , is a transcriptional regulator such as REST with important roles in multiple , related cell types such as ESC and NSC . In the present study , we mapped REST regulatory targets in ESC and NSC by complementary microarray and sequencing methods . We have shown that REST recruitment has dual components , consisting of an apparently cell-type–independent core population of binding sites in addition to a substantial pluripotency-associated set found only in ESC . The diversity of the REST regulatory network was even greater when analyzed on the transcriptional level , where we observed almost complete discordance in gene regulation in ESC and NSC . We have also expanded our understanding of REST's role in ESC by showing that it shares a substantial set of target genes , including Rest itself and Nanog , with Oct4 , Sox2 and Nanog , the core pluripotency transcription factors . In this genome-wide , comparative study , we found evidence for diversity in the REST recruitment profile between cells of various differentiation capabilities . The three-way comparison of recruitment in ESC , NSC , and fibroblasts demonstrated a substantial commonality in REST recruitment , in addition to a large minority of ESC-specific binding . However , even among the commonly bound loci , we observed large variation in the level of REST recruitment . Hierarchical clustering carried out on the data confirmed that , for this set of three cell types , the REST binding profile is most strongly influenced by pluripotency . In contrast , we could find no evidence for specific binding sites in either NSC or fibroblasts ( Figures 2 and 4 ) , suggesting that the REST recruitment profile is most extensive in ESC and decreases in line with loss of developmental potential . These findings suggest that the unique genomic and chromatin organization of ESC [1] is also reflected in the recruitment profile of generic transcription factors such as REST . What is the mechanistic basis for this promiscuous recruitment in pluripotent cells ? One possibility is that weaker RE1 motifs ( with lower affinity ) are only bound under the higher REST concentrations found in ESC [13] . This is supported by the fact that most of the ESC-specific sites have more degenerate RE1 motifs ( Figure 4F ) . However , this interpretation may be overly simplistic: among the commonly bound sites , many were occupied to a much greater extent in NSC than in ESC ( Figure 2 ) . Furthermore , contrary to previous reports [13] , we did not observe any difference in the total levels of REST protein in ESC and NSC ( unpublished data ) . These data suggest that the difference in overall patterns of REST occupancy is not simply a consequence of REST protein concentrations . Given the profoundly different chromatin architecture observed in ESC [1 , 41] , it is likely that many chromatin domains that are accessible to soluble factors in ESC become inaccessible at subsequent stages of differentiation . Another related possibility is that nucleosome positioning around RE1s is instructive for REST recruitment and serves to exclude REST from many sites upon loss of differentiation . Our recent demonstration that REST recruitment relies upon the ability of its cofactor , Brg1 , to remodel local chromatin in an acetylation-dependent manner [42] lends weight to these arguments . Finally , other transcription factors may serve to recruit REST to weaker RE1s in an ESC-specific manner . This effect has previously been observed for the nuclear hormone receptor , ERα [43] . We are currently investigating all these possibilities by a combination of techniques used in this paper . Transcription factor–target gene relationships have generally been inferred from ChIP evidence alone . In the present study , we avoided such assumptions by assimilating gene expression data with our PET analysis of REST recruitment . Specifically , we surveyed the functional response of all known genes to inhibition of REST by a dominant-negative construct . At the level of sensitivity of our assay at least , we found that only a small minority of detectable genes to which REST is recruited actually respond to its removal . This confirms , on a genome-wide level , previous observations that the removal of REST is often not sufficient for target gene derepression [12–14 , 44] . It is likely that , in such circumstances , gene activation requires the presence of particular activating transcription factors , or that repression by additional , REST-independent mechanisms must also be removed . Gene response is , however , strongly influenced by the relative location of REST binding sites to the TSS; specifically , TSS-proximal binding sites strongly repress gene transcription , from either upstream or downstream , and that the potency of this regulation drops rapidly within 2–3 kb ( Figure S13 ) . These data raise important new questions over the precise mechanisms governing gene regulation by REST . What factors determine whether a bound gene will respond to REST ? Given the heterogeneity of responsiveness , we suggest that complex subnuclear organization determines which REST-bound loci have access to appropriate corepressor complexes , and therefore which genes are repressed . The data also lead us to question why the majority of high-quality RE1 motifs are at non-promoter loci [18] if they are broadly incapable of regulating gene transcription . In any case , these findings force us to consider that genome-wide mapping projects alone are insufficient for meaningful reconstruction of gene regulatory networks without accompanying functional data on gene expression . To date , genomic surveys of REST target genes ( including this study ) have demonstrated their significant enrichment for genes relating to nervous system development and function [18–20 , 23] . Therefore , we were surprised to find that among ESC-specific REST targets , there are a large and significant number of genes encoding members of the Wnt signaling pathway ( Figure 4D and Dataset S6 ) . Wnt , a crucial determinant of both pluripotency and mesendodermal fate in ESC , is tightly controlled by both activating and repressive mechanisms in ESC [31 , 45 , 46] . Repression of Wnt and Wnt receptor genes is thus an important candidate mechanism by which REST maintains the pluripotent state . Repression of Wnt signaling by REST may also contribute to tumor suppression . REST was identified as a suppressor of human epithelial cell transformation and REST is frequently deleted in colorectal tumors [47] . Deregulated Wnt signaling is a frequent event in the genesis of many tumor types and plays an important role in the proper maintenance of the stem cell niche of the colonic epithelium [48] . In light of the connection we have established between REST and Wnt , it is possible that REST plays an important role in regulating Wnt signaling and that loss of REST function leads to tumor initiation . It will be interesting to determine whether REST is expressed in tissue stem cell populations and , if so , whether it regulates expression of Wnt pathway components . It currently remains unclear to what degree REST is responsible for regulating the Wnt pathway . It should be noted that the Wnt pathway genes were not among those derepressed by DN:REST in ESC . It seems likely that tight regulation of the Wnt pathway is critical and would , thus , be mediated by many competing , and reinforcing circuits that converge on this node in the transcriptional network . In addition to the Wnt pathway , we found other strong evidence that REST is an important controller of pluripotency in ESC . A highly significant number of genes ( 200–400 ) are commonly targeted by REST and the pluripotency factors Oct4 , Sox2 , and Nanog ( Figure 7 ) ; 107 genes are targets of all four factors . Many of these genes encode transcription factors , including Znf206 , Esrrb , and Nanog , which have all been implicated in pluripotency maintenance . The reprogramming factor Lin28 is bound by REST and Nanog . The Rest gene itself is a common target of all four factors; thus , REST autoregulation would appear to be an evolutionarily conserved property , given its previous observation in human [49] and now in mouse . In response to recruitment of REST , we showed that Zfp206 , Zfp281 , and Lin28 are transcriptionally repressed . Surprisingly , other pluripotency genes did not show changes in expression at the level of sensitivity of our assay . These data suggest that REST does repress the pluripotent phenotype at the level of transcription , but only weakly in cultured ESC . It is clear , however , that REST does prevent expression of many neural genes in ESC . We hypothesize that REST may be capable of repressing pluripotency genes such as Nanog at distinct developmental time points ( which may not be faithfully represented by the ESC model ) , perhaps depending on the availability of correct corepressor molecules , which are known to change dynamically during ESC differentiation [5] . In this way , REST may be both a pro-pluripotency gene—by repressing neural phenotype in ESC—and an anti-pluripotency gene—by repressing pluripotency during subsequent differentiation . Finally , we could find no evidence for recruitment of REST to mir-21 in mouse ESC , as reported recently [39] ( Figure 7C and Figure S15 ) . Furthermore , mir-21 levels were unaffected by REST in our previous study [50] , suggesting that the reported regulation of mir-21 may in fact be an indirect effect . Together , our results suggest a model in which the intersection of activating ( Oct4 , Sox2 , Nanog ) and repressive ( REST ) transcriptional signals control ESC pluripotency ( Figure 8 ) . The outcome of these opposing forces is expression levels of a large number of genes that are appropriate for the pluripotent state . In addition to potentially antagonizing pluripotency by binding genes such as Nanog and Zfp206 , REST also appears to promote pluripotency through repression of multiple components of the Wnt pathway . The regulatory relationships suggested by our whole-genome mapping study will need to be functionally confirmed in future by knock-down of REST , which will lead to derepression of target gene expression; furthermore , it is possible that such regulation takes place only during stages of differentiation subsequent to that represented by the ESC as discussed above . Regardless of such details , however , our findings show that REST has a complex role in both promoting and antagonizing the pluripotent state .
E14 cells ( American Type Culture Collection ) were cultured feeder-free as described [51] . NS5 neural stem cells were grown as described in [29] . NIH3T3 fibroblasts were cultured in Dulbecco's Modified Eagle's Medium supplemented with 10% fetal calf serum at 37 °C in 5% CO2 . Details of ESC and NSC differentiation and immunohistochemistry can be found in the Text S1 . ChIP was performed according to the Hinxton protocol [23] . Briefly , sonicated , cross-linked chromatin from 2 × 107 cells was immunoprecipitated ( IP ) using 10 μg anti-REST antibody ( Upstate 07–579 ) . Immune complexes were collected using 50 μl of a 50% ( v/v ) slurry of BSA-blocked Protein G Sepharose . The same amount of nonspecific rabbit IgG was used in control IPs to gauge background , and non-IP Input DNA samples were also prepared for reference ChIP-chip hybridizations . The RE1 ChIP-chip design was based on 1 , 319 RE1s from the mouse genome that had a PSSM score >0 . 90 [18] . Centered on each RE1 , a 200-bp window was searched for appropriate 50mer hybridization probes with the following criteria: ( 1 ) 40–60% GC content; ( 2 ) no secondary structure; ( 3 ) ≥15 nt difference between all 50mer probes; ( 4 ) contiguous match between probes of ≤25 nt . 1 , 095 RE1s satisfied these criteria . Additionally , 92 negative control non-RE1 probes and two sets of tiled RE1-bearing promoters ( Nppa and Syt4 ) were included in the design . Amine-conjugated DNA probes were synthesized and printed in duplicate onto Codelink Activated Slides ( GE Healthcare ) . Non-IP DNA ( Input , 250 ng ) and 46 μl ChIP DNA were amplified following the manufacturer's instructions ( Bioprime ( Exo- ) kit , Invitrogen ) . Purified , amplified DNA ( 1 μg ) was then labeled with Cy3 or Cy5 using a ULS arrayCGH labeling kit ( Kreatech Biotechnology ) . Corresponding labeled DNAs were subsequently pooled , mixed with 90 μg mouse CoT-1 DNA ( Invitrogen ) and concentrated with Microcon YM-30 columns ( Millipore ) to a volume of 5 μl . The resultant concentrates were each mixed with 2 μl Kreablock ( Kreatech Biotechnology ) , 80 μg yeast tRNA , 40 μg herring sperm DNA , 19 μl DIG Easy Hyb Buffer ( Roche ) . The final 38-μl hybridization mixes were incubated for 15 min at 70 °C followed by 45 min at 37 °C , then hybridized to the microarrays ( which were pre-hybridized with 60 μl DIG Easy Hyb Buffer at 42 °C for 1 h ) . Hybridizations were performed with a MAUI hybridization station ( BioMicro Systems ) at 42 °C for 20 h . Microarrays were washed ( 2x SSC+0 . 01% SDS at room temperature ( RT ) 5 min , 1x SSC at RT 5 min , 0 . 6x SSC at 60 °C 5 min , 0 . 2x SSC at RT 5 min ) , then scanned and imaged using a GenePix 4000B Scanner and software ( Axon ) . We used a Gaussian Mixture Model to analyze raw ChIP-chip intensity data using the R package mclust [52]: the data are fitted to a finite number of Gaussian curves , each with a distinct mean and variance . See Text S1 for additional details . A total of ∼200 ng REST ChIP DNA , sheared to an average size of ∼750 bp , was used for the construction of each ChIP-PET library , essentially as described [31] and sequenced on a 454 Sequencer . We previously constructed a database of potential RE1 sites in the whole genome [18] . Comparing REST PET5+ clusters with this database identified 1 , 351 clusters in ESC that contained a candidate RE1 motif ( Seqscan PSSM score >0 . 83 ) . For the remaining 1 , 109 high-confidence clusters , we extracted 200 bp of flanking sequence and submitted them to the de novo motif-finding algorithms MEME [53] and Weeder [53] . These programs identified a degenerate RE1 motif consisting of positions 7–17 of the full-length motif , as well as the left ( positions 1–9 ) and ( positions 12–21 ) right half-sites of the canonical RE1 motif . This suggested that there were still weak canonical RE1 motifs present in the remaining clusters , as well as individual half-site motifs . To investigate this we scanned the 1 , 109 clusters using the full-length RE1 PSSM with a relaxed stringency threshold , as well as with PSSMs representing each of the left and right half-sites alone , and with both left and right PSSMs together ( of E < 0 . 0001 , using the technique described in [5] ) . For PET clusters containing both left and right motifs , we compiled their orientation as well as the distance separating the two motifs . Gene ontology analysis was carried out using the online package available at http://www . pantherdb . org [34] . Bonferroni-corrected p-values are shown as calculated by Panther based on binomial statistics . The DN:REST construct [14] , consisting of the REST DNA-binding domain alone , was cloned into the pCAG vector ( Invitrogen ) with a FLAG tag at the N terminus . 1 μg of pCAG_DN:REST ( or empty pCAG vector ) DNA was transfected with 2 . 5 μl Lipofectamine ( Invitrogen ) into E14 . Transfection efficiency was 60–80% , as determined by the internal ribosomal entry site-driven green fluorescent protein ( GFP ) fluorescence . GFP-expressing cells were sorted by FACS and robust DN:REST expression was detected with the FLAG-antibody ( Sigma-Aldrich ) . We used a recombinant adenovirus expressing DN:REST [14] for NS5 cells . The infection rate was 90–100% , as judged by GFP fluorescence . RNA was harvested after 48 h of DN:REST expression . Total RNA was extracted from at least three biological replicates each of control cells and DN:REST-transfected ( or infected ) cells . RNA was labeled using a TotalPrep RNA Amplification kit ( Ambion ) and hybridized on Sentrix Mouse Ref-6 Expression BeadChip microarrays ( Illumina ) ( see Text S1 for details ) .
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Embryonic stem cells have the unique and defining property of pluripotency: the ability to differentiate into all cell types . Key transcription factors form interconnected gene regulatory networks that control pluripotency and differentiation . Recently , the transcriptional repressor RE1-silencing transcription factor ( REST ) was implicated in the maintenance of pluripotency . This was surprising , given that REST has long been known as an essential regulator of neurodevelopment . How does REST regulate pluripotency ? Does REST have distinct cohorts of binding sites and target genes in different developmental contexts ? To address these questions , we made whole-genome maps of REST binding sites in two mouse stem cell types: embryonic ( ESC ) and neural ( NSC ) stem cells . These data were compared with each other and with gene expression data from cells in which REST activity was inhibited . The target genes were almost completely distinct in the two cell types . Surprisingly , we found that REST recruitment has two approximately equal components: common sites across all cells and an ESC-specific component . These pluripotency-associated sites are enriched for particular classes of genes , including those mediating the Wnt signaling pathway , which is an essential regulator of pluripotency .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"developmental",
"biology",
"neuroscience",
"molecular",
"biology",
"genetics",
"and",
"genomics"
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2008
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REST Regulates Distinct Transcriptional Networks in Embryonic and Neural Stem Cells
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Genetic analysis of pathogenic organisms is a useful tool for linking human cases together and/or to potential environmental sources . The resulting data can also provide information on evolutionary patterns within a targeted species and phenotypic traits . However , the instruments often used to generate genotyping data , such as single nucleotide polymorphisms ( SNPs ) , can be expensive and sometimes require advanced technologies to implement . This places many genotyping tools out of reach for laboratories that do not specialize in genetic studies and/or lack the requisite financial and technological resources . To address this issue , we developed a low cost and low tech genotyping system , termed agarose-MAMA , which combines traditional PCR and agarose gel electrophoresis to target phylogenetically informative SNPs . To demonstrate the utility of this approach for generating genotype data in a resource-constrained area ( Madagascar ) , we designed an agarose-MAMA system targeting previously characterized SNPs within Yersinia pestis , the causative agent of plague . We then used this system to genetically type pathogenic strains of Y . pestis in a Malagasy laboratory not specialized in genetic studies , the Institut Pasteur de Madagascar ( IPM ) . We conducted rigorous assay performance validations to assess potential variation introduced by differing research facilities , reagents , and personnel and found no difference in SNP genotyping results . These agarose-MAMA PCR assays are currently employed as an investigative tool at IPM , providing Malagasy researchers a means to improve the value of their plague epidemiological investigations by linking outbreaks to potential sources through genetic characterization of isolates and to improve understanding of disease ecology that may contribute to a long-term control effort . The success of our study demonstrates that the SNP-based genotyping capacity of laboratories in developing countries can be expanded with manageable financial cost for resource constraint laboratories . This is a practical formula that reduces resource-driven limitations to genetic research and promises to advance global collective knowledge of infectious diseases emanating from resource limited regions of the world .
Single nucleotide polymorphisms ( SNPs ) are highly valuable genetic markers that have advanced our knowledge of diverse biological fields such as human health [1 , 2] , infectious disease epidemiology [3–5] , agriculture [6] , and ecology [7] , among others . In the study of infectious diseases SNPs can be informative of bacterial phenotype , such as antibiotic susceptibility [8 , 9] , and also can be used to classify unknown strains . For non-recombining bacterial pathogens , most of their SNPs become fixed in the genome and are faithfully replicated throughout future generations [3 , 10] . These stable signatures can be used to classify unknown strains into known phylogenetic groups according to SNP profiles [3 , 11 , 12] . Within the context of epidemiological investigations , these SNP profiles can link isolates from active outbreak sites to possible sources and help track disease transmission patterns [5 , 13 , 14] . Genotyping assays that use real-time PCR to identify single SNPs remain in demand despite the wide-scale availability of whole genome sequence ( WGS ) data and continued reductions in WGS costs . For many research facilities that are interested in small-scale studies or face resource limitations , a WGS-based approach to SNP typing is not a feasible nor a desirable option . A variety of other technological platforms have been employed for SNP typing and have been extensively described in several publications [15–18] . Popular platforms for SNP typing use real-time PCR instruments that employ Dual Probe TaqMan assays or melt-MAMA SNP assays [19–21] . But real-time platforms are not commonly available in resource constrained laboratories , due to their high upfront costs and the need for ongoing highly technical instrument maintenance . However , a more simplified method for SNP genotyping that employs conventional PCR coupled with standard agarose gel electrophoresis ( agarose-MAMA ) is a viable alternative in these settings . The advantage of this alternative method is that it utilizes relatively inexpensive instruments that are almost universally available even in developing nations where it is used for a variety of molecular applications . Much of this is due to the simplicity of the agarose gel electrophoresis platform , in contrast to the complex instrumentation of the real-time platform [20 , 22] . To illustrate the effectiveness of agarose-MAMA as a SNP genotyping tool in resource constrained laboratories , we developed Y . pestis assays for use at the Institut Pasteur de Madagascar ( IPM ) . Y . pestis is the bacterium infamously known as the causal agent of the disease plague . Y . pestis is ecologically established on nearly every inhabited continent [12 , 23] and remains a particularly significant threat to human health in developing nations in Africa and especially the island country of Madagascar [24 , 25] . Primarily a zoonotic agent , Y . pestis has a complex ecological cycle involving rodent-host populations and flea vectors . In unfortunate circumstances , humans are incidental hosts [26] . Without prompt antibiotic treatment , human death rates vary from 30–60% to 100% depending on the route of exposure , with pneumonic plague being the most deadly [24 , 25] . Within the last two decades , Madagascar has reported some of the highest incidences of human plague infections throughout the world [24] . Given that Y . pestis is a re-emerging public health threat in Madagascar [27–29] and other developing African nations , performing SNP typing studies through the use of agarose-MAMA tools could lead to greatly improved epidemiological investigations and to more effective disease management . Here , we describe how we resolved technological limitations that prevented Y . pestis SNP genotyping studies at IPM by: 1 ) re-designing a SNP-based Y . pestis genotyping system ( from melt-MAMA real-time platform to agarose-MAMA ) to be compatible with existing resources in Madagascar , and 2 ) validating these genetic tools at IPM for adoption . Collectively , these achievements have removed a research barrier at IPM once imposed by technological disparities and serve as a promising model for building similar research capacities in other developing nations affected by plague and/or other pathogens common to low-resource settings . In addition , these tools generate data that can be compared to other sequence-based methods and can feed into existing global databases . This ability can strengthen scientific exchange between affected countries and the international scientific community , thereby advancing the global collective knowledge of dangerous infectious diseases .
We present a SNP genotyping tool that uses standard genetic equipment and reagents that are accessible to nearly any laboratory . The successful development of agarose-MAMA tools removes the dependence on real-time instruments for conducting SNP-based epidemiological studies . Due to emerging infectious diseases being most globally prevalent in resource challenged countries [30 , 31] , building research capacities in these regions has been a goal for the World Health Organization and other agencies charged with global biosafety , response , and biosecurity efforts [32 , 33] . It is now possible for researchers in resource constrained locations to conduct SNP studies to obtain bacterial phenotype information such as antibiotic resistance or phylogenetic information to more robustly understand the dynamics of disease transmission and potentially identify sources of human infections . The phylogeny of Y . pestis in Madagascar has recently expanded [34] to include more phylogenetic groups ( Fig 1 ) and the 18 assays developed for this study define a subset of these phylogenetic groups or lineages as illustrated on a simplified phylogeny ( Fig 1 ) . The genotyping results generated from each agarose-MAMA was identical to independent SNP genotyping technologies [34] when tested across the same diverse panel of 16 Y . pestis DNA strains . Assay specificity remained intact when these agarose-MAMAs were tested on diverse types of negative controls ( high levels of human , Leptospira spp . , and Bacillus anthracis DNA , or a no template water control ) . When the assays were employed in a stepwise , hierarchical order ( Fig 2 ) , each isolate could be assigned to a single lineage or one subgroup as depicted in the simplified Malagasy Y . pestis phylogeny ( Fig 1 ) . The equivalent performance of our agarose-MAMA tools to melt-MAMAs [11] and other independent SNP technologies [34] demonstrate that MAMA tools preserve their genotyping accuracy independent of the technology platform used . For the 18 selected SNPs , we were able to successfully design agarose-MAMAs to achieve 100% genotyping accuracy through a few key optimization steps . Only one of our assays failed initial optimization efforts; however , when we redesigned the assay to target the reverse complement of the reference template DNA ( strain CO92 ) , the assay was rescued to full functionality . Essential validation steps included the identification of the optimal ratios for the concentration of the two forward primers ( ancestral:derived ) as described [20] , ideal annealing temperature for each assay , appropriate number of PCR cycles , and an occasional need for MgCl2 concentration alteration in the reaction mix . These optimization strategies worked well on 8 other bacterial pathogens [20] and should be applicable to many other pathogenic organisms as well . The GC-clamp ( 21–28 oligo base ) , added to derived MAMA forward primers but excluded from ancestral MAMA forward primers , was sufficient to provide visible size differences between the two allelic states when visualized on an agarose gel ( Fig 3 ) . A repeated pattern of 5’-cgggttcgggttcgggttcgggttcggg-3’did not appear to induce non-specific binding nor interact with template DNA in an inhibitory manner . However , we observed that the GC-clamp on the derived MAMA primer did frequently confer a competitive advantage over the ancestral MAMA , as previously described for MAMA tools [20] . This competitive advantage resulted in cross-hybridization of primers for a subset of assays . This phenomenon may be based on the ability for GC rich DNA regions to anneal at lower temperatures compared to GC poor regions . The GC-clamp of the derived MAMA primer likely anneals to its target amplicon at lower temperatures compared to the no-clamp ancestral primer . As a consequence , the GC-clamp primer anneals to the template at an earlier time point per PCR cycle than the no-clamp primer , resulting in a competitive kinetic advantage . To correct for this competitive advantage , we followed published guidelines [20] by increasing the ancestral primer concentration relative to the derived primer concentration of the affected assays . In these skewed reaction mixes , the ancestral primer had >1x-4x concentration compared to its paired derived primer . We have detected best results with 4:1 and 2:1 ( ancestral:derived ) primer ratios , depending on the assay . We obtained maximal assay specificity by a combination of customizing the annealing temperature per assay and/or adjusting the MgCl2 concentrations . Most of our assays accurately genotyped SNP alleles at annealing temperatures around 60°C; however , some assays had non-specific product at this condition . For these assays , non-specific PCR products were present in our negative controls and/or in our sample PCR product , visualized as extra banding on a gel . A reduction of MgCl2 concentration by 0 . 5 mM was sufficient to greatly increase the specificity of most assays , as evidenced by the elimination of non-specific amplification . But this reduction in MgCl2 decreased the PCR robustness in a subset of assays . To address this loss , the annealing temperatures were reduced in affected assays while still preserving SNP specificity . For the few assays that did not respond to lowered MgCl2 strategy , the raising of annealing temperatures while still maintaining the normal 2 . 0 mM concentration of MgCl2 yielded improvements in reducing non-specific products . The assays described here are capable of genotyping directly from complex clinical samples if pathogen DNA levels are sufficient ( Fig 4 ) . This capability is highly important as nearly 43 . 32% of 775 F1 RDT [36] positive human plague cases between 2011–2015 in Madagascar and many environmental samples ( rodents and fleas ) do not yield live isolate culture ( IPM records ) . Using two agarose MAMA tools ( Mad-05 and Mad-43 ) to demonstrate proof of principal , we were able to determine that the three complex clinical samples belonged to group I lineage and not group II . This genetic assignment for these samples matched the results of a recent publication [34] . These samples were not further tested on additional agarose-MAMAs . Out of five complex clinical samples positive for the high copy plasmid pla gene [37] only four were positive for Y . pestis chromosomal DNA , assessed by a TaqMan assay targeting the 3a gene ( Fig 4 and S3 Appendix ) . Of the four 3a-positive samples , one isolate ( Yp3182 ) gave a late amplification with the 3a assay resulting in a Ct value of 36 using real-time PCR . This indicates a very low concentration amount for this clinical extract , near a single copy of pathogen DNA [20 , 21] . The other three samples ( Yp2483 , Yp2486 , Yp2485 ) ( Fig 4 ) generated Ct values ranging from 24–27 , which indicates higher concentrations of Y . pestis chromosomal DNA in these clinical samples . These more concentrated Y . pestis clinical samples were successfully genotyped by agarose-MAMA tools following an increase in the number of PCR cycles . The low-level sample ( Yp3182 ) failed to amplify PCR product using agarose-MAMA tools . Together , these results indicate that agarose-MAMAs can genotype directly from complex clinical samples if the pathogen target ( Y . pestis DNA ) is of sufficient concentration . Published studies show that TaqMan assays are highly sensitive [20 , 21] and can readily detect minute amount of template not detectable using melt-MAMAs [20] . Our results suggest that the same is true for agarose-MAMAs ( Fig 4 ) . Following the transfer of our agarose-MAMA tools to the IPM facility , a subset of our agarose-MAMAs was validated for genotyping accuracy . The differing instruments and reagents used at IPM introduced very little variability on assay performance when tested on the same DNA panel previously used at NAU ( Fig 5 ) . The optimized PCR conditions identified at NAU for the assays were suitable for most of the assays when conducted at IPM . The most apparent difference in assay performance between the two laboratories was an increase in non-specific amplification at high fragment size ( Fig 5 ) and , for some assays , also in our negative controls at IPM , which was not observed at NAU . These non-specific fragments did not affect the MAMA’s capability to accurately genotype isolates . Band profiles were not distinctly in line with either the ancestral or derived product but rather appeared either as a smear across both profiles or as a much fainter band falling between the ancestral and derived fragment sizes . We suspect that the basis of this performance difference is due to different Taq polymerases used between the two institutions . NAU employed an antibody-immobilized Taq polymerase ( Invitrogen , Carlsbad , CA ) , which has been characterized as having no polymerase activity prior to a hot-start step in the thermal cycle protocol [38] . However , IPM utilizes regular Taq polymerase , which may begin product synthesis with primers and template in the master mix prior to PCR thermal cycling [38] . Although master mix preparation was done on ice to suppress premature Taq polymerase activity , additional bands above the target PCR product and faint bands in the NTC amplification suggest that pre-PCR Taq activity was not completely suppressed ( Fig 5 ) . We therefore reduced the concentration of MgCl2 in five of our assays ( Mad-43 , Mad-46 , Mad-12 , Mad-36 and Mad-58 ) to 1 . 5mM and observed the elimination of amplification in the negative controls in most of the affected assays . Reduction of MgCl2 also imparted a general improvement of assay specificity for positive control and test isolates . Surprisingly , the reduction of MgCl2 did not necessitate a corresponding increase in the number of PCR cycles for most assays , contrary to the results observed in our laboratory at NAU . Once again , this may be a result of differences in Taq polymerase activity between the two institutions although we have not found any published evidence of this occurring elsewhere . As a way to rule out possible contamination as a source of non-specific banding , all surfaces used for the preparation of PCR reaction mixtures were sterilized with UV light for 15 minutes and decontaminated with 70% ethanol . Following slight modifications , we found that results of the majority of our assays at IPM aligned very well with results produced at NAU ( Fig 5 and S5 Appendix ) . Our success in developing agarose-MAMA tools and transferring them to IPM facility in Madagascar demonstrates that this SNP genotyping strategy can be achieved with existing technologies routinely used in developing nations ( Fig 5 ) . The transferred agarose-MAMA technology is now in regular use at IPM and was recently used to infer the source of a 2015 pneumonic outbreak [39] . There is ample evidence that this same assay design strategy would transfer well to many other pathogenic organisms [20] . This would allow institutions in developing countries to perform molecular studies in-house and on local infectious organisms . The current methods largely used for short-term control efforts are presence/absence assays ( PCR based [40] or protein based [36] ) that diagnose the causative agents of disease outbreaks but provide no genetic resolution . Having in-house capabilities to genetically discriminate goes beyond what presence/absence assays can provide , therefore , resource-constrained laboratories will be able to advance their epidemiological capabilities over the status quo . The SNP data they generate locally can be shared among research institutes and compared to existing global databases . These advanced capabilities would accelerate the understanding of plague ecology , persistence , and evolution; which in turn could beneficially inform strategies for disease control . Building research capacity in low-resource settings is an important endeavor for global biosafety , response , and biosecurity preparedness [32 , 33] . Our study is a successful model of achieving this goal pragmatically . We successfully developed and transferred genetic tools to a developing nation . The design principles for this technology , which we detail above , can be applied to diverse pathogen species [20] . The use of MAMA technology will give the scientific community the means to gain insight into the genetic patterns and population structure of many neglected diseases . This is a practical formula that will advance global collective knowledge of infectious diseases emanating from more impoverished regions of the world .
The 16 archival strains and five clinical samples were not subject to IRB regulations because they did not meet the federal definition of human subjects research according to 45 CFR 46 . 102 ( f ) . All samples underwent de-identification of patient information prior to Northern Arizona University ( NAU ) transfer . They were collected as part of the medical workup mandated by the Ministry of Health in Madagascar and not for the purpose of this study . For this reason the strains used in this study does not meet the federal definition of human subjects research according to 45 CFR 46 . 102 ( f ) and therefore are not subject to review from NAU Institutional Review Board . DNA samples utilized in this study were obtained from 16 Y . pestis isolates and 5 clinical samples ( bubo aspirates and sputum ) collected from suspected human plague cases ( Table 1 ) . The 16 archival strains and 5 clinical samples ( Table 1 ) originated from diverse geographic locations in Madagascar and were collected as described in a recently published study [34] . Our assays worked well on DNA concentrations that ranged from 1 ng to100 pg . Molecular confirmation of Y . pestis in five clinical samples was based on PCR detection of the pla gene located in a high copy number plasmid PCP1 in addition to positive F1 RDT [28 , 36 , 37 , 41 , 42] . Eighteen previously published SNPs [11 , 34 , 35] specific , or canonical [3] , for a subset of distinct phylogenetic groups within the Y . pestis Malagasy phylogeny ( Fig 1 ) were selected as the targets of agarose MAMA genotyping assays following published guidelines [20] . These selected SNPs can be used in a hierarchical way ( Fig 2; Table 2 ) to assign an unknown strain to one of the most common lineages or phylogenetic subgroups in Madagascar [11 , 34 , 35 , 43] . The common reverse primer and two forward allele-specific MAMA primers for each assay were designed using NetPrimer analysis software ( Premier Biosoft , Palo Alto , CA ) . One forward primer represents the original SNP allele , referred to as “ancestral” and the other represents the mutated SNP allele , referred to as “derived” . The MAMA primers for each assay were designed to compete for the same SNP locus on the template and the resulting amplicon product is generated by the allele-specific MAMA primer that most closely matches the template . To differentiate between the amplicon products of the derived and ancestral genotypes , additional length was added on the derived amplicon product but not the ancestral product . This was achieved by adding 21–28 oligonucleotides rich in GC content at the 5’end of the derived MAMA forward primer ( GC-clamp ) ( Fig 3 ) . Since the primers are incorporated in the final amplicon product , the addition of the GC-clamp on the derived MAMA forward primer resulted in derived amplicon products that were 21–28 bp longer than their ancestral amplicon counterparts . To maximize the visible size differences between the two allelic-specific amplicons when viewed on an agarose gel , the size of the PCR amplicon was restricted to ≤ 80 bases total length . Amplicons within ~80 bases show the greatest migration difference on a gel when small size differences exist . This is the case with our derived and ancestral allele-specific PCR products , which differ between 21–28 bases ( Fig 3 , Table 3 , S2 Appendix ) . Additionally , through the use of the GC-clamp , our assays retained the capability of SNP genotype discrimination on a real-time PCR platform , which is based on differential melt-curve properties of each SNP-specific PCR product [20] . Initial PCR conditions were identified at NAU . PCR conditions for different assays varied and are described in Table 3 . PCR amplification per assay was carried out in 20 μL volume with the following reagents ( see S1 Appendix for volume of each reagent ) : for one reaction , 1x PCR buffer without MgCl2 , MgCl2 range of 1 . 5–2 . 5 mM , 0 . 30 mM deoxynucleoside triphosphate , 1 . 6 units of platinum Taq DNA polymerase ( Invitrogen , Carlsbad , CA ) , both sets of forward MAMA primers ( derived and ancestral allele-specific ) with one common reverse primer at 0 . 40 μL each ( for a 1:1 ratio ) , molecular grade water to achieve 18 μL total volume and 2 . 0 μL of diluted DNA template at ~1ng/μL per reaction . We did not directly test our assays on genomic DNA concentrations below 100 pg but previously published work suggests that the MAMA approach is sensitive to DNA amounts below 100 pg [20] . For each set of reactions , at least one of each ancestral and derived allele templates were used as positive controls as well as at least two no-template controls ( NTC ) . Thermal cycling parameters for the eighteen assays are as follows: initial denaturation at 94°C for 5 min followed by 30–40 cycles of 94°C for 30 s , 51°C -67 . 3°C ( Table 3 ) for 30 s , and 72°C for 30 s , with a final extension at 72°C for 5 min . All PCR amplifications were performed with a MJ Research PTC 200 thermal cycler ( BioRad , Hercules , CA ) . Conditions of agarose gel electrophoresis for PCR amplicons included adding 4 μL of 6x loading dye 0 . 25 w/v xylene cyanol FF and 30% v/v glycerol , water ( Thermo Fisher scientific , Waltham , MA ) to individual PCR products to achieve a 1x final dye concentration . Individual reactions ( 20 μL ) mixed with loading dye were loaded onto a 2% agarose gel matrix; 100 bp DNA ladder ( Invitrogen , Carlsbad , CA ) was used for size referencing . Gels were prepared in 1x lithium borate buffer ( S2 Appendix ) and stained with SybrSafe dye ( Life Technologies , Carlsbad , CA ) . Electrophoresis was conducted at 300V for 25–30 minutes and viewed under UV transillumination . The genotyping accuracy of our SNP assays was validated using positive DNA controls that represented known ancestral and derived allele states for each SNP target . Assay accuracy was further assessed by testing them on 16 genomic DNA extracts of Malagasy Y . pestis strains belonging to known diverse phylogenetic groups ( Table 1 ) based on amplicon sequencing [34] . To assess assay capability to genotype Y . pestis directly from complex DNA samples ( containing high levels of host DNA ) , we tested the performance of each assay on a positive control sample comprised of high concentrations of human DNA with our positive control Y . pestis DNA extract ( strain A1122 ) . To further assess this capability , we tested two assays ( Mad-05 and Mad-43 ) on five human complex samples confirmed to be plague positive . This confirmation was based on Y . pestis-specific TaqMan assay targeting a high copy plasmid pla gene [37] . We assessed the limit of detection of our MAMA tools on these five human clinical samples by testing them on a new TaqMan assay designed for the chromosomal 3a gene ( Fig 4 and S3 Appendix ) . To confirm the specificity of the assays to the Y . pestis genome we tested assay performance on DNA extracts of Bacillus anthracis , human DNA background , and no template water control . To compare assay performance at different research facilities , NAU and IPM jointly conducted a second validation study on the IPM laboratory premises using IPM PCR reagents and instrument ( Fig 5 ) . Thirteen of the eighteen assays were selected for validation ( Table 3 ) . At IPM , specificity was confirmed by testing on Leptospira interrogans serovar Canicola and no template controls . PCR was prepared for each assay using the Taq Core Kits 10 , Cat# EPTQK300 PCR reagents ( MP Biomedicals , Santa Ana , CA ) and amplification was conducted using AB Applied Biosystems Veriti 96 Well Thermal Cycler thermal cycle ( ThermoFisher Scientific , Waltham , MA ) . Electrophoresis was performed using 2% agarose gels visualized on a Gelscan ( Bio-Rad , Hercules , CA ) .
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Although genetic characterization of pathogenic organisms is a powerful tool for investigating outbreak origins and transmission , associated high upfront costs and demanding technological maintenance exclude this tool for many under-resourced laboratories . Paradoxically , resource constrained regions commonly suffer from high rates of infectious diseases and could benefit most from genetic tracking tools . One such country is Madagascar , which lacks resources to acquire high tech genetic typing equipment , yet suffers from seasonal human plague outbreaks . A serious disease , plague is caused by the clonal bacterium , Yersinia pestis , and is capable of causing human outbreaks . Using plague as a model organism , we developed a genetic typing method that requires only basic , widely used molecular machinery . Our tools target unique single mutations in the Y . pestis genome to assign isolates to distinct phylogenetic groups with known geographical distributions . Transfer of this technology to Madagascar permits genetic characterization of strains from current outbreaks . This eliminates the need for external genetic analysis and expands the research capacity of this resource-constrained laboratory by allowing rapid , in-house strain typing . Ultimately , our goal is to help improve the ability of local institutes to genetically characterize circulating strains , link outbreaks to originating sources , and improve our understanding of the ecology of tropical diseases in resource-limited regions of the world .
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2017
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Low cost, low tech SNP genotyping tools for resource-limited areas: Plague in Madagascar as a model
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Songs of many songbird species consist of variable sequences of a finite number of syllables . A common approach for characterizing the syntax of these complex syllable sequences is to use transition probabilities between the syllables . This is equivalent to the Markov model , in which each syllable is associated with one state , and the transition probabilities between the states do not depend on the state transition history . Here we analyze the song syntax in Bengalese finch . We show that the Markov model fails to capture the statistical properties of the syllable sequences . Instead , a state transition model that accurately describes the statistics of the syllable sequences includes adaptation of the self-transition probabilities when states are revisited consecutively , and allows associations of more than one state to a given syllable . Such a model does not increase the model complexity significantly . Mathematically , the model is a partially observable Markov model with adaptation ( POMMA ) . The success of the POMMA supports the branching chain network model of how syntax is controlled within the premotor song nucleus HVC , but also suggests that adaptation and many-to-one mapping from the syllable-encoding chain networks in HVC to syllables should be included in the network model .
Complex action sequences in animals and humans are often organized according to syntactical rules that specify how actions are strung together into sequences [1] , [2] . Many examples are found in birdsong . Songs of birdsong species such as Bengalese finch [3]–[5] , sedge warbler [6] , nightingale [7] , and willow warbler [8] consist of a finite number of stereotypical syllables ( or notes ) arranged in variable sequences . Quantitative analysis of the action syntax is critical for understanding the neural mechanisms of how complex sequences are generated [1] , [3] , [5] , [9] , [10] , and for comparative studies of learning and cultural transmissions of sequential behaviors [11] . Pairwise transition probabilities between syllables are widely used to characterize variable birdsong sequences [3] , [4] , [7] , [8] . This is equivalent to using the Markov model to capture the statistical properties of the syllable sequences . The Markov model is a generative statistical model of sequences , and consists of a set of states . Here the states are mathematical abstractions; they can correspond to concrete neural substrates in specific neural mechanisms of birdsong generation . There is a start state and an end state , which correspond to the start and the end of the sequences , respectively . For each syllable , there is one corresponding state . A state sequence starting from the start state and ending at the end state is produced through probabilistic transitions from one state to the next , and the corresponding syllable sequence is generated . The transition probabilities between the states depend only on the state pairs , and are set to the observed pairwise transition probabilities of the associated syllables . More sophisticated models allow chunks of fixed syllable sequences to be associated with state transitions , with a possibility that a syllable appears in different chunks [5] , [12] , [13] . However , no detailed statistical tests of these state transition models have been performed , and their validity as quantitative descriptions of the birdsong syntax remains unclear . In this paper , we analyze the songs of Bengalese finch . We demonstrate that the Markov model fails to capture the statistical properties of the observed sequences , including the repeat number distributions of individual syllables , the distributions of the N-grams ( sequences of length N ) [14] and the probability of observing a given syllable at a given step from the start of the sequences . We introduce two modifications to the Markov model and show that the extended model is successful in describing the syntax of the Bengalese finch songs . The first modification is adaptation . Syllable repetitions are common in the Bengalese finch songs . Allowing the repeat probabilities of syllables to decrease with the number of repetitions leads to a better fit of the repeat number distributions . The second modification is many-to-one mapping from the states to the syllables . A given syllable can be generated by more than one state . Even if the transitions between the states are Markovian , the syllable statistics are not Markovian due to the multiple representations of the same syllables . The resulting model , which we call a partially observable Markov model with adaptation ( POMMA ) , has history-dependent transition probabilities between the states and many-to-one mappings from the states to the syllables . The POMMA successfully describes the statistical properties of the observed syllable sequences . It is consistent with the branching chain network model of generating variable birdsong syntax , in which syllable-encoding chain networks of projection neurons in the premotor song nucleus HVC are connected in a branching topology [10] , [15] .
For Bird 1 , the vocal elements were clustered into 25 types according to the similarities of their spectrograms ( Materials and Methods ) . We identified seven types of vocal elements as song syllables ( Figure 1a , for syllables A to G , respectively ) . The rest were call notes ( 14 types; 7 examples shown in Figure 1b; C1 and C2 were the the most frequent call notes with , respectively ) and noise . The song syllables were distinguished by rich structures in the spectrograms and tight distributions of the durations ( ) , ( Figure1a ) , and frequently appeared together in long sequences ( sequence length mean ) with small inter-syllable gaps ( ) ( Figure 1c–d ) . The gaps between the consecutive syllables were filled with silence or small noisy fluctuations; no call notes or unidentified vocal elements were in them . In contrast , the call notes had broad or simple spectra and more variable distributions of the durations ( ) , and appeared in short sequences ( sequence length mean ) . All consecutive sequences of the song syllables with inter-syllable gaps smaller than 200ms were assigned as song sequences . Additionally , syllable E ( Figure 1a ) , which predominantly appeared at the start of the sequences obtained above , was assigned as a start syllable such that whenever syllable E appeared for the first time and was not following another E , a new song sequence was started . Thus , a long sequence containing k non-continuous E's in the middle was broken into k+1 song sequences . Altogether , we ended up with 1921 song sequences . Sequences of call notes can precede or follow song sequences , and these call notes were considered to be introductory notes . A simple statistical model of the song sequences is the Markov model , which is completely specified by the transition probabilities between the syllables . For each syllable , there is a corresponding state; additionally , there is a start state ( symbol s ) and an end state ( symbol e ) , as shown in Figure 2a . We computed the transition probability pij for the state Si associated with syllable i to the state Sj associated with syllable j , from the observed song sequences as the ratio of the frequency of the sequence ij over the total frequency of syllable i . Transitions with small probabilities ( pij<0 . 01 ) were excluded . To evaluate how well the Markov model describes the statistics of the observed song sequences , we generated 10000 sequences from the model , and compared three statistical properties of the generated sequences and the observed sequences . The method of sequence generation is as follows . From the start state , one of three states associated with syllables C , E , D can follow with probabilities , , , respectively ( Figure 2a ) . A random number r is uniformly sampled from 0 to 1 . If , is selected ( the state following the start state is ) , and the generated sequence starts with C . If , is selected ( ) , and the sequence starts with E . If , is selected ( ) , and the sequence starts with D . From the selected state , the next state can be selected similarly according to the transition probabilities from . This process of sampling random numbers and selecting the next state and syllable is continued until the end state is reached , generating a specific syllable sequence . Examples of the generated syllable sequences are shown in Figure 2b . The first statistical property to be compared was the distribution of the syllable repeats . Except syllable F , all syllables appeared in repetitions , and the number of repeats were variable . For each syllable , we constructed the probability distribution of the repeat numbers by counting the frequencies of observing a given number of repeats in the observed song sequences . The distributions are shown as black curves in Figure 3a . We also constructed the repeat number distributions from the sequences generated from the Markov model . These are shown as cyan curves in Figure 3a . For syllables E and G , the comparisons are favorable . However , for other syllables the distributions clearly disagree . To quantify the difference between two distributions and , we defined the maximum normalized difference d , which is the maximum of the absolute differences divided by the maximum values in the two distributions , i . e . . The d-values for syllables A , B , C , D , E are , respectively . The major difference is that , for syllables A , C , D , the observed distributions peak at repeat number 4 , 2 , 2 , respectively , while the generated distributions are decreasing functions of the repeat numbers . Indeed , if the probability of returning to state from itself is a constant , the probability of observing repeats of the associated syllable is , which is a decreasing function of . Therefore the Markov model is incapable of producing repeat number distributions having maxima at . The second statistical property to be compared was the N-gram distribution . An N-gram is a fixed subsequence of length . For example , syllable sequences EC and AA are 2-grams; ECC and AAA are 3-grams; etc . We constructed the probability distributions for 2- to 7-grams in the observed song sequences by counting the frequencies of a given subsequence . The results are shown in Figure 4a as black curves , with the N-grams sorted according to decreasing probabilities . We also computed the probability distributions of the corresponding N-grams in the generated sequences . The results are shown in Figure 4a as cyan curves . The distributions for 2-grams agree very well , which is expected , since the Markov model was constructed with the transition probabilities , which are equivalent to the 2-gram distributions . The distributions are quite different for 3- to 7-grams , with -values ranging from 0 . 26 to 0 . 93 ( Figure 4a ) . The final statistical property to be compared was the step probability of the syllables , which is defined as the probability of observing a syllable at a given step from the start . The step probabilities for all syllables computed from the observed song sequences , as well as the step probability of the end symbol , which describes the probability of observing that a sequence has ended at or before a given step , or equivalently , the cumulative distribution function of the sequence length , are plotted as black curves in Figure 5a; and those from the generated sequences are plotted as cyan curves . The comparison for syllable E is quite good ( ) . But the differences between the probabilities for other syllables and the end symbol are large , as indicated by the d-values ranging from 0 . 11 to 0 . 61 . Because the number of the observed song sequences is finite , even a perfect statistical model that would exactly reproduce the Bengalese finch songs cannot lead to zero d-values when compared to the observed distributions . One way of assessing the goodness of fits is to use benchmarks for the d-values created from the observed syllable sequences . We split the observed sequences into two groups by randomly assigning each sequence with a probability 0 . 5 . One group is considered as generated by a perfect statistical model and compared against the other group . For each group we computed the repeat number distributions , the N-gram distributions , and the step probability distributions . The distributions from the two groups were compared to obtain the d-values . We performed the random splitting 500 times and constructed distribution profiles for each d-value . These profiles characterized the fluctuations of the d-values due to the finite number samplings of the observed sequences . For each d-value , we chose the point in the profile as the benchmark . This means that the probability that the d-value is smaller than the benchmark is 0 . 95 . The benchmarks are plotted as gray vertical bars in Figure 6 . A good statistical model of the syllable sequences should produce d-values smaller than the benchmarks or close to them . The d-values obtained from the Markov model , plotted as the cyan curves in Figure 6 , are mostly far beyond the benchmarks . It is clear that the Markov model fails to capture the statistical properties of the songs of Bird 1 . One way of extending the Markov model is to allow the transition probabilities to change depending on the state transition history . There are many possible formulations of such dependence . Adaptation , in which the transition probabilities are reduced as the state transitions are repeatedly revisited , is one formulation motivated by the observation that repeated activations of synapses and neurons reduce their efficacy [16]–[18] . Ideally , all transition probabilities should be subject to dynamical changes depending on the histories of the state transitions in the Markov model . But such a model is difficult to analyze . We therefore considered a simple model in which only the return probabilities of the states from themselves are adaptive . In particular , the return probability of a state is reduced to after repetition of the associated syllable . The transition probabilities to all other states are mutiplied by a factor to keep the total probability normalized . Here is the adaptation parameter , and is the return probability when . The probabilities recover to original values once the dynamics moves on to other states . In this Markov model with adaptation , the probability of observing repetitions is given by ( Materials and Methods ) . We fitted the parameters and for the states with self-transitions in the Markov model ( Figure 2a ) using the repeat number distributions in the observed song sequences . The resulting model is shown in Figure 2b , which is identical to the Markov model ( Figure 2a ) except that the return probabilities for the states associated with syllables A , C , D , E are adaptive , with , respectively . Fittings for syllables B and G did not lead to an adaptive model ( ) , so the associated return probabilities are unchanged . To evaluate the Markov model with adaptation , we again generated 10000 song sequences and compared the repeat number distributions , the N-gram distributions , and the step probabilities to the observed song sequences . The generation procedure was the same as in the original Markov model , except that the return probabilities were adaptive as prescribed above . The repeat number distributions , shown as green curves in Figure 3b , are much improved compared to the Markov model . In particular , the peaked distributions of syllables A , C , D are well reproduced . This demonstrates that the adaptation is capable of producing peaked repeat number distributions . Adaptation did not improve the comparisons of the N-gram distributions ( Figure 4b ) . Adaptaion improved the comparisons of the step probabilities for syllables C , D , F but not for syllables A , B , D and the end symbol ( Figure 5b ) . The d-values ( green curves in Figure 6 ) compared to the benchmarks confirm these observations . The Markov model with adaptation is a better statistical model for song sequences of Bird 1 than the Markov model; however , it is still not capable of accurately describing all statistical properties . In the Markov model and its extension with adaptation , each syllable is associated with one state . Hence the number of states is equal to the number of the syllables , plus two if we count the start and end states ( we will exclude the start and end states when we count the number of states in a model ) . However , it is possible that there is more than one state corresponding to one syllable . This many-to-one mapping from the states to the syllables enables the state transition models to describe more elaborate statistical properties of syllable sequences [10] . With the many-to-one mapping , the number of states can be larger than the number of syllables . When this is the case , some of the states are “hidden” , and cannot be simply deduced by counting the number of syllable types . This kind of model is often referred to as “partially observable Markov model” ( POMM ) [10] , [19] , and is a special case of the hidden Markov model ( HMM ) in which each state is associated with a single symbol . We tested whether introducing many-to-one mapping in addition to the adaptation , which leads to a “partially observable Markov model with adaptation” ( POMMA ) , would better explain the statistical properties of the observed song sequences . To derive a POMM from observed sequences , we developed a state merging method , in which the sequences are translated into a POMM with tree transition structure , and the states are merged if they have equivalent statistical properties and deleted if they are rarely reached ( Materials and Methods ) . To incorporate adaptation to syllable repetitions , we first derived a POMM with the non-repeat versions of the song sequences , in which the repeats of syllables were ignored but the number of repeats were recorded . For example , the non-repeat version of a song sequence ECCDDFBBGBAA is E ( 1 ) C ( 2 ) D ( 2 ) F ( 1 ) B ( 2 ) G ( 1 ) B ( 1 ) A ( 2 ) , where the repeat numbers are in the parenthesis . While creating the tree-POMM and merging the states , the repeat numbers were kept track of , so that the repeat number distribution for each state could be constructed . After following the POMM derivation procedure , there were 18 states in the model . The resulting model was evaluated by generating 10000 sequences following the state transitions from the start state . If a state with no repeat syllable was reached , the syllable associated with the state was generated . If a state with repeat syllables was reached , a repeated sequence of the syllable was generated with the repeat number sampled from the repeat number distribution associated with the state . The sequence stopped if the end state was reached . The generated sequences were compared with the observed sequences for the repeat number distributions of each syllable , the N-gram distributions , and the step probabilities of each syllable and the end symbol . We further tested deletion of each state and mergers of all pairs of states with the same syllables , while monitoring the d-values of the three statistical properties . The deletions and mergers were accepted if the d-values fell below the benchmarks or they were less than the corresponding d-values of the model with the 18 states . The resulting POMM , shown in Figure 7a , has 11 states . Syllables B , C , D , G are associated with two states each , and syllables A , E , F have one associated state each . We next modeled the repeat number distributions in each state with the adaptation model described previously . For some states , the adaptation model was not adequate to fit well the repeat number distributions ( cosine-similarity of the distributions with best fitting parameters; Eq . ( 1 ) in Materials and Methods ) . In such a case , the state was split into two serially connected states . The transitions and associated probabilities to were set to , and and emitted to the same states and probabilities as . has a self-transition with probability and adaptation parameter , while has no self-transition but has a transition probability to . The repeat number distribution with these parameters is given by ( Materials and Methods ) . The parameters were fit with the nonlinear least square fitting procedure . Each state-splitting thus introduced one more state and one more parameter to the model , and was adequate to fit well the observed repeat number distributions when necessary . The resulting POMMA is shown in Figure 7b . Three states associated with syllables A , C , D were split . Altogether , there are 14 states , and the number of states for syllables A to G are 2 , 2 , 3 , 3 , 1 , 1 , 2 , respectively . We generated 10000 syllable sequences from the POMMA ( examples shown in Figure 7c ) , and compared with the observed song sequences the repeat number distributions ( Figure 3c ) , the N-gram distributions ( Figure 4c ) , and the step probabilities ( Figure 5c ) . The comparisons are excellent . All d-values fall below or close to the benchmarks , as shown with the red curves in Figure 6 . In contrast , the d-values for the Markov model are mostly far beyond the benchmarks , as shown with the cyan curves in Figure 6 . The d-values for the Markov model with adaptation are also larger than those for the POMMA , as shown with the green curves in Figure 6 . In particular , the d-values for the N-gram distributions are far beyond the benchmarks and the d-values of the POMMA . Thus , the POMMA is a much better model than the Markov model or the Markov model with adaptation . We repeated the analysis for songs of Bird 2 . The vocal elements were clustered into 7 types , with 6 types identified as song syllables ( Figure 8a , for syllables A to F , respectively ) and one type identified as the introductory note ( Figure 8a , C1 , ) . The song sequences occurred in long sequences ( mean length s . d . ) , with the gaps between consecutive syllables smaller than . The introductory note appeared with repeats preceding the song sequences , and had much smaller volume compared to the song syllables . Less call notes were recorded for Bird 2 than for Bird 1 since the song sequences could be distinguished from the calls based on the lengths of the consecutive sequences of vocal elements with the gaps . A total of 845 song sequences were used for deriving the models . We derived the POMMA for Bird 2 using the same procedure as for Bird 1 . The POMM derived with the non-repeat versions of the song sequences has 10 states ( Figure 9a ) . There are two states associated with syllable A , three states with syllable C , and one state with all other syllables . The states in the POMM with syllable repeats were replaced with states with adaptive self-transition probabilities and additional states when necessary to derive the POMMA ( Figure 9b ) . Syllable A is associated with state 12 and state 10 of the POMM . In state 12 , the number of repetitions of syllable A ranges from 2 to 16 and the repetition distribution peaks at 6 . We modeled this distribution by replacing state 12 with two serially connected states , each with adaptive self-transitions ( Materials and Methods ) . The self-transition probabilities and the adaptation parameters are for , and for . The transition probability from to is . The inward transitions to state 12 of the POMM were set to with the probabilities intact . The outward transitions from state 12 were transferred to and , with the transition probabilities scaled to make sure that total transition probabilities out from and were normalized including the self-transitions and the transitions from to . The resulting repeat number distribution with these parameters was fitted with the observed distribution using the nonlinear least square procedure ( Materials and Methods ) , and the cosine-similarity of the fitted and the observed distributions reached 0 . 98 . We tested simpler models of the repeat number distribution for state 12 , including one state with adaptive self-transition probability and two serial states with only one state with adaptive self-transition probability , but they did not work as well . In state 10 of the POMM , syllable A repeats twice more than 99 . 7% of the time , with the rest being single repeats . We modeled this by replacing state 10 with two serial states with no self-transitions , and with a small probability of not transitioning from to to account for the rare case of single syllable A . The inward transitions to state 10 were transferred to , and the outward transitions from state 10 were transferred to and , similarly as for the case of state 12 . The situation is similar for syllable B in state 11 , which predominantly has two repeats ( 90% ) . State 11 was replaced with two serial states with no self-transitions . The number of repetitions for syllable C in state 6 ranged from 1 to 6 and peaked at 3 . This repetition number distribution was model with one state with adaptive self-transition probability . All other states with more than one repeat were accurately modeled by adding self-transitions as in the Markov model . The cosine-similarities of the fitted and the actual repeat number distributions were all greater than 0 . 95 . The resulting POMMA , shown in Figure 9b , has 13 states ( for syllables A to F , respectively ) . The POMMA accurately describes the statistical properties of the syllable sequences of Bird 2 . We generated 10000 song sequences using the POMMA , and compared to the observed sequences the repeat number distributions , the N-gram distributions , and the step probability distributions . The comparisons are excellent ( Figure 10a–c ) . The -values between the model and the observed distributions are below or very close to the benchmarks obtained from the observed sequences as in the case of Bird 1 ( Figure 10d–f , red curves ) . In contrast , the Markov model and the Markov model with adaptation , derived and evaluated following the same procedure as for Bird 1 , fail to describe the statistical properties of the observed sequences ( Figure 10d–f , cyan and green curves ) . The Markov model with adaptation cannot accurately model the repeat number distribution of syllable A , which has double peaks as shown in the first graph in Figure 10a , even though the model can accurately describe the repeat number distributions of other syllables . This contributed significantly to the inaccuracy of the Markov model with adaptation in the N-gram distributions and the step probability distributions . In the POMM , different states can be associated with the same syllable type . One possible piece of evidence of such many-to-one mapping from states to syllables can be the subtle differences that might exist in the instances of the same syllable associated with different states . For Bird 1 , there are two states for syllables B , C , D , G in the POMM shown in Figure 7a . We compared the duration distributions of the same syllable types associated with different states , as shown in Figure 11a . The distributions are clearly distinctive for syllables B , C , G ( , shuffle test of the significance that the difference of the means of the two distributions is none-zero; the null-distribution of the difference of the means was generated using 500 pairs of randomly shuffled distributions , and the -value is the two-tailed probability of the difference of the means greater than the observed value given the null-distribution ) . There is no clear evidence of distinctions for syllable D ( ) . Despite the significant differences in the durations for syllables B in the two states , the spectrograms of the syllables in the two states are very similar , as shown in Figure 11b . The same is true for other syllables . For Bird 2 , the duration distributions of the same syllable types associated with different states are mostly distinctive ( in three cases and in one case ) , as shown in Figure 11c , while spectrally the syllables are very similar ( examples shown in Figure 11d ) . Most interestingly , durations of the syllables associated with the same state in the POMM can also be distinctive depending on the positions of the syllables in the repetition . In Figure 12a we show three cases . The first is syllable B associated with state 11 in the POMM . The durations of syllable B in the first position of repetition is significantly longer than in the second position of the repetition ( ) . The second is syllable A associated with state 10 . The durations of syllable B in the first position of repetition is clearly shorter than those in the second position ( ) . Spectrally , these sets of syllables are indistinguishable ( Figure 12b for syllable B and 12c for syllable A ) . Both states were replaced with two serial states in the POMMA . Weaker evidence ( ) also exists for syllable A associated with state 12 in the POMM ( Figure 12a ) , which is replaced with two serial states both with adaptive self-transition probabilities in the POMMA . The systematic variations of syllable durations on the positions in repetition supports the idea of using multiple states to model repeat number distributions associated with single states in the POMM . Taken together , the results on syllable durations provide some evidence for the validity of the many-to-one mapping from the states to the syllables .
Bengalese finch songs consist of variable sequences of a finite number of syllables . We have shown that the statistical properties of the sequences are well captured by a state transition model , the POMMA , in which the repeat probabilities of the syllables adapt and many-to-one mappings from the states to the syllables are allowed . The Markov model , which has been commonly used in studies of characterizing variable birdsong sequences , is clearly inadequate for the Bengalese finch songs . The POMMA is an extension of the Markov model . As in the Markov model , each state is associated with a single syllable , and the state transitions are characterized by the transition probabilities . However , unlike the Markov model , many states are allowed to be associated with the same syllable , and the state transition probabilities can vary depending on the history of the state transitions dynamics . These extensions are motivated by considerations of the neural mechanisms of birdsong generation . The premotor nucleus HVC ( used as a proper name ) is a critical area in songbird brain for song production [20] . Firing of HVC neurons that project to RA ( the robust nucleus of the arcopallium ) drives singing [21] , [22] . Experimental evidence suggests that a syllable is produced by the bursts of spikes propagating in a chain network of HVC projection neurons [22]–[25] . A set of HVC projection neurons reliably drive the RA neurons [22] , which in turn drive downstream motor neurons to produce sound . Such a chain network in HVC could be a neural representation of a single state in POMMA . Thus , the association of a state to a single syllable is a reflection of the reliability of a chain network driving the production of a syllable . The connections from HVC to RA are learned [26]–[29] . This makes it possible that different sets of HVC projection neurons are set up during learning to drive acoustically similar syllables . In zebra finch , different neural activity in HVC has been observed during vocalizations of acoustically similar syllables [21] , [30] , supporting the possibility of multiple sets of HVC neurons driving the same syllable . Such a possibility of many-to-one associations from the neural sets in HVC to syllables motivates introduction of many states corresponding to one syllable in the POMMA . It is conceivable that the same syllable driven by different sets of HVC neurons have subtle differences in the acoustic features due to imperfections of learning . Indeed , we found that instances of the same syllable associated with different states in the POMMA can have significantly different duration distributions ( Figure 11 and Figure 12 ) . A recent study has shown that the acoustic features of Bengalese finch syllables can shift systematically depending on the sequences around the syllables [31] , which is in agreement with our observation . There can be alternative explanations to our observations that do not require separate sets of HVC neurons to encode the same syllable . One possibility is that the sequence-dependent differences in the acoustic features are due to the history dependence of the activations of the unique set of HVC neurons driving the syllable . Another possibility is that the differences are due to the inertia of the motor periphery rather than the variations in neural activity [31] . Finally , the differences can be due to sequence dependent activations of neurons in other areas in the song system , such as RA [31] . More direct experiments , such as single unit recordings in HVC of singing Bengalese finch , are required to test unambiguously whether the many-to-one mapping from HVC to RA exits . The POMMA can be directly mapped onto the branched chain network model of the Bengalese finch song syntax [10] . Each state of the POMMA corresponds to a syllable-encoding chain network of HVC projection neurons , and each transition in the POMMA corresponds to the connection from the end of the synaptic chain corresponding to to the start of the synaptic chain corresponding to . The POMMA and the network model thus have identical branching connection patterns . In the network model , spike propagation along a chain drives the production of a syllable . At a branching point , spike propagation continues along one of the connected chain networks with a probability that depends on a winner-take-all competition and noise [10] , [15] . The success of the POMMA in capturing the statistical properties of the Bengalese song sequences supports the branched chain network model of Bengalese finch song syntax . A critical prediction for the network model is that , for some syllables , HVC projection neurons should burst intermittently , bursting during some instances of the syllables but not in others . This is markedly different from the case of zebra finch , in which HVC projection neurons burst reliably for each production of the song sequence [22] , [25] . The prediction can be tested with electrophysiological experiments . Adaptations are widely observed in neural systems . Continuous firing can reduce neuron excitability [18] , and excitatory synapses can be less effective when activated repeatedly [16] , [17] . In zebra finch , consecutive singing increases the durations of the song syllables [32] . It is possible that the slow-down of the song tempo is due to some adaptive processes in HVC . In the branched chain network model of the Bengalese song syntax , weakening connection strength from one chain network to another at a branching point reduces the transition probabilities between them [10] . These observations suggest that the transition probabilities might not be fixed . Introducing adaptive processes in the neural excitability and synaptic efficacy should lead to adaptive transition probabilities in the branched chain network model , especially for the repeated activations of a chain network , which correspond to the reduction of the self-transition probability . It remains to be seen experimentally whether HVC projection neurons or the excitatory synapses between them have the adaptive properties . It might be also possible to see the signatures of adaptation by analyzing the burst intervals of HVC projection neurons during syllable repetitions , or the burst intervals of RA neurons . The observation that burst intervals in RA neurons steadily increase with song sequence repetition in zebra finch [32] suggests that similar effect could be observed in Bengalese finch . We emphasize that adaptation is important for reducing the complexity of the state transition model . It is possible to include syllable repetitions in the POMM , with no adaptations of the transition probabilities , and accurately describe the statistical properties of the Bengalese finch songs ( Materials and Methods; supplementary Figures S2–S4 ) . However , compared to the POMMA with adaptation , the number of states is larger . While the POMMA has 14 and 13 states for Bird 1 and Bird 2 ( Figures 7b and 9b ) , respectively , the POMM has 20 and 18 states ( Figures S2 and S3 ) . In the POMM , many states are needed to produce the peaked repeat number distributions such as that of syllable A in Bird 2 ( Figure 10a ) . The difference of the number of states in the POMM and the POMMA should increase with the number of syllables with peaked repeat number distributions . It is the significant reduction of the model complexity that motivates our choice of the model with adaptation ( the POMMA ) rather than the non-adapting model ( the POMM ) . We have used multiplicative reduction of the repeat probabilities . It remains to be investigated whether other formulations of the adaptation can be similarly or even more effective . In our approach , only the repeat probabilities are adapted . A more consistent model should allow adaptation and recovery in all transition probabilities , such that the state transition dynamics depends on the history of the entire syllable sequence , not just the syllable repetitions . This approach might be important if there are repeats of short sequences such as ABABABAB , in which the transition probabilities from A to B and B to A might need to be adapted . But such a model is difficult to derive from the observed sequences . In our data , repetitions of short sequences were rarely seen , hence adapting only the repeat probabilities of single syllables was adequate . We have shown that adaptation alone is not sufficient to augment the ability of the Markov model to describe the Bengalese finch songs , and the many-to-one mapping from the states to the syllables is necessary . However , we cannot rule out the possibility that the more consistent model with all transition probabilities adaptive , and perhaps with more complex forms of adaptation , can eliminate the requirement for the many-to-one mapping . The POMMA is closely related the hidden Markov model ( HMM ) [33] , which is widely used to model sequential structures in human languages [14] , [33] , [34] and genomes [35] , [36] . In the HMM , the transitions between the states are as in the Markov model , but each state is allowed to emit all symbols ( or syllables in birdsong case ) with some probability dependent on the state . The flexibility of the state and the symbol associations makes the HMM much more capable of capturing statistical properties of sequences than the Markov model . To apply the HMM to birdsong , however , it makes more sense to require that a state can be associated with a single syllable only , if the correspondence between the model and the neural dynamics of birdsong generation is considered [10] . HVC neurons reliably activate RA neurons [22] , and there is no evidence that activation of the same sets of HVC or RA neurons can probabilistically produce multiple syllables . The HMM with the restriction that one state emits one symbol is the POMM [10] , [19] . The POMM is distinguished from the Markov model in that a syllable can be associated with multiple states ( many-to-one mapping from the states to the syllables ) . Even though the transitions between the states are Markovian , the syllable statistics can be non-Markovian due to the multiple representations of the same syllables [10] . The HMM with no one-to-one restriction does not lead to a more compact model than the POMMA for the Bengalese finch songs ( Materials and Methods ) . To achieve the level of the accuracy of the POMMA , the HMM needs close to 18 states for both Bird 1 and Bird 2 ( Figures S7 ) , which is similar to the POMM . Indeed , most states in the HMMs predominantly emit one syllable ( Figures S5 and S6 ) , and the structures of the HMMs and the POMMs are similar for both birds . There are previous efforts of describing Bengalese finch song sequences with state transition models [12] , [13] . Chunks of syllable sequences , which are fixed sequences of syllables , were extracted from the observed sequences and used as the basic units of the state transition models [12] , [13] . A syllable can appear in many chunks , hence these models implicitly contain the many-to-one mapping from the states to the syllables . But the chunk extractions and the state models were not derived from the statistics of the observed sequences . Furthermore , the models were not tested against the observed song sequences for statistical properties . In contrast , the POMMAs were derived from and tested with the observed song sequences . Although there is a close connection between the POMMA and the branched chain network model of how HVC generates variable syllable sequences in Bengalese finch [10] , [15] , the POMMA or the POMM can be compatible with alternative neural mechanisms , including feedback control of sequences through RA to HVC projections [31] , syntax generation in other nuclei upstream to HVC or RA in the song system [12] , [37] , [38] , noisy recurrent networks in HVC [39] , and branched chain networks of inhibitory HVC interneurons [40] . It is also possible that different statistical models can be derived from these mechanisms . More detailed analyses of the alternative mechanisms are needed to see whether they can produce syllable sequences with statistics compatible to the observed Bengalese finch songs . There should be a family of equivalent POMMAs for the songs of a Bengalese finch . For example , the same repeat distributions can always be modeled with more states . The POMMA that we have derived is the simplest model that is consistent with the data . Given this insight , we expect that the neural representation of the syntax should be similar to the derived POMMA but most likely not identical . We have developed a state merging method for deriving the POMM from the observed syllable sequences . It is possible to use the well-established methods of training the HMM [33] to derive the POMM . We observe that our method is faster than the training methods of the HMM . A more detailed analysis of the state merging method is needed to quantify its speed and convergence properties . In conclusion , we have derived a compact POMMA that successfully describes the statistical properties of Bengalese finch songs . Our approach can be useful for modeling other sequential behaviors in animals and statistical properties of sequences in general .
Acoustic recordings were performed with a boundary microphone ( Audio-Technica PRO44 ) . Microphone signals were amplified and filtered ( 8th-order Bessel high-pass filter with and 8th-order Bessel low-pass filter with kHz , Frequency Devices ) . The filtered signals were digitized with a 16-bit A/D converter ( PCI-6251 , National Instruments ) with a sampling rate of kHz . Vocal elements were defined as continuous sounds bounded by silent periods . Thresholding the amplitudes of the pressure waves is a common approach of isolating vocal elements in birdsongs [31] , [41] , [42] . We developed a similar method . From the pressure wave of a vocalization , the oscillation amplitude at time was obtained by finding the maximum of in the interval of one oscillation cycle that contains . The amplitude was further transformed to , where is a smoothing function that uses the second order Savitzky-Golay filter with window ( 801 data points ) . Vocal elements were isolated by detecting continuous regions in that were above a threshold function . The threshold function was obtained in moving windows ( step size ) ; in each window , the threshold was set at the 0 . 3 point from the floor of to the local maximum of in the window . The floor is the characteristic value of in the regimes with no sound , and was identified as the position of the lowest peak in the histogram of the values of for all . A detected region was excluded if the total area above was smaller than multiplied by the difference between the maximum value and ; or if the maximum value of in the region minus was smaller than ; or if the width of the region was less than . These exclusions ensured that most noisy fluctuations were not counted as vocal elements . The results of the vocal element isolations were manually checked and adjusted by plotting out the waveforms in conjunction with the boundaries of the vocal elements to ensure that no obvious mistakes were made . The parameters used in the above procedure were empirically determined to yield the best results in our dataset . They should be adjusted if the procedure is used for other recordings of birdsong . The waveform of an isolated vocal element was transformed into a spectrogram , which is the energy density at frequency and time . The frequency was restricted to to . The spectrogram was computed with the multi-taper method [43] ( time-bandwidth product , 1 . 5; number of tapers , 2 ) with window size and step size ( software from http://chronux . org ) . The frequency was discretized into grids with between adjacent points . To exclude silent periods at the beginning and the end of the vocal element , the time span of the spectrogram was redefined to the region in which the total power in the spectrum at each time point exceeded 5% of the maximum of the total powers . We used a semi-automated procedure to cluster the vocal elements into separate categories . Similarities between the vocal elements were defined and used in a clustering algorithm . The final results were visually inspected and adjusted by plotting the spectrograms of all vocal elements in the clusters . The similarity between the vocal elements was defined as follows . The spectrogram was considered as a sequence of spectra at the discrete time points . The spectrum at each time point was smoothed over the frequency domain using the second order Savitzky-Golay filter with window size of 5 frequency points . The smoothed spectrum was further decomposed into a slowly varying background by smoothing with the second order Savitzky-Golay filter with window size of 20 frequency points; and peaks by subtracting out . The relative importance of the peaks compared to the background was characterized by the weight , where is the standard deviation of the distribution over the frequency domain . The spectrum at of was compared to the spectrum at of by computing which is the weighted sum of the cosine-similarities between the peaks and between the backgrounds . Here and are the peaks and and are the backgrounds of and , respectively . The cosine-similarity of two vectors ( or distributions ) was defined as ( 1 ) where and are the means and is the norm . is the maximum of the weights across all time points of the two syllables . If , the two spectra and were considered the same ( denoted ) . Otherwise the two spectra were defined as distinctive . The similarity between two syllables was characterized by the longest common subsequence ( LCS ) between them . A common subsequence was defined by a set of time points in syllable and a set in syllable , such that the spectra at corresponding time points are the same , i . e . , , . . . , . There was an additional restriction that corresponding time points did not differ by more than , i . e . for all . The length of the common subsequence is . LCS is the common subsequence with the maximum length . A long LCS indicates that the two syllables are similar , while a short LCS indicates they are dissimilar . We defined the similarity score of two syllables as the length of LCS divided by the mean of the lengths of the two syllables . Types of vocal elements were identified by clustering 4000 vocal elements using a core-clustering algorithm , modified from the algorithm described in Jin et al [44] . The algorithm is based on the distance between vocal elements , defined as one minus the similarity score , and consists of the following steps . ( 1 ) For each vocal element , find the list of nearby vocal elements with distances less than 0 . 1 . ( 2 ) Among the vocal elements that are not yet part of a cluster , select the one with at least 5 nearby vocal elements and the smallest mean distances to its nearby vocal elements as the core point of a new cluster . ( 3 ) Assign all unclustered vocal elements that are in the nearby-list of the core point to the new cluster . All vocal elements that are in the nearby-list but already clustered are reassigned to the new cluster if their distances to the core points of their respective clusters are larger than their distances to the new core point . ( 4 ) Repeat steps ( 2–3 ) until no new cluster could be created . ( 5 ) Merge clusters . Two clusters are merged if at least 5% of the vocal elements in each cluster had small distances ( ) to the vocal elements in the other cluster . ( 6 ) Assign vocal elements that are not yet clustered . A vocal element is assigned to the cluster that had the maximum number of members whose distances to the vocal element are less than 0 . 15 . In some cases , individual clusters contained separate vocal element types that had subtle differences but distinguishable . Such clusters are split into new clusters . Once the types of vocal elements were identified with the clustering algorithm , we used the following procedure to classify all vocal elements that were not already clustered . ( 1 ) Identify the center of each cluster as the vocal element that has the minimum mean distances to all other vocal elements in the cluster . ( 2 ) Compute the distances from the vocal element to be assigned to the cluster centers . The three clusters with the lowest distances are selected . ( 3 ) Compare the durations of the vocal elements in the selected clusters to the duration of the candidate vocal element , and select 20 ( or less if the cluster size is smaller than 20 ) from each selected cluster that are closest . ( 4 ) Compute the distances from the candidate vocal element to the selected vocal elements . ( 5 ) Assign the vocal element to the cluster to which the most of the selected vocal elements with the distances smaller than 0 . 2 belong . ( 6 ) If none of the selected vocal elements have distances less than 0 . 2 , do not assign the candidate vocal element . The unclustered vocal elements were grouped into 2000 blocks , and their mutual distances were computed . The clustering and identifying procedures were repeated until no more clusters emerge . During this process , clusters were merged if they were subjectively judged as similar by inspecting the spectrograms and the mutual distances between the members of the clusters . Individual vocal elements were reassigned to different clusters if necessary . The final results of the clustering of the vocal elements were validated and adjusted by visual inspections of the spectrograms . In the case of a state with self-transition , the transition probability is initially but is reduced to after repetitions of the state , where is the adaptation parameter . The probability of having repeats is then More complex repeat distributions can be modeled with more states . One model has two serial states . Both are associated with the same syllable , and only has self-transition . The transition probability from to is , and the self-transition probability of is initially but undergoes adaptation with the adaptation parameter . The probability of observing one repeat is given by The probability of observing repeats is given by Another model with two serial states allows both and to have self-transitions with parameters for and for . The probability of transitioning to after leaving is . The probability of observing one repeat is The probability of observing two repeats is in which the first and the second terms are the probabilities of the state sequences and , respectively . Similarly , for , the probability of observing repeats is given by where and Here and are the probabilities of repeating and times , respectively . The cases above were all we needed to model the Bengalese finch songs in this study . More complex models with more states can be necessary for other Bengalese finch songs , and the repeat number distributions can be similarly derived . We used a state-merging method to derive the POMM from the observed syllable sequences . The process is illustrated with an example in Figure S1 with a simple case of two syllables 1 and 2 . From 5000 observed sequences ( Figure S1a ) , a tree Markov model is constructed ( Figure S1b ) . For each sequence , the tree model contains a unique path of state transitions from the start state . This is achieved by starting with the start state and the end state only , and adding new states as needed by finding the paths for the sequences . For example , consider the first sequence 12 . At this point no states are emitted from the start state . A new state with syllable 1 is added and connected from the start state; a new state with syllable 2 is added and connected from ; finally , connects to the end state . With the additions of the two states , the sequence is mapped to a state transition path . Now consider the second sequence 121 . State transitions generate the first two syllables in the sequence . To generate the last 1 , a new state with syllable 1 is added , and is connected from and also to the end state . Now branches into and . This process continues , until all observed sequences are uniqued mapped into the paths in the tree model . The transition probabilities from a state to all connected states are computed from the frequencies of the transitions observed in the sequences . The tree model is a simple POMM that is a direct translation of the observed sequences; it contains all observed sequences . However , the tree model is incapable of generating novel sequences that are statistically consistent with the observed sequences . Moreover , since each transition probability can be considered as a parameter , the number of parameters in the tree model is enormous , severely restricting its predictive power . To reduce the number of parameters , a more concise POMM is derived by merging the equivalent states in the tree model . If two states are associated with the same syllable , and the probability distributions of subsequent sequences of length 15 or smaller are similar ( cosine-similarity ) , the two states are merged . This is done until no further mergers are possible . Finally , state transitions with probabilities smaller than 0 . 01 are eliminated , and all states that are reached less than 0 . 005 times in all observed sequences are also eliminated . These merging and pruning procedures lead to a concise POMM with five states for the simple example , as shown in Figure S1c . There are two states for syllable 1 , which is an example of the many-to-one mapping . Indeed , the observed sequences in Figure S1c was generated with a POMM with structure identical to the one in Figure S1c and with equal transition probabilities to all connected states from a given state . The example demonstrates that the state merging method can lead to a concise POMM from observed sequences . The procedure was used to derive the POMMs for Bird 1 and Bird 2 using the non-repeat versions of the syllable sequences and keeping track of the number of syllable repetitions in each state , as described in the main text . The accuracy of the POMM from the state merging procedure was tested by generating 10000 sequences ( see the main text for the generation procedure ) and comparing with the observed sequences the repeat number distributions , the N-gram distributions , and the step probability distributions . The -values were computed and compared with the benchmarks derived from the observed syllable sequences as discussed in the main text . The number of states in the POMM was further reduced by testing mergers of all states associated with the same syllables and testing deletions of all states . The mergers and deletions were accepted if the -values of the resulting POMM fell below the benchmarks or they were smaller than the -values of the original POMM . The state merging and subsequent reduction of the number of states was fully automated . The POMM derived from the above procedure were morphed into the POMMA by replacing each state associated to repeating syllables with one or more states with adaptive self-transition probabilities . Various adaptive models for the repeat number distributions were tested as described in the main text . The process of morphing the POMM to the POMMA was not automated . To derive the POMM from the syllable sequences but include the syllable repetitions without introducing adaptation , each state associated with repeating syllables in the POMM derived with the non-repeat versions was replaced by its own POMM . The replacing POMM was derived from the repeat sequences of the syllable using the HMM training method described below . In this case , since there is only single syllable in the repeat sequences , the HMM is equivalent to the POMM . We increased the number of states in the replacing POMM until the repeat number distribution of the syllable could be reproduced with the cosine-similarity . The in and out transitions in the POMM from the non-repeat versions were retained in the replacements . The resulting POMMs for Bird 1 and Bird 2 are shown in Figures S2 and S3 . Direct applications of the state merging procedure did not lead to concise POMMs using the syllables sequences with repetitions . The main reason was that the syllable repetitions , especially when the mean repetition number was larger , required more sequences than available to accurately judge the statistical equivalence of the states for merging in the tree POMM . We used the Baum-Welch algorithm for training the HMM from the observed sequences [33] . A number of states is chosen for the HMM . There is a start state and an end state , which only emit the start and the end symbols , respectively . All other states can be associated with any of the syllables with the emission probabilities . The transitions from the start state to the end state and from all states to the start state were excluded . All transition and emission probabilities were set randomly initially , and adjusted with the observed sequences using the Baum-Welch algorithm until they converged ( errors of the probabilities below 0 . 001 ) . To avoid local minima in deriving the HMM , we repeated the training process 20 times , and selected the HMM with the maximum log-likelihood for the observed sequences . The derived HMM was evaluated by generating 10000 sequences and comparing the statistics with the observed sequences . The generation method is the same as in the Markov model , except that at each state , the syllable generated is determined from the emission probabilities at that state . The number of states in the HMM was systematically varied . The results for Bird 1 and Bird 2 are shown in Figures S5–S7 .
|
Complex action sequences in many animals are organized according to syntactical rules that specify how individual actions are strung together . A critical problem for understanding the neural basis of action sequences is how to derive the syntax that captures the statistics of the sequences . Here we solve this problem for the songs of Bengalese finch , which consist of variable sequences of several stereotypical syllables . The Markov model is widely used for describing variable birdsongs , where each syllable is associated with one state , and the transitions between the states are stochastic and depend only on the state pairs . However , such a model fails to describe the syntax of Bengalese finch songs . We show that two modifications are needed . The first is adaptation . Syllable repetitions are common in the Bengalese finch songs . Allowing the probability of repeating a syllable to decrease with the number of repetitions leads to better fits to the observed repeat number distributions . The second is many-to-one mapping from the states to the syllables . A given syllable can be generated by more than one state . With these modifications , the model successfully describes the statistics of the observed syllable sequences .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"neuroscience/behavioral",
"neuroscience",
"neuroscience/motor",
"systems",
"neuroscience/theoretical",
"neuroscience"
] |
2011
|
A Compact Statistical Model of the Song Syntax in Bengalese Finch
|
The incorporation of the envelope glycoprotein complex ( Env ) onto the developing particle is a crucial step in the HIV-1 lifecycle . The long cytoplasmic tail ( CT ) of Env is required for the incorporation of Env onto HIV particles in T cells and macrophages . Here we identify the Rab11a-FIP1C/RCP protein as an essential cofactor for HIV-1 Env incorporation onto particles in relevant human cells . Depletion of FIP1C reduced Env incorporation in a cytoplasmic tail-dependent manner , and was rescued by replenishment of FIP1C . FIP1C was redistributed out of the endosomal recycling complex to the plasma membrane by wild type Env protein but not by CT-truncated Env . Rab14 was required for HIV-1 Env incorporation , and FIP1C mutants incapable of binding Rab14 failed to rescue Env incorporation . Expression of FIP1C and Rab14 led to an enhancement of Env incorporation , indicating that these trafficking factors are normally limiting for CT-dependent Env incorporation onto particles . These findings support a model for HIV-1 Env incorporation in which specific targeting to the particle assembly microdomain on the plasma membrane is mediated by FIP1C and Rab14 .
HIV-1 particles assemble on the plasma membrane of infected human cells . The underlying shell of the developing viral particle is formed by the Pr55Gag polyprotein ( Gag ) , which is translated deep within the cytoplasm of cells and reaches the plasma membrane by an unknown mechanism [1] , [2] , [3] . Gag binds to the viral genomic RNA through its nucleocapsid ( NC ) region and is responsible for packaging of RNA into the developing particle . A -1 ribosomal frameshift results in the formation of the Gag-Pol precursor protein in a molar ratio of 20∶1 Gag to Gag-Pol [4] . Gag , Gag-Pol , and the associated viral genomic RNA traffic together to the particle assembly site on the plasma membrane . The envelope glycoprotein complex ( Env ) of HIV-1 is simultaneously incorporated onto the lipid membrane of the developing particle , and yet must travel via a very different route to reach the assembly site . Env is synthesized on ribosomes associated with the endoplasmic reticulum as a precursor protein , gp160 [5] . Trimerization of gp160 is required for exit from the ER [6] . During transit through the Golgi , gp160 becomes heavily glycosylated and the precursor subunits are cleaved by furin-like proteases to create gp41 ( TM ) and gp120 ( SU ) subunits . A trimer of heterodimers of TM and SU forms the functional Env glycoprotein complex . Once the mature complex reaches the cell surface , it is either incorporated onto budding virions or rapidly internalized [7] , [8] , [9] , [10] . In contrast to the dense concentration of envelope glycoprotein spikes on the surface of orthomyxoviruses [11] , paramyxoviruses [12] , herpesviruses [13] , and the relative abundance of envelope spikes on gammaretroviruses [14] , lentiviral particles incorporate a very limited number of envelope proteins . Estimates from electron tomography studies reveal an average of 8–14 trimers per virion particle when virions with full-length HIV envelopes are examined [15] , [16] . SIV strains with truncations in the cytoplasmic tail ( CT ) have been noted to incorporate 70–79 trimers and have been widely employed in cryoEM studies of the envelope spike [15] , [16] , [17] . The reasons for the paucity of Env trimers on lentiviral particles in the absence of mutant CT domains remain unknown . HIV and SIV Env proteins incorporate long CTs , averaging about 150 amino acids in length . The Env CT has been the subject of numerous studies , yet a complete understanding of how the CT mediates incorporation onto budding virions remains elusive . The Env CT includes a membrane-proximal YXXφ motif that mediates clathrin- and AP2-dependent endocytosis [8] , [9] , [18] , [19] , [20] and a number of additional tyrosine-based and dileucine motifs that have been implicated in endocytosis and trafficking of Env [21] . A YW802 motif was reported to interact with TIP47 , and by this interaction mediate Env interaction with Gag and incorporation onto particles [22] . Murakami and Freed reported that the long CT of Env is required for particle incorporation into virions and for viral replication in T cell lines and primary cells [23] . This report established the cell type-specific , tail-dependent incorporation onto virions , suggesting that host factors may mediate Env incorporation and be differentially expressed in distinct cell types . The Rab11a Family Interacting Proteins ( FIPs ) are effector molecules that bind Rab11 family members and mediate sorting of cargo from the endosomal recycling compartment to the plasma membrane [24] , [25] . We evaluated Rab11-FIPs as potential mediators of HIV-1 Env incorporation into particles . We identified Rab11-FIP1C/RCP as a specific host cellular factor mediating the delivery of HIV-1 Env onto virion particles in a CT-dependent manner . FIP1C-dependent Env incorporation was not mediated through interaction with Rab11a , but instead required interaction with Rab14 .
The limited number of Env trimers on HIV-1 particles suggested to us that there is an inherent limitation to Env incorporation that is unrelated to the production of Env protein in the cell . To directly test this hypothesis , we expressed a constant amount of Gag expression plasmid together with an increasing amount of full-length or cytoplasmic tail-truncated Env . We used the CTdel-144 Env ( CT144 ) [23] , which retains only 6 cytoplasmic residues of the tail , in order to determine if the cytoplasmic tail was important in limiting the amount of Env on the particle . Expression of full-length Env in cells increased sequentially with increasing amount of plasmid transfected . The amount of Env on released HIV-1 particles did not continue to increase , however , but instead reached a plateau at levels of 0 . 8 µg or more of transfected DNA ( Fig . 1A , WT Env ) . CT144 expression in cells increased similarly , but incorporation onto HIV-1 particles continued in a linear fashion without apparent saturation ( Fig . 1A , CT144 ) . The saturation of full-length Env incorporation was further documented by quantitation of Env/Gag ratio in released particles as measured by densitometry ( Fig . 1B ) or by gp120 and p24 ELISA ( Fig . 1C ) . We confirmed that the pelletable Env observed in these experiments was particle-associated and not due to overexpression of Env and release on microvesicles ( Fig . S1 ) . Despite the ability to incorporate Env at higher levels upon overexpression , particles bearing CT144 Env were consistently less infectious as measured using TZM-bl indicator cells ( Fig . 1D ) . A plateau in particle infectivity conferred by WT Env was noted at the same level of Env expression found to be saturating by Western blot or ELISA analysis ( Fig . 1D ) . These results indicated to us that the cytoplasmic tail of Env confers a limitation to Env trimer incorporation onto HIV-1 particles . Saturation of Env on HIV particles could be due to limiting concentrations of a cellular factor that is required for its trafficking or incorporation . We initially investigated a variety of factors involved in cellular recycling pathways as candidates . Depletion of Rab11a or Rab11b did not significantly diminish Env on particles , yet overexpression of the active , GTP-bound form of Rab11a ( Rab11a S20V ) diminished cellular and particle-associated Env ( Fig . S2 ) . This led us to consider the Rab11 Family Interacting Proteins ( Rab11-FIPs ) as candidates involved in HIV-1 Env trafficking , reasoning that one of the FIPs may have been saturated by Rab11 S20V overexpression . The Rab11-FIPs were originally identified in a yeast 2-hybrid screen using Rab11aS20V as bait [24] . Rab11-FIPs form parallel coiled-coil homodimers and bind to two Rab11a-GTP molecules , creating a heterotetrameric complex that regulates distinct intracellular membrane trafficking events . To examine the potential involvement of the Rab11-FIPs in Env trafficking , we performed shRNA-mediated depletion of each of the described FIPs in HeLa cells . Cells were transfected with NL4-3 proviral DNA and analyzed for particle output , Env content , and infectivity of the released particles . Knockdown efficiency was assessed by real-time PCR , and varied from 65–90% by this assay ( Fig . 2A ) . A striking phenotype was observed in this experiment . Depletion of FIP1C led to markedly diminished particle infectivity , while little effect was seen with depletion of FIP2 , FIP3 , FIP4 , or FIP5 ( Fig . 2A ) . We next verified the knockdown of FIP1C using specific antisera , and examined cellular and viral levels of Env ( Fig . 2B ) . Notably , the cellular levels of gp160 were not altered by FIP1C depletion , while particle-associated Env was markedly lower . Both gp120 and gp41 levels were diminished , suggesting a defect in Env heterotrimer incorporation rather than simply induced shedding of gp120 ( Fig . 2B ) . While depletion of FIP1C removed Env from particles , it had no effect on cellular Env production or on the release of particles ( CA bands , Fig . 2B ) . The striking effect of FIP1C depletion on Env incorporation suggested to us that it might be an essential cofactor for Env trafficking and particle incorporation . However , to rule out off-target effects , we next restored cellular levels of FIP1C using an shRNA-resistant GFP-tagged FIP1C cDNA . Fig . 2C demonstrates that FIP1C depletion and not transduction with a scrambled shRNA vector depleted cellular FIP1C levels and diminished particle Env incorporation . FIP1C levels were not restored upon expression of the unaltered FIP1C cDNA . In contrast , expression of FIP1C cDNA with silent mutations rendering it resistant to shRNA-mediated depletion was able to fully restore the incorporation of Env onto HIV-1 particles ( Fig . 2C , FIP1C cDNA* lanes ) . Accompanying the restoration of FIP1C function and Env incorporation , the infectivity of the released particles was fully restored ( Fig . 2D ) . We next examined the effect of FIP1C depletion on truncated Env using NL4-3delCT144 , and extended our analysis to the H9 T cell line . In HeLa cells , depletion of FIP1C markedly diminished Env incorporation in released particles as before ( Fig . 3A , WT lanes ) . CT144 Env incorporation was not altered by depletion of FIP1C ( Fig . 3A , CT144 lanes ) . The effect of FIP1C depletion was even more striking when the experiment was performed in H9 cells ( Fig . 3A , H9 particle lanes ) . CT144 was very poorly incorporated in control-transduced cells in H9 cells , reflecting cytoplasmic tail-dependent particle incorporation of Env as had been reported [23] . The small amount of incorporated CT144 Env was not altered by FIP1C depletion ( Fig . 3A ) . Particle infectivity measured in supernatants from wildtype NL4-3 virus was diminished upon FIP1C depletion in both HeLa and H9 cells , while NL4-3delCT144 particle infectivity was not altered ( Fig . 3B ) . Notably , the effect of FIP1C depletion on HIV-1 Env incorporation was specific for HIV , as no effect on Env incorporation or particle infectivity was observed for viruses pseudotyped with VSV-G or the amphotropic murine leukemia virus ( MLV ) Env ( Fig . S3 ) . FIP1C is involved in directed trafficking of membrane proteins from the endosomal recycling complex to the plasma membrane . We hypothesized that FIP1C depletion would diminish the specific outward trafficking of HIV-1 Env from this compartment . Cell surface Env was measured in control H9 cells versus those depleted of FIP1C . Cell surface gp120 levels were significantly diminished following FIP1C depletion , while total cellular levels of Env were comparable in control and FIP1C-depleted cells ( Fig . 3C and 3D , WT ) . In contrast , no significant effect on cell surface levels of CT144 Env was observed following FIP1C depletion ( Fig . 3C and 3D , CT144 ) . We conclude from this that there is a partial depletion of cell surface Env following FIP1C/RCP depletion , and this depletion is itself dependent upon an intact cytoplasmic tail . We next established a population of H9 cells depleted for FIP1C , and infected this population with NL4-3 virus or NL4-3delCT144 at a multiplicity of infection ( MOI ) of 0 . 5 . No replication of the cytoplasmic tail-truncated virus was observed in H9 cells ( Fig . 3E , closed triangles ) . Wildtype NL4-3 replicated well in H9 cells that had been transduced with control shRNA ( filled squares , Fig . 3E ) . Depletion of FIP1C resulted in markedly delayed and slow replication of NL4-3 ( Fig . 3E ) . At late timepoints , this virus was able to replicate more efficiently , and this was associated with higher levels of FIP1C that developed in the H9 population over time , and not with viral reversion to a FIP1C-independent state ( not shown ) . We concluded from this that FIP1C was essential for Env incorporation and for viral spreading infection . FIP1C normally resides in a perinuclear compartment that colocalizes strongly with Rab11a [26] . HIV-1 Env is found extensively in intracellular compartments as well as on punctate foci on the plasma membrane , making studies of surface redistribution of Env by cellular factors challenging . However , we reasoned that Env might redistribute GFP-FIP1C in a measurable manner . GFP-FIP1C was found predominantly in a focal perinuclear location in HeLa cells in the absence of HIV-1 Env expression ( Fig . 4A ) . Transfection of NL4-3 provirus led to a marked redistribution of GFP-FIP1C to intracellular vesicles and to the plasma membrane ( Fig . 4B ) . The redistribution of GFP-FIP1C out of the perinuclear compartment was especially evident in fields where cells expressing Env were adjacent to those lacking Env ( Fig . 4C ) . In contrast , transfection of NL4-3delCT144 provirus in general retained the perinuclear distribution of GFP-FIP1C ( Fig . 4D ) . The differences in redistribution of FIP1C out of the perinuclear location were scored by a blinded observer and were significantly different for WT vs . CT144 provirus ( Fig . 4E ) . To determine if this effect was due to Env alone and not to other components of the provirus , the experiment was repeated with expression of WT Env and CT144 Env alone . Again the peripheral redistribution of FIP1C by WT Env was noted , with a much lower level of FIP1C outside of the perinuclear region upon expression of CT144 Env ( Fig . 4F ) . Differences in FIP1C distribution between full-length and CT144 Env were statistically significant ( p<0 . 001 by chi squared test ) . These results suggest a CT-dependent outward movement of FIP1C . While FIP1C was critical to Env incorporation onto particles , depletion of Rab11 itself did not significantly alter Env incorporation ( discussed further below ) . To reconcile how a Rab11a-binding protein acting as an adaptor could modulate Env incorporation in a Rab11a-independent manner , we investigated the involvement of Rab14 in Env incorporation . Rab14 has recently been identified as a Rab11-FIP binding protein [27] . These investigators had suggested that Rab14 might interact with the Rab binding domain of multiple Rab11-FIP proteins . We therefore sought to investigate the association of Rab14 with different Rab11-FIPs using a split-ubiquitin yeast two-hybrid assay as a method for assessing protein interactions . While we did confirm that Rab14 interacted with FIP1C as well as FIP1B , we did not find any interaction with FIP2 ( Supplemental Table S1 in Text S1 ) . All three Rab11-FIP proteins interacted with Rab11a , but none interacted with Rab8a . Given these results , we next evaluated whether Rab14 associated in situ with FIP1C . Fig . 5A demonstrates that GFP-FIP1C colocalized extensively with both Rab11a and Rab14 in a concentrated compartment representing the ERC . However , FIP1C ( 1–614 ) , which lacks a Rab11a binding domain was distributed throughout the cytosol rather than concentrated in the ERC , and did not colocalize with either Rab11a or Rab14 ( Fig . 5B ) . The carboxyl terminal truncation Rab11-FIP1C ( 560–649 ) caused a marked concentration of both Rab11a and Rab14 with the dominant negative construct ( Fig . 5C ) . Nevertheless , while GFP- FIP1C ( 592–649 ) , which retains the Rab11-binding domain , did colocalize extensively with Rab11a , the localization with Rab14 was markedly attenuated ( Fig . 5D ) . These results suggested that Rab14 may not be associating with the Rab11-binding domain as suggested by Kelly at al . ( 2010 ) . Since Kelly et al . ( 2010 ) had suggested that Rab14 associated with other Rab11-FIP proteins through their conserved Rab11-binding domains , we next examined the association of Rab14 with the dominant negative trafficking mutant GFP-FIP2 ( 129–512 ) . While this mutant strongly concentrated Rab11a , it had no effect on Rab14 distribution ( Fig . S4 ) . Similarly , we did not observe any effects of a carboxyl terminal fragment of FIP5 on Rab14 distribution , although it did strongly concentrate Rab11a ( data not shown ) . These immunofluorescence studies , combined with yeast two-hybrid results , indicated that Rab14 is not interacting with the Rab binding domain of Rab11-FIP1C , since this helix is strongly conserved in all FIP proteins . To evaluate further the binding requirements for Rab14 with FIP1C , we compared the sequences of FIP1C and FIP2 in their carboxyl termini and performed directed mutagenesis of candidate residues that were different between the two Rab11-FIPs . We mutated two residues from FIP1C to those matching the sequence in FIP2 to create FIP1C ( S580N/S582L ) . Fig . 5E shows that this double mutation in the context of GFP-FIP1C ( 560–649 ) elicited a loss of colocalization with Rab14 , while colocalization with Rab11a was unaffected . These results demonstrate that the region proximal to the Rab11-binding domain in FIP1C is responsible for association with Rab14 , separate from the Rab11a association requirements . We next confirmed direct binding of WT GFP-FIP1C but not GFP-FIP1C ( S580N/S582L ) with Rab14 through co-immunoprecipitation studies ( Fig . S5 ) . Having mapped the Rab14 binding domain on FIP1C , we next asked if the Rab14-FIP1C interaction was responsible for the FIP1C-dependent incorporation of Env onto HIV-1 particles . Based on the interaction between FIP1C and Rab14 , we considered the possibility that Rab14 is involved in HIV-1 Env trafficking and incorporation onto HIV particles . We first examined the effects of dominant-negative ( S25N ) and constitutively-active ( Q70L ) forms of Rab14 on incorporation of HIV-1 Env . Remarkably , dominant-negative Rab14 ( S25N ) diminished the incorporation of Env onto HIV-1 particles in a dose-dependent fashion ( Fig . 6A ) . Constitutively-active Rab14 ( Q70L ) expression had the opposite effect , and resulted in an enhanced level of particle-incorporated Env ( Fig . 6A , Rab14Q70L ) . These results supported an important role for Rab14 in HIV-1 Env incorporation . In the absence of FIP1C , the enhancement of particle-incorporated Env by Rab14 ( Q70L ) was not observed ( Fig . S6A ) . Consistent with our prior findings of CT-dependence , Rab14 constitutively-active and dominant-negative constructs had no effect on incorporation of CT144 Env onto particles ( Fig . 6A , rightmost panels ) . Combined with the interaction mapping data above , these data strongly suggest that an interaction between FIP1C and Rab14 is required for incorporation of full-length HIV-1 Env onto particles . To evaluate further the role of Rab14 on Env incorporation , we depleted Rab14 or Rab11a in HeLa cells using shRNA and compared the effects on Env incorporation . Rab14 depletion , but not Rab11a depletion , diminished HIV-1 Env incorporation ( Fig . 6B ) . The effect of Rab14 depletion was specific for HIV-1 Env , as it had no effect on pseudotyping of HIV particles with VSV-G or amphotropic MLV Env ( Fig . S6B ) . To further examine the specificity of the Rab14-FIP1C interaction in mediating HIV Env incorporation , we utilized the interaction domain mapping data outlined above , and asked if FIP1C ( S580N/S582L ) , which does not interact with Rab14 , could rescue Env incorporation in FIP1C-depleted HeLa cells . Strikingly , shRNA-resistant wildtype FIP1C rescued Env incorporation as before , while the 580/582 double mutant was unable to rescue Env onto particles ( Fig . 6C , compare lanes 3 and 4 to lanes 5 and 6 ) . This indicates that Rab14 interaction is required for rescue of Env particle incorporation . We then introduced mutations in the Rab11 binding domain that have been shown to eliminate Rab11a binding and disrupt membrane association ( FIP1C ( I621E ) ) or that eliminate Rab4 interactions without disrupting membrane interactions ( FIP1C ( D622N ) ) [28] , placing these both in the context of the shRNA-resistant FIP1C construct . When compared head-to-head , FIP1C ( I621E ) was not able to rescue Env particle incorporation ( Fig . 6C , lane 7 ) while FIP1C ( D622N ) was competent for rescue ( Fig . 6C , lane 8 ) . We interpret this to mean that mislocalized I621E does not reach the ERC , and subsequently cannot direct outward sorting of Env . It is unclear whether this means that normally Rab11 binding is required for the initial ERC localization of FIP1C , followed by Rab14-directed outward sorting , or if the mislocalization and lack of rescue of Env incorporation is reflective of other defects elicited by this mutation . The fact that knockdown of Rab11a or Rab11b did not alter Env incorporation ( Fig . S2A ) suggests the latter interpretation , but further work will be needed to define this . Taken together , our data support a model in which a specific Rab14/FIP1C complex mediates the intracellular trafficking and particle incorporation of the HIV-1 Env glycoprotein complex . Cytoplasmic tail-dependent incorporation of HIV-1 Env onto HIV particles is not limited to T cells , but also has been demonstrated in monocyte-derived macrophages ( MDMs ) [23] . We therefore sought to determine if FIP1C-dependent Env incorporation was also present in MDMs . Using an efficient siRNA method we recently established in MDMs [29] , we depleted FIP1C in primary MDMs from two donors , then infected the cells with VSV-G-pseudotyped wildtype NL4-3 or NL4-3delCT144 . As expected , incorporation of gp120 and truncated gp41 on particles from NL4-3delCT144-infected cells was markedly lower than wildtype NL4-3 ( Fig . 7A ) . Depletion of FIP1C in MDMs significantly diminished gp41 and gp120 particle incorporation ( Fig . 7A ) and infectivity ( Fig . 7B ) . These data indicate that FIP1C is required for the CT-dependent incorporation of Env onto HIV particles in MDMs as it is in T cell lines and HeLa cells . Our initial rationale for searching for cellular trafficking factors involved in Env incorporation was the fact that limitations to Env particle incorporation were not related to the amount of Env produced in infected or transfected cells ( Fig . 1 ) . After identifying the involvement of FIP1C and Rab14 in Env incorporation , we wished to determine if overexpression of FIP1C and Rab14 would therefore overcome this limitation and significantly enhance Env incorporation . We created a cell line that inducibly overexpressed FIP1C and Rab14Q70L , and repeated the titration of Env expression in the presence of a constant amount of cellular Gag . Remarkably , the expression of FIP1C and Rab14Q70L enhanced Env incorporation by 2–3 fold ( Fig . 7C ) . Cell surface levels of Env were also increased upon overexpression of FIP1C and Rab14Q70L as assessed by a surface biotinylation assay ( Fig . 7C , bottom ) . The increase in Env incorporation on particles released from this cell line was quantified by densitometric evaluation of Env/Gag ratios and by determination of Env and Gag particle content by ELISA ( Fig . 7D ) . We conclude that FIP1C and Rab14 are limiting factors for Env particle incorporation in HeLa cells .
Retroviral envelope proteins are incorporated onto developing viral particles by a process that remains incompletely understood . Pseudotyping of viruses or virus-like particles ( VLPs ) with envelope glycoproteins of foreign viruses has revealed both specific and non-specific mechanisms of Env incorporation . HIV-1 can form infectious particles with Env proteins from a wide variety of retroviral subfamilies , including alpha- , beta- , gamma- , and delta- , and even spumaretroviruses [30] , [31] , [32] , [33] , [34] ( reviewed in [35] ) . This promiscuity for pseudotyping extends to Env glycoproteins from other viral families , including coronaviruses [36] , paramyxoviruses [37] , filoviruses [38] , rhabdoviruses [39] , and others . Promiscuity of Env incorporation would argue either for a lack of specificity of Env incorporation , or for a common mechanism that is shared by these diverse viruses for Env delivery and incorporation . In contrast , HIV-1 Env itself is quite selective in its ability to pseudotype other retrovirus particles . HIV-1 Env with a full length CT is unable to pseudotype MLV vectors , although truncation of Env CT allows MLV pseudotyping [40] . HIV-1 mutants with point mutations in MA fail to incorporate full-length HIV-1 Env , while truncation of CT allows Env incorporation [41] , [42] . Although direct interactions between Env CT and MA have been reported in vitro for HIV and SIV [43] , [44] , a direct interaction has been difficult to substantiate in cell-based assays . Thus there is selectivity and specificity of particle incorporation that is conferred by the full length HIV-1 Env CT , while the mechanism has not yet been fully explained . The specificity of HIV-1 Env incorporation could be due to direct interactions with MA , to indirect Gag-Env interactions mediated by an intermediate host protein , or could be explained by cotrafficking to a common site of assembly as outlined by Jorgenson and colleagues [45] . Our data support a requirement for specific outward Env trafficking from the ERC mediated by FIP1C and Rab14 . Functionally , the Rab11-FIPs play important roles in the recycling of cargo from the recycling system to the cell surface . Rip11/Rab11-FIP5 is required for the transport of GLUT4 vesicles to the cell surface following insulin treatment [46] . FIP2 regulates the recycling of a number of plasma membrane proteins , including aquaporin-2 and CXCR2 , to the cell surface [47] , [48] . FIP2 has also been implicated in trafficking and assembly of respiratory syncytial virus , providing prior evidence that viruses may usurp the FIP-mediated transport mechanisms to deliver their cargo to the site of assembly [49] . Most significant for this report is the ability of FIP1C to regulate the recycling of α5β1 integrin and EGFR1 to the cell surface [50] . We identified FIP1C and its binding partner Rab14 as essential components determining the incorporation of full-length but not truncated Env onto HIV-1 particles . Rab11-FIP1 was first identified in 2001 through a yeast 2-hybrid screen employing dominant active Rab11a as bait [24] . Rab11a and Rab11-FIPs localize to the endosomal recycling complex and to the apical recycling endosome in polarized cells , where they regulate sorting and plasma membrane recycling [25] , [26] . There are five members of this family that have been divided into two classes based upon the presence of specific motifs including C2 domains ( class 1 members FIP1B , FIP1C , FIP2 , and Rip11/FIP5 ) and EF hands ( class 2 members FIP3 and FIP4 ) [25] , [51] . All family members possess a carboxyl-terminal alpha-helical Rab11-binding domain ( RBD ) . The RBD of the FIPs forms a parallel coiled-coil homodimer that interacts with switch 1 and switch 2 regions of the GTP-bound form of Rab11a , resulting in a heterotetrameric complex of two FIPs and two Rab proteins [52] , [53] , [54] , [55] . While a previous investigation has suggested that the class I FIPs are capable of forming complexes with the Rab14 GTPase [27] , our findings suggest that Rab14 interacts specifically with FIP1C through a region separable from the Rab11 binding domain . These results indicate that FIP1C has multiple binding domains for small GTPases , as previously demonstrated for Rabaptin-5 ( Rab5 and Rab4 ) and FIP3 ( Arf6 and Rab11 ) [29] , [56] . Our studies demonstrate the discrete point mutations in FIP1C proximal to the RBD can disrupt Rab14 binding without affecting Rab11a association . Similarly , we could not demonstrate any association between Rab14 and FIP2 or FIP5 in either split ubiquitin yeast two-hybrid assays or in immunofluorescence studies . The difference between our studies and those of Kelly , et al . ( 2010 ) could be due to differences in the cell systems examined , but it seems more likely that the discrepancy stems from the antibodies used to detect Rab14 . The Aviva Rab14 antibody we have used here has no cross reactivity with either Rab11a or Rab7 . In contrast , we have found that the antibody used by Kelly , et al . [27] has prominent cross-reactivity with recombinant Rab11a and Rab7 ( data not shown ) . In any case , the fact that the S580N/S582L FIP1C dual mutant was unable to rescue Env particle incorporation confirms that it is the Rab14/FIP1C interaction rather than the interaction of Rab11a with FIP1C that defines the trafficking complex for HIV Env . In support of this , we found that the GTP-bound form of Rab14 ( Q70L ) actually enhanced the amount of particle-associated Env , while dominant-negative Rab14 ( S25N ) diminished Env incorporation in a dose-dependent fashion . Similar experiments with dominant-negative and constitutively-active Rab11a did not demonstrate this effect . In fact , the constitutively-active Rab11a ( S20V ) construct diminished Env incorporation , perhaps through competition for FIP1C binding . Together , our results support a complex of GTP-bound Rab14 and FIP1C as essential components mediating the transport of HIV-1 Env to the particle assembly site . Our data support a model of directed outward trafficking of HIV-1 Env to a plasma membrane microdomain for specific incorporation onto the lipid envelope of budding virions . We suggest that FIP1C and Rab14 are required for this directed trafficking from the ERC to the site of particle assembly and budding , as depicted in Fig . 8 . This model predicts that cellular levels of specific trafficking factors may limit the number of Env trimers incorporated onto particles . In support of this idea , we were able to modestly increase the amount of Env on HIV particles by overexpression of constitutively-active Rab14 . We propose that the delivery of Env trimers to the cell surface is required but not sufficient for particle incorporation for wildtype viruses bearing intact CTs . Truncated Env constructs such as CT144 reach the cell surface of permissive cells and may be acquired passively , explaining the lack of saturation of truncated Env shown in Fig . 1 of this report . However , T cells are not permissive for incorporation of truncated Env [23] . Therefore the nature of the assembly microdomain in which assembly occurs may differ between cell types such as HeLa cells and H9 cells , with exclusion of truncated Env from this microdomain in H9 cells and lack of exclusion in HeLa . Active trafficking of full length Env , directed by Rab14 and FIP1C , is required for Env delivery and particle incorporation in both cell types . The tail dependence of this phenotype predicts that specific motifs in the CT will be engaged with a FIP1C/Rab14 complex in the outward trafficking of Env . Future work will focus on the identification of these motifs and the interacting domains within FIP1C . We note that there are numerous polymorphisms in FIP1C alleles reported in the NCBI database , some of which could be damaging to its function . It will be important in future studies to examine the potential effect of polymorphisms in FIP1C on HIV acquisition or disease progression . In conclusion , we have identified for the first time a complex of Rab14 and Rab11-FIP1C with a biologically-relevant cargo . The Rab14/FIP1C complex directs HIV-1 Env to the plasma membrane assembly site for incorporation onto developing particles in a manner that depends upon the long cytoplasmic tail of Env . It will be of great interest to define other components of this trafficking complex , such as motor protein ( s ) involved in the outward trafficking of Env , and to extend these findings to the assembly processes of other retroviruses as well as more distant families of viruses .
Peripheral blood was obtained from healthy volunteer donors according to a protocol approved by the Emory University Institutional Review Board ( IRB ) . Written informed consent was obtained from donors , and samples were de-identified prior to handling by laboratory personnel . HeLa and TZM-bl cells [57] ) were maintained in DMEM containing 10% FBS and antibiotics . The H9 T cell line ( ATCC HTB-176 ) were cultured in RPMI medium 1640 supplemented with 10% FBS , 2 mM Glutamine , and antibiotics . MDMs were prepared from human peripheral blood as described previously [29] . HeLa T-REX cells for inducible expression of FIP1C and Rab14 were created using the T-REx system and vector pCDNA5/TO ( Invitrogen ) . Cells were maintained in tetracycline-free media and induction carried out with 1 µg/ml doxycycline . Vesicular stomatitis virus G glycoprotein ( VSV-G ) -pseudotyped HIV-NL4 . 3 and HIV-CT144 virus stocks were generated by cotransfecting 293T cells with pNL4 . 3 or pNL4 . 3CT144 and the VSV-G expression plasmid pHCMV-G . Viruses were harvested from transfected 293T supernatants 48 hours post-transfection , filter-sterilized , and assayed with TZM-bl indicator cells for infectivity assessment . Env expression plasmids pIIINL4env and pIIINL4envCTdel-144 were gifts kindly provided by Dr . Eric Freed [23] . Vesicular stomatitis virus glycoprotein ( VSV-G ) expressing plasmid pHCMV-G was provided by J . Burns [58] . Amphotropic MLV Env expression vector pCL-Ampho was from Inder Verma [56] . Rab11a expression plasmids and FIP1C expression plasmids have been previously described [24] , [26] , [59] . FIP1C-specific shRNA constructs and methods , siRNA sequences , and real-time PCR reagents are described in Supplemental Experimental Procedures . Silencing of FIP1C-expression in MDMs was carried out by transfecting FIP1C-specific siRNA ( Dharmacon ) using the N-TER nanoparticle siRNA transfection system ( Sigma ) as previously described [29] . Rabbit polyclonal antibody against FIP1C was obtained from Sigma . Goat polyclonal antibody against HIV-1 gp120 and gp160 ( used in Western blotting ) was AHP2204 from AbD Serotec ( Oxford , UK ) . Human anti-gp120 antibody for immunofluorescence experiments was IgG1 b12; synthesized from recombinant cDNA by the laboratory of James Crowe ( Vanderbilt University ) . Antibody used for immunoblotting of gp41 was murine monoclonal 5009 from BTI research reagents ( Columbia , MD ) . HIV Gag detection was performed with either rabbit anti-p17 polyclonal , mouse anti-p24 monoclonal CA-183 ( provided by Bruce Chesebro and Kathy Wehrly through the NIH AIDS Research and Reference Reagent Program ) , or mouse anti-p24-FITC ( KC57-FITC ) obtained from Beckman Coulter ( Fullerton , CA , USA ) . Anti-VSV-G antibody was from Sigma ( V5507 ) , and anti-amphotropic MLV goat antiserum ( 679 ) was obtained from Chris Aiken ( Vanderbilt ) . Rabbit anti-Rab14 antibody was obtained from Aviva Systems Biology . Mouse monoclonal anti-Rab11a ( 8H10 ) was described previously by the Goldenring laboratory [60] , who also provided rabbit anti-Rab11b polyclonal antiserum ( VU76 ) . Alexa Fluor goat anti-mouse and Alexa Fluor goat anti-rabbit secondary antibodies , as well as the DAPI nucleic acid stain were obtained from Molecular Probes ( Eugene , OR , USA ) . IRDye goat anti-mouse and IRDye goat anti-rabbit secondary antibodies used for Western blots were obtained from Li-cor Biosciences ( Lincoln , NE , USA ) . Images of GFP-FIP1C and Env distribution were obtained with a Nikon TE2000-U spinning disc confocal fluorescence microscope with automated stage and Hamamatsu EM-CCD camera developed by Improvision under the control of the Volocity software , or with a DeltaVision imaging system developed by Applied Precision . The system was equipped with an Olympus IX70 microscope and a CoolSnap HQ2 digital camera under the control of the softWoRx software . Imaging processing and deconvolution was performed using softWoRx 3 . 7 . 0 . Colocalization measurements were quantified with the colocalization function of Volocity 5 . 2 . 1 . For immunofluorescence experiments , HeLa cells were washed with PBS and fixed in 4% paraformaldehyde for 12 minutes at RT . After fixation , cells were extensively washed including an overnight wash at 4 degree . Cells were then permeabilized for 10 minutes with 0 . 2% Triton X-100 and block in Dako blocking buffer for 30 minutes . Primary and secondary antibodies were diluted in Dako antibody diluent to appropriate concentrations . DAPI was used to stain the nuclei of the cells . The coverslips were mounted in Gelvatol overnight and examined directly the next day . Images of endogenous Rab11 and Rab14 together with GFP-FIP1C were obtained using an Olympus Fluoview confocal microscope; details of immunofluorescence staining protocols and antibodies for these experiments are provided in Supplemental Experimental Procedures . HeLa and H9 surface staining was performed with human monoclonal anti-gp120 antiserum at final concentration of 0 . 1 ug/ml in PBS with 2% BSA and a second PE-conjugated anti-human antibody at 0 . 02 ug/ml . Mouse anti-p24-FITC ( KC57-FITC , Beckman-Coulter ) was employed following permeabilization to allow gating on the infected population . HeLa cells were harvested at 48 hours post-infection in this analysis , while H9 cells were harvested at day 4 post-infection . Analysis was performed on a FACSCanto flow cytometer ( BD Biosciences ) and using FlowJo software ( Treestar , Inc ) . At 48 hours post transfection , virion- containing culture supernatants were harvested , clarified by low speed centrifugation and filtered ( 0 . 2 µm ) . Infectious virus release was determined by inoculating TZM-bl indicator cells , plated the previous day in 12 well plates at 1×105 cells/well , with 400 µl of serially diluted supernatants . At 48 hours after infection , cells were fixed and stained with X-gal . Blue cells were counted and infectivity was calculated as blue cell numbers per nanogram of p24 inoculation . The remainder of the virion containing supernatant ( 750 µl ) was layered onto 200 µl of 20% sucrose in PBS and centrifuged at 20 , 000 g for 2 hours at 4°C . Virion pellets , and corresponding virion producing cells were dissolved in SDS PAGE loading buffer . Virion and cell lysates were separated on 10% polyacrylamide gels and subjected to Western blotting .
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Enveloped viruses must develop strategies to ensure that a sufficient quantity of their receptor-binding envelope proteins are incorporated onto the surface of viruses as they form . The HIV envelope glycoprotein is specifically incorporated onto assembling virions in relevant cells such as T lymphocytes in a manner that requires its long cytoplasmic tail . The mechanism underlying this specific incorporation has remained unknown . Here , we identify a cellular trafficking pathway that is required for the incorporation of HIV envelope onto virions . A combination of the adaptor protein Rab11-FIP1C and Rab14 directs the envelope protein onto assembling virions , and loss of either of these host factors results in the production of virus particles lacking envelope . We also found that FIP1C was required for replication in T cell lines . This study identifies a trafficking complex required for HIV envelope incorporation and for the formation of infectious HIV particles .
|
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"immunodeficiency",
"viruses",
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2013
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Rab11-FIP1C and Rab14 Direct Plasma Membrane Sorting and Particle Incorporation of the HIV-1 Envelope Glycoprotein Complex
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Seasonal influenza viruses are typically restricted to the human upper respiratory tract whereas influenza viruses with greater pathogenic potential often also target extra-pulmonary organs . Infants , pregnant women , and breastfeeding mothers are highly susceptible to severe respiratory disease following influenza virus infection but the mechanisms of disease severity in the mother-infant dyad are poorly understood . Here we investigated 2009 H1N1 influenza virus infection and transmission in breastfeeding mothers and infants utilizing our developed infant-mother ferret influenza model . Infants acquired severe disease and mortality following infection . Transmission of the virus from infants to mother ferrets led to infection in the lungs and mother mortality . Live virus was also found in mammary gland tissue and expressed milk of the mothers which eventually led to milk cessation . Histopathology showed destruction of acini glandular architecture with the absence of milk . The virus was localized in mammary epithelial cells of positive glands . To understand the molecular mechanisms of mammary gland infection , we performed global transcript analysis which showed downregulation of milk production genes such as Prolactin and increased breast involution pathways indicated by a STAT5 to STAT3 signaling shift . Genes associated with cancer development were also significantly increased including JUN , FOS and M2 macrophage markers . Immune responses within the mammary gland were characterized by decreased lymphocyte-associated genes CD3e , IL2Ra , CD4 with IL1β upregulation . Direct inoculation of H1N1 into the mammary gland led to infant respiratory infection and infant mortality suggesting the influenza virus was able to replicate in mammary tissue and transmission is possible through breastfeeding . In vitro infection studies with human breast cells showed susceptibility to H1N1 virus infection . Together , we have shown that the host-pathogen interactions of influenza virus infection in the mother-infant dyad initiate immunological and oncogenic signaling cascades within the mammary gland . These findings suggest the mammary gland may have a greater role in infection and immunity than previously thought .
The influenza A RNA virus ( Orthomyxoviridae ) is a significant threat to human health and the global economy by causing morbidity and mortality through frequent epidemics and sporadic pandemics [1] . Seasonal influenza viruses such as seasonal H1N1 and H3N2 infect the cells lining the upper respiratory tract . These viruses typically cause mild clinical symptoms including rhinorrhea , fever , and myalgia [2] . Unlike seasonal influenza , highly pathogenic viruses such as avian H5N1 are able to infect organs outside the upper respiratory tract such as the lower lungs , intestines , and brain , causing a more severe illness [3] . The localization of influenza virus infection is largely influenced by the distribution of the virus cellular receptors where the α2 , 6-linked sialic acid receptor is predominantly expressed in the upper respiratory tract and α2 , 3-linked sialic acid receptors in the lower [4] . In 2009 , a novel variant pandemic H1N1 influenza A ( 2009 H1N1 ) virus emerged from Mexico that targets both the upper and lower respiratory system [4] . During the 2009 H1N1 pandemic , infants , nursing-mothers , and pregnant women were considered to be at high risk of developing severe disease [5–7] and viral or bacterial pneumonia [8] . Pediatric and pregnant/postpartum mother mortalities were sharply increased in the 2009–2010 influenza season compared to previous seasons [9 , 10] . Importantly , the underlying mechanisms leading to severe disease in mothers and infants have not been elucidated , suggesting the need for an animal model . The mother-infant dyad represents a unique relationship where the infant and mother are often considered to function as a single unit . Through breastfeeding , mothers provide nutrients , beneficial bacteria , and immune protection to the infant by transfer of cells , antibodies , proteins , and sugars through breast milk [11 , 12] . The consumption of breast milk has a central role establishing infant health and disease susceptibility by influencing the infant gastrointestinal and immune systems [13 , 14] . The maternal breast is a highly structured organ and an area of frequent traffic between infant and mother . Fluid exchange also occurs from infant to mother during breastfeeding as liquid can infiltrate the mammary gland through retrograde flux of the nipple [15] . It is currently thought that maternal plasma cells producing antibodies for breast milk delivery are a consequence of cellular migration from the maternal gut to the breast without a host-pathogen interaction occurring within the mammary gland [16] . Although most infants worldwide are breastfed , little is known regarding pathogen transmission between nursing-mothers and infants , breast susceptibility to pathogens , or the local immune response within the breast due to lack of a translatable research model [5] . Breast disease is a significant problem worldwide . The postpartum and lactation phase in breast metamorphosis has been associated with increased breast cancer incidence . While breastfeeding has been shown to protect against long-term breast cancer development , postpregnancy breast cancer is hypothesized to be a consequence of breast remodeling and involution associated with lactation [17 , 18] . Infectious breast tumor etiologies have been hypothesized as nucleic acids from viruses , such as human papilloma virus ( HPV ) and Epstein-Barr virus ( EBV ) , have been recovered from within breast tumors [19–22] . These viruses express oncoproteins that inactivate the host’s basic cellular activity by blocking tumor repressor proteins or promote cellular transformation [23] . Furthermore , these viruses may have a significant latency period allowing long periods of viral presence without clearance . Although infants and mothers are highly susceptible to the influenza virus [24–27] , little is known regarding mother-infant transmission and susceptibility of breast tissue . We developed a ferret model of influenza transmission in the mother-infant dyad ( infant-to-mother ferret influenza transmission model ) with the aim of investigating virus transmission and immune consequences during influenza infection in this highly susceptible population . Ferrets are considered to be the most appropriate animal model for human influenza pathogenesis , vaccine optimization , and antiviral development [28 , 29] . Ferrets are also used to investigate transmissibility of the influenza virus due to the similarity of the ferret and human respiratory biology [29–31] . Previously , we developed both “young” and “aged” ferret models of influenza pathogenesis due to shared characteristics of development and aging between ferrets and humans [32 , 33] . Building on these studies , we developed a novel infant-mother ferret influenza model utilizing nursing-mother ferrets and their 4-week-old feeding-infants ( infant-mother ferret influenza transmission model ) to investigate the host-pathogen interactions of influenza virus infection in the mother-infant dyad . Our investigation showed established virus infection within the mammary gland and viral shedding in breast milk suggesting a novel mechanism of influenza virus transmission .
To investigate influenza infection in the mother-infant dyad , we intranasally inoculated 4-week-old infant ferrets with the 2009 H1N1 virus , A/California/07/2009 ( Cal/07 ) , at 105 EID50 . Infants were housed with nursing-mothers before and after inoculation . Nasal washes ( NW ) , temperature , weight , clinical assessment , and survival were collected daily as shown in the design schematic ( Fig 1A ) . Necropsies were performed at Day 3 , 4 , 7 , and 14 post-inoculation or if an animal was removed from the study ( reached weight cut-off or had succumbed to illness ) . Inoculated infant ferrets developed illness leading to mortality starting with a high fever at 104% of baseline temperature ( p<0 . 05 ) Day 2 post-infant-inoculation . Infants then developed hypothermia ( Day 6 ) ( Fig 1B ) . All infants reached weight cut-off ( lost 20% of original weight; p<0 . 05 ) or died by Day 8 post-infant-inoculation ( Fig 1C and 1D ) . Four days post-infant-inoculation , nursing-mother ferrets also developed influenza-like symptoms including increased body temperature ( 103% of baseline ) . Mother weights significantly decreased by 13% Day 4 post-infant-inoculation ( Fig 1C ) . Weight did not recover by study end and one mother reached cut-off ( Fig 1D ) . The weight and temperature of the mothers of mock inoculated control infants are presented in S1 Table which showed mothers did not develop symptoms post-mock-inoculation of infant ferrets . As the mothers of inoculated infants began to display clinical signs of influenza including temperature increases , weight loss , and mortality , we went on to analyze the upper and lower respiratory tracts of the infants and their mothers to determine virus transmission and pathogenesis . Viral burden in infant NW was first detected Day 1 post-infant-inoculation ( Fig 2A ) . The NW of nursing-mothers were positive for influenza virus Day 3 post-infant-inoculation . Viral titers remained high in mother NW Day 6–7 ( Fig 2A ) at more than 6 TCID50/ml ( Log10 ) . Residual virus persisted in the mothers’ NW by Day 10 ( S1A Fig ) . Virus transmission between adult ferrets pair-housed ( S1C Fig ) were used as a comparison of transmission dynamics between infants and mother ferrets . For this control , one adult was intranasally inoculated under similar circumstances as the infant-mother inoculations and NW were collected for viral load assessment . Naïve cage mates of inoculated adult ferrets began shedding virus in NW Day 3 post-inoculation . These results showed that the influenza virus was able to be transmitted from direct intranasally inoculated infant ferrets to the upper respiratory tracts of their nursing-mothers on a similar time scale as that of adult ferrets . We next determined if transmission of the virus from infants to mother led to lower respiratory tract infection . Examination of viral titers in the lower respiratory tract of the mothers found virus levels above 6 TCID50/ml ( Log10 ) in both the trachea and lungs post-infant-inoculation in 2 out of 3 mothers investigated ( Day 4 and 7 post-infant-inoculation ) ( Fig 2B ) . H&E ( hematoxylin & eosin ) staining of mother lungs revealed delayed , airway-localized inflammation ( Fig 2C ) . Day 3/4 post-infant-inoculation , mother lungs showed areas of minimal infiltrating leukocytes ( black arrows: diffuse areas; green arrows: dense infiltration ) ( Fig 2C ) . Increased leukocyte infiltration was observed Day 7 post-infant-inoculation where sites of inflammation were dense in mononuclear cells with lymphocyte-like morphology . Infected mother lungs were compared to the lungs of adult male ferrets directly inoculated with Cal/07 through the intranasal route ( Fig 2C , right column ) . The lungs collected from mothers of mock inoculated infants were similar in architecture to that of mock inoculated adult male ferrets . The adult lungs collected Day 3/4 post-direct-inoculation had markedly more leukocyte infiltrates compared to the mother lungs at Day 3/4 post-infant-inoculation ( Fig 2C ) . Taken together with the above data , these findings indicated that 2009 H1N1 transmission from infant to mother led to severe respiratory disease with upper and lower respiratory tract infection and accompanying lung pathology but on a delayed time course compared to direct adult infections . To determine if influenza infected nursing-mother ferrets could transmit the virus to their feeding-infants , we reversed the inoculation/infection design as shown in the schematic ( Fig 3A ) . We intranasally inoculated nursing-mothers with Cal/07 at 105 EID50 and observed clinical symptoms as well as determined respiratory viral load and pathogenesis . Following inoculation , nursing-mother ferrets developed a biphasic fever which spiked at 104% ( baseline temperature ) ( Fig 3B ) Day 3 post-mother-inoculation . Weight loss was observed in the inoculated mothers which reached nadir on Day 7 post-mother-inoculation at 89% of original weight ( p<0 . 05 ) ( Fig 3C ) . Although mother mortality did not occur , the infants of inoculated mothers lost weight , had increased temperature , and all died or reached cut-off by Day 11 post-mother-inoculation ( Fig 3D ) . The weight of infants of inoculated nursing-mothers began to decline on Day 3 ( Fig 3C ) . Although infants did not have a significant increase in temperature they did become hypothermic as they reached mortality ( Fig 3B ) . Virus was detected in mother NW Day 1 post-mother-inoculation ( peak Day 2 at 6 TCID50/ml ( Log10 ) ) and infant viral shedding began Day 3 ( Fig 3E ) . Live influenza virus was detected in the lungs of infants reaching weight cut-off or succumbing to illness between Day 5 and Day 8 post-mother-inoculation ( 3–8 TCID50 ( Log10 ) ) ( Fig 3F ) . Significant pathology was also detected in lungs of infants feeding from 2009 H1N1 inoculated nursing-mothers Day 7 post-mother-inoculation . The lungs were characterized by areas of dense leukocyte infiltration and alveoli destruction ( Fig 4 ) . In summary , these data indicate that influenza transmission was also possible from mother to feeding-infants causing upper and lower respiratory tract infection and respiratory tract disease . Mammary glands are a significant point of contact and possible pathogenic transmission within the mother-infant dyad [34] . To investigate the susceptibility of mammary glands to influenza infection and the mammary as a point of transmission , we determined virus presence within mammary tissue and virus shed in expressed milk as shown in the experimental design graphic ( Fig 5A ) . All mammary glands were collected from mothers of directly influenza inoculated infants on Day 4 and Day 7 post-infant-inoculation ( 3 per time point ) . Live viral load was quantified from each mammary tissue . MDCK titration assay revealed live virus in 7 of the glands ( Fig 5B ) where some viral loads reached 6 TCID50/g ( Log10 ) or higher . All mothers assessed had at least one gland positive for live influenza virus . Nipples of positive mammary glands also had significant amounts of live influenza virus on Day 4 post-infant-inoculation ( 3–7 TCID50/g ( Log10 ) ) ( Fig 5C ) . To determine if influenza virus positive mammary glands were able to shed virus in the milk expressed from the infected mammary gland , we measured both live virus and vRNA in expressed milk from mothers of infected infants . Live virus assessment in milk collected between Day 3 and Day 7 post-infant-inoculation revealed virus presence where some samples contained high virus levels between 6–7 TCID50 ( Log10 ) ( Fig 5D ) . vRNA analysis from milk collected on separate occasions showed vRNA was present in some milk samples as high as ~10 , 000 copies/5 ng totRNA . Most samples collected had between 10 and 1000 copies ( Fig 5E ) . vRNA and live virus were not detected in milk collected at baseline ( Fig 5D and 5E ) . High levels as well as moderate and low levels of vRNA were detected in infant ferret feces ( Fig 5F ) . Negligible or no amounts of vRNA were found in the peripheral blood of intranasally inoculated infants or their mothers to account for viremia contributing to severe disease or virus shedding from milk ( Fig 5G ) . Control directly inoculated adults were also negative for blood vRNA . This data suggested that mammary glands are able to be harbor live influenza virus and viral shedding can occur through milk expression . We next visualized the virus in 2009 H1N1 influenza positive mammary glands ( H1N1+ MG ) to determine virus localization and gland pathology following infection . Histopathology and virus localization in H1N1+ MG revealed destruction of mammary architecture ( Fig 6 ) . The acini of control glands had either thick epithelial cell layers indicative of active milk production ( green arrows ) or acini filled with pink proteinaceous fluid ( milk ) ( blue arrows ) ( Fig 6 ) . Virus staining was not detected in Control mammaries ( top right panel ) . At Day 4 post-infant-inoculation , the glands of nursing-mothers had retained tissue architecture although leukocyte infiltration ( yellow arrows ) was identified . Foci of virus staining ( light brown in color ) was also observed ( Day 4 post-infant-inoculation ) . By Day 7 , marked increases in leukocyte infiltration ( yellow arrows ) in and surrounding the lobules was noted in some mammary glands ( Fig 6 ) where the lobules of these glands had lost typical mammary acini architecture ( bottom left panel ) . Increased viral staining was seen by Day 7 post-infant-inoculation with loss of significant tissue structure ( bottom right panel ) . Viral staining was more pronounced in epithelial cells ( black arrows ) ( semi-quantitative analysis ) . These data provide evidence that the influenza virus is capable of infecting cells of the mammary gland . To uncover the molecular mechanisms of influenza infection within the mammary glands we performed gene expression analysis on RNA isolated from H1N1+ MGs . Global gene expression analysis by hierarchical clustering using Pearson correlation revealed increased expression of innate immune and cell remodeling genes , as well as downregulation of metabolism and lymphocyte activation genes ( Fig 7A ) . Kyoto Encyclopedia of Genes and Genomes ( KEGG ) Pathway analysis showed pronounced alterations in signaling gene expression in H1N1+ MGs ( Fig 7B; Ingenuity Pathway Analysis S2 Fig , Gene Enrichment Scores S2 Table ) . Jak-STAT ( Janus Kinase-Signal Transducers and Activators of Transcription ) signaling was prominently affected , highlighted by increased STAT3 and involution signaling ( STAT3 , PIAS4 , and SOCS5 ) . Corresponding decreases were seen in STAT5A/B milk production and STAT associated gene networks PRL ( Prolactin ) , STAT5A/B , SOCS1 ( Suppressors of Cytokine Signaling 1 ) , JAK3 , EPO ( Erythropoietin ) , and EPOR ( Erythropoietin Receptor ) ) [35–38] . As well as PRL , other genes involved in milk production [39] , LPO ( Lactoperoxidase ) , LPL ( Lipoprotein lipase ) , ATP2B2 ( ATPase for Ca2+ transport during milk production ) , SLC34A2 ( protein component of milk ) , LALBA ( alpha-lactalbumin ) , and CSN2 ( β-casein ) , were also significantly downregulated ( Fig 7B ) . Cancer Related and Cell Cycle genes including Wnt , p53 , and TGFβ signaling pathways , as well as cell attachment ( Focal Adhesions and Adherens Junctions ) gene networks , were regulated in H1N1+ glands . Significant expression of M2 macrophage genes were found including CD163 , CLEC7A ( C-type lectin domain family 7 , member A ) , MSR1 , CD209 , LGMN ( Legumain ) , and MRC1 ( Mannose receptor C type 1 ) transcripts ( Fig 7B ) . MMP1 , MMP2 , MMP8 , MMP11 , and MMP14 matrix metalloproteases were also increased along with collagen genes COL5A2 , COL3A1 , COL1A1 , COl1A2 , COL4A1 , COL6A3 , and COL4A6 ( Fig 7B and S2 Fig ) . Upregulation of Integrins ( ITGA4 , ITGB5 , ITGB1 , ITGB6 ) and other genes involved in Focal Adhesions and Adherens Junctions ( CAV1 ( Calveolin 1 ) , RAP1A , PAK1 ( p21 Activated Kinase ) , ROCK1 ( Rho Kinase ) , VAV1 ( guanine nucleotide exchange factor ) , WASL ( Wiskott-Aldrich Syndrome-Like ) , SPPL1 ) was also noted ( Fig 7B and S2 Fig ) . Gene networks involved in Cell Cycle and Cell Proliferation/Stress were also prominent . These included MAP Kinase associated protein genes ( MAP2K3 , MAP2K6 , MAPK8 , MAPK13 , MAPK1 , MAP2K1 , MAP2K4 , JUN , and FOS ) and Cyclin regulation genes ( CDC16 , CD4 , CDKN2D , CDKN2D , CDKN1A , CDC26 , CDK7 , CDC45 ) ( S2 Fig ) . Many of these genes have known roles in cancer gene networks such as the proto-oncogenes JUN and FOS , RAP1A ( RAS-related protein 1A ) , ITGB5 , as well as BRCA2 ( Breast cancer 2 , early onset ) and PCNA ( Proliferating cell nuclear antigen ) ( Fig 7B and S2 Fig ) . The P53 gene network included both pro- and anti-apoptotic gene upregulation ( Bax , CASP9 ( Caspase 9 ) , CASP3 ( Caspase 3 ) , PTEN ( Phosphatase and Tensin homolog ) , CDK4 , and MDM4 ( Mouse Double Minute 4 ) ) . Functional annotation analysis by KEGG classification of significantly upregulated or downregulated genes similarly suggested pronounced transcription-level changes in cellular proliferation , remodeling , and metabolic pathways in H1N1+ MG . Genes associated with cell growth , morphology , and catabolism were significantly enriched among upregulated genes while genes implicated in lipid and protein metabolism were dominant among downregulated gene subsets ( S2 Table ) . Seven signaling cascade gene classifications exhibited statistically significant enrichment among upregulated or downregulated genes for at least one time-point ( Fig 7D ) . Among upregulated genes , cell growth ( Wnt , TGF-β ) and stress response ( p53 ) signaling pathway genes were significantly enriched by Days 6/7 with a trend of increasing enrichment over time . A similar profile was observed for TLR signaling-associated genes . Conversely , genes implicated in Jak-STAT and PPAR signaling were significantly enriched among downregulated genes , with potential implications for milk production ( Fig 7D ) . Microarray gene expression was validated by qRT-PCR for 9 ferret specific primer sets including STAT3 , STAT5 , FOS , CSN2 , and CXCL10 ( S3 Fig , Ferret Primer Sequences S3 Table ) . To further validate the gene expression results , we analyzed the protein expression of prominent regulators of gene networks discovered in our data analysis . IHC analysis of STAT3 expression in control and H1N1+ MGs ( Fig 8 , left panels ) showed increased STAT3 protein expression in virus positive glands with specific increases within the nucleus and diminished cytoplasmic staining of the mammary gland cells ( shown in the inset magnified picture ) . STAT5 protein was prominent in the control tissue but decreased in the H1N1+ samples ( Fig 8 , right panels ) . Together , our gene expression profiling and validation suggests influenza virus infection may regulate milk cessation , breast involution , and oncogenic microenvironment related gene networks . Since H1N1+ MG had significant differentially regulated gene pathways , we also investigated the expression profiles of Bystander mammary glands to determine if these glands also had evidence of inflammation . We defined Bystander mammary glands to be mammary glands that were negative for virus by qRT-PCR but present on a mother who had H1N1+ MGs . It is not known if Bystander glands were previously virus infected and were able to clear the virus prior to collection and gene profiling . Parallel analyses of Bystander mammary glands ( H1N1- at time of collection ) partially recapitulated responses detected in H1N1+ MG ( S4 Fig; S4 Table ) . Both H1N1+ and Bystander gland response signatures were characterized by upregulation of proliferation , remodeling , and immune response associated genes ( S5 Table ) . Downregulation of sugar/lipid metabolism genes and STAT5A/B was also a shared feature of both H1N1+ and Bystander glands ( S4 Fig; S5 Table ) . However , Bystander gland responses differed from H1N1+ MG as the PPAR/Hedgehog/Adipocytokine pathway was downregulated and the immune responses were less robust . Catabolism , apoptosis , and TGF-β/p53-associated gene responses characteristic of H1N1+ gland responses were also less pronounced in Bystander glands ( S4 Fig ) . We next sought to define the immune response regulation within the H1N1+ MGs compared to the canonical gene pathways that have been determined in influenza infected lungs . Clustal analysis of H1N1+ MGs alongside infected lungs revealed the common upregulation of antiviral response pathways but also tissue specific regulation of other immune genes ( Fig 7C ) . Induction of genes typical of the influenza antiviral response [40] such as CXCL10 and CCL2 were seen Day 6/7 post-infant-inoculation in H1N1+ MG ( Fig 7C ) . The immune response also differed in the mammary gland compared to the lung . The response in mammary tissue was characterized by decreased expression of lymphocyte-associated genes such as CD3e , IL2Ra , CD4 , AICDA ( Activation Induced Cytidine Deaminase ) [41]; IL4 and IL1β upregulation; and unchanged IL6 expression ( Fig 7C ) . These results suggest that influenza infected mammary glands are capable of producing an antiviral and immune response that is unique compared to the immune response mounted during infection in the respiratory tract . Above we determined that lactating mammary glands are able to host the influenza virus but it was not discerned if the mammary glands were directly susceptible to influenza infection . To determine if mammary glands are able to be infected with the influenza virus infection and act as a vessel of transmission , we inoculated active glands of lactating mother ferrets with Cal/07 ( 105 EID50 ) or PBS through the lactiferous ducts ( mammary gland inoculation experimental design , Fig 9A ) . This also allowed us to investigate the direct responses of influenza infection in the mammary gland as well as the role of the mammary gland in virus transmission . Following direct inoculation with Cal/07 , mothers and their infants developed significant disease characterized by temperature increases , weight loss , and infant mortality which was not observed in the vehicle inoculation groups ( Fig 9 ) . H1N1 mammary inoculated mothers developed a significant fever at 104% of original temperature Day 2 post-mammary inoculation not seen in the mock inoculation control mothers ( Fig 9B , left panel ) . Virus inoculated mothers also lost between 7–10% of their original weight and did not recover by the end of study ( Fig 9B , right panel ) . Infants feeding on virus inoculated mammary glands also had significant weight loss with a 30% survival rate by Day 7 post-mammary-inoculation ( Fig 9C , right panel and D ) . Live virus , average 6 TCID50/ml ( Log10 ) , was detected in the NW of infants feeding from virus inoculated glands Day 4 post-mammary-inoculation ( Fig 9E ) . Virus was detected in mother NW after detection in the infants ( Day 7 pi ) . Live virus was present in expressed milk , Day 2 and Day 4 post-mammary-inoculation , between 3 and 13 TCID50/ml ( Log10 ) there by confirming successful inoculation and infection ( Fig 9F ) . As virus was detected in mother NW after in infant NW , these results suggested that infants developed respiratory infection directly from virus shed from the inoculated mammary gland . Mammary glands were stained with H&E to determine tissue architecture from direct inoculation of 2009 H1N1 and subsequent virus replication . Histopathology showed destruction of acini glandular architecture and absence of milk staining ( Fig 10A ) . Gene expression profiling by qRT-PCR revealed Cal/07 directly inoculated mammary tissues had significant decreases in the milk production genes STAT5A , STAT5B , LPL , and CSN2 ( Ferret Primer Sequences S3 Table; Fig 10B ) . The β-casein protein was decreased in milk samples collected from inoculated tissue when it was able to be collected ( S5 Fig ) . Taken together , direct mammary gland inoculation suggests influenza transmission is possible through breastfeeding and influenza virus infection in mammary tissue leads to pathogenesis and milk cessation . We next sought to determine if human breast cells were also susceptible and permissible to influenza virus infection . To this end we inoculated three cell lines of cultured human epithelial breast cells with Cal/07 strain ( 2009 H1N1 ) ( in the absence of exogenous proteases ) to visualize the virus life-cycle in inoculated cells , assess the viral kinetics , and determine cell viability post-inoculation ( Fig 11 ) . “Normal” non-tumorigenic ( MCF-10A ) and adenocarcinoma ( MCF-7 and MCDA-MB-231 ) human epithelial breast cell lines were used to eliminate single cell type biases . To visualize the virus within the cell and determine virus subcellular localization , inoculated cells were stained for Influenza A Virus ( IAV ) NP ( blue ) , nucleus ( green ) , and actin filaments ( red ) ( Fig 11A ) . Positive NP stain was used to determine if viral replication may be occurring within the nucleus . At 24 h post-inoculation influenza NP was detected in the nucleus ( white arrows ) of all three cell types , MCF-7 , MDA-MB-231 , and MCF-10A , indicated by co-localization of the nuclear stain ( green ) with NP staining ( blue ) ( Fig 11A ) . Furthermore , marked punctate NP accumulation at the plasma membrane was observed in MCF-10A cells ( yellow arrows ) and to a smaller degree in the other cell types . vRNA was significantly increased in all cells types between 3 and 24 h post-inoculation by ~10 fold ( Fig 11B ) . Cell viability analysis showed significant drops in cell viability of MCF-10A and MDA-MB-231 post-inoculation reaching ~35% viability at the 72 h time point compared to uninoculated cells at the same incubation ( Fig 11C ) . To assess productive infection , live virus was quantified from collected supernatant at each time point . Live viral titers ranged between 3 and 4 TCID50/ml ( Log10 ) for all cell types ( Fig 11D ) . Baseline control ( BC ) wells had minimal or no detectable live virus . Together , these results suggest that the 2009 H1N1 virus was able to enter “normal” human breast cells leading to virus replication and productive infection .
Here we investigated influenza virus transmission within the mother-infant dyad using a novel ferret model . We found the influenza virus transmitted between nursing-mothers and infants in a bidirectional manner by respiratory and mammary transmission mechanisms . Transmission led to severe lower respiratory disease and mortality . Ferret mammary glands as well as human breast cells were both susceptible to infection and were able to replicate the virus efficiently suggesting mammary glands have the potential to function as a transmission vessel . Substantial changes in gene regulation were observed in H1N1 positive mammary tissue including induction of milk cessation genes and oncogenic pathways accompanying tissue pathogenesis . Influenza has caused high mortality in children ( >280/season ) and pregnant/postpartum women ( ~24% ) during the influenza season in the United States [8 , 10] and our study may be used for future investigations of immune responses and therapeutic testing in these populations . Infants and toddlers are infected with 8 to 10 respiratory viruses ( such as RSV ) as well as other common viruses ( rotavirus , enteroviruses ( EV71 ) ) each year [24–27 , 42] . Our findings support investigation into the transmission dynamics of influenza and other childhood viruses during breastfeeding . Since retrograde flux of the mammary glands can allow liquid from infants to enter mammary ducts , viral transmission from the infected infants into the breast is possible via breastfeeding . The susceptibility of mammary glands to common pathogens may have both beneficial and deleterious long-term consequences which should be considered in future studies . As well , since vRNA was found in infant feces as well as in infant respiratory tracts , both respiratory and gastrointestinal introduction of the influenza virus are possible as routes of infection from mother to infant . Our gene expression profiling in virus positive mammary glands indicated induction of oncogenic pathways as well as immune responses [19–22] . Infection in mammary tissue may also evoke effective long-term adaptive immune responses . This may be a response conserved through evolution to serve as an advantage for future fecundity of that mother enabling protection by immune memory . We found live virus was present in milk and within the mammary glands of mothers feeding 2009 H1N1 virus inoculated infants . Since live virus was shed in the milk , this suggested that infection may also be possible by contact with mother’s milk . Although we showed 2009 H1N1 virus presence in the mammary gland by four independent methods , immunohistochemistry , tissue viral load assay , milk viral load assay , and qRT-PCR , the kinetics of the virus expression in these assays were not always correlative . Since the milk vRNA and milk live viral load data were not aliquots of the same sample it is possible the amount of virus shed in milk may not be regular throughout the day or in each infection . RNA extraction from milk samples presented challenges due to the fat content of the milk which were not encountered in live viral load assessment of either milk or mammary gland tissue which may explain the higher live viral loads . As well , milk viral quantifications may differ from that of the mammary gland tissue viral quantification since virus in the milk may be diluted or concentrated dependent on the fullness of the mammary gland at the time of milking . Although these data did not directly correlate , the presence of virus in milk and within the mammary glands was a consistent finding . To investigate the transmission potential through breastfeeding and the direct role of the mammary gland , we conducted direct virus inoculations to the mammary gland . By directly inoculating the mammary gland , we were able to eliminate the possibility of respiratory transmission in the initial portion of the experiment by isolating the infection within the mammary . Controlling the experiment through other means such as the addition a non-lactating adult to the cage of mother and infants brings ethical considerations as the presence of another adult would put the infant ferrets in danger . In our inoculation experiments , virus began shedding in milk Day 2 post-mammary-inoculation and was present in the infant NW between 2 and 4 days post-mammary-inoculation . Since virus was not detected in mother NW until later in the time course , this data suggested that the infants received the virus from infected mammary glands , not through the mother’s respiration . Virus transmitted from the mammary gland led to severe disease and infant mortality despite the atypical influenza transmission mode . Since infants are susceptible to respiratory tract infection by various microbes present in mammary glands/milk , such as bacteria and yeast [43–46] , our findings showing respiratory infection from pathogens present in breast milk may have implications into possible sources of bacteria that may participate in influenza-bacterial co-infections in neonates . Moreover , influenza infection in mammary tissue may exacerbate or increase mastitis incidence in mothers and should be investigated in relation to resident and foreign microbial populations . Together , these results give insight into mechanisms which may contribute to influenza severity in infants and nursing-mothers . The mammary gland is a unique organ which undergoes several structural and molecular transformations throughout the female’s life-cycle , i . e . puberty , pregnancy and postpartum . Pregnancy associated breast cancer ( PABC ) has been hypothesized to be a consequence of breast remodeling that occurs during pregnancy-lactation cycles and mammary involution [17 , 18] . We showed increases in oncogene pathways and involution programing in influenza positive glands . Breast involution is characterized by the presence of proteolytic cleavage , apoptosis , and leukocyte recruitment ( i . e . , macrophages ) for secretory alveolar cell removal [17 , 47] . Our gene expression analysis implicated significant presence of M2 macrophages in influenza infected mammary glands by the upregulation of the M2 specific transcripts CD163 , CLEC7A , M5R1 , CD209 , LGMN , and MRC1 . M2 macrophages have a similar phenotype as tumor associated macrophages ( TAM ) , since they regulate MMPs and support a microenvironment favorable for oncogenesis [47] . In agreement with this profile we showed increased MMP1 , MMP2 , MMP8 , MMP11 and MMP14 in the H1N1+ MGs . We also found oncogenic collagen and cell attachment gene signatures upregulated in our data: COL5A2 , COL3A1 , COL1A1 , COL1A2 , and COL4A6 , Integrins ( ITGB6 ) and cell-matrix interaction proteins ( SPP1 ) [17] . Several of these collagen genes have previously been shown to be associated with both cancer progression and involution in mouse models of mammary involution [17 , 48 , 49] . As further validation and discovery , future studies should investigate the full transcriptomics during influenza virus infection in active mammary glands of the ferret to eliminate any biases of the canine microarray . Although no structural or tissue pathological evidence for cancer progression was found in our studies possibly due to the short time frame of the study , clear tissue evidence of milk cessation was seen . Viruses such as HPV have been shown or speculated to be the etiological agent of specific cancers [50] including breast cancer [19 , 51] . Breast cancer is the most common cancer affecting women worldwide [52] and viral nucleic acids have been found in breast tumors i . e . , bovine leukemia virus ( BLV ) and mouse mammary tumor virus ( MMTV ) [19 , 51] . Viruses may instigate cancer progression through oncogenic viral proteins or by induction of chronic inflammation [19 , 53 , 54] . We found induction of genes typical of influenza antiviral responses ( CXCL10 ) [40] as well as cancer associated genes upregulated in H1N1+ MG suggesting mammary glands . As our expression analysis showed the induction of a cancer-promoting microenvironment in mammary glands infected with the influenza virus , it is important to note that viruses that have been found to be directly oncogenic , such as HPV , have infectious cycles differing from that of the influenza virus [23] . Therefore we hypothesize if long-term biological networks are upregulated during influenza infection in breast tissue then those genetic changes would most likely be due to inflammation . The long-term implications of pathogen infection in breast tissue are not known but our data suggests influenza infection leads to short-term pathogenic genetic reprograming within mammary glands . The CDC recommends continued breastfeeding in the event of infant influenza infection [55] . Although the importance of breastfeeding cannot be overstated [11 , 12] , our findings may impact the management of influenza infection in the mother-infant dyad . Reports have shown most pregnant women with severe influenza infection did not receive the yearly influenza vaccine [10] . Our data reinforces the importance of seasonal influenza vaccination in pregnant and breastfeeding women . Our investigation also suggests that continued breastfeeding during mother or infant influenza infection could lead to breast infection , live virus shedding , and breast pathogenesis . Short-term milk expression by breast pumping with possible pasteurization may be an appropriate strategy for management of influenza infection in the mother-infant dyad . Previously , a guideline has been investigated for the management of influenza transmission in the mother-infant dyad . Since these guidelines had only considered the possibility of respiratory transmission , further investigation and guideline development may be important [56] . Although not much is known regarding viral transmission through breastfeeding , great efforts have been devoted to modeling mother-to-child HIV transmission due to the importance of breastfeeding in HIV high incidence countries [57] . HIV in breast milk exists in both cell-associated ( leukocytes ) and cell-free forms [34] . Our data suggests that the influenza virus infects glandular epithelial cells whereas HIV primarily targets leukocytes leading to systemic circulation . Our observation here implies novel dynamics of influenza transmission through breastfeeding is a mechanistically different mode of virus transmission in the mother-infant dyad and a formally unidentified system of immune response regulation within the mammary gland for the local production and direct delivery of pathogen specific antibodies . This suggests there is much to be understood regarding the mother-infant relationship and pathogen transmission . Recently , MERS-CoV has been speculated to be transmitted from camels to humans through consumption of camel milk [58] . Together these findings support a hypothesis that respiratory viruses may have the ability to also infect mammary tissue , including human breast cells , possibly due to a shared branched architecture and cellular structure of epithelial cells in lungs and mammary glands .
All animal work was conducted in strict accordance with the Canadian Council of Animal Care ( CCAC ) guidelines . The protocol license number AUP 1031 was assigned by the Animal Care Committee of the University Health Network ( UHN ) . UHN has certification with the Animals for Research Act including for the Ontario Ministry of Agriculture , Food and Rural Affairs , Permit Number: #0132–01 and #0132–05 , and follows NIH guidelines ( OLAW #A5408-01 ) . The animal use protocol was approved by the UHN Animal Care Committee ( ACC ) . All efforts were made to minimize animal suffering and infections and sample collections were performed under 5% isoflurane anesthesia . The 2009 H1N1 virus strain , A/California/07/2009 ( Cal/07 ) , in chicken egg allantoic fluid was provided by the Influenza Reagent Resource , Influenza Division , WHO Collaborating Center for Surveillance , Epidemiology and Control of Influenza , Centers for Disease Control and Prevention , Atlanta , GA , USA . TCID50 and EID50 determinations were done as previously described [59] . All virus work was performed in a BSL-2+ facility as previously described [29] . Four-week-old-male and female ferrets , female Jill ferrets ( aged 5 months to 1 year ) and male ferrets 8 months old ( adult ) were bred in an on-site SPF ferret colony ( University Health Network , Toronto , ON , Canada ) . Ferrets were shown to be seronegative by haemagglutination inhibition ( HI ) assay against currently circulating influenza A and B strains before infection . Infant ferrets were housed in litters with nursing-mother ferrets . Litters were housed according to UHN ferret colony specifications . Ferrets were maintained and monitored in litters or as previously described ( adults ) [29 , 32] . Briefly , prior to inoculation , 4-week-old nursing ferret litters were randomly selected and housed in appropriate litter boxes contained in bioclean portable laminar-flow clean-room enclosures ( Lab Products , Seaford , DE , USA ) in the BSL-2+ animal holding area . Baseline body temperature and weight were measured on Day -1 and 0 for each animal . Temperatures were measured by using a subcutaneous implantable temperature transponder ( BioMedic Data Systems , Inc . , Seaford , DE , USA ) for mothers and adults . Infant temperature was measured rectally by DT-610B Single Input K-Type Thermometer ( ATP Instrumentation , Leicestershire , United Kingdom ) . Nasal washes from the inoculated ferrets were collected on specified days post-inoculation in nasal wash buffer ( 1% BSA and 100 U/ml penicillin , 100 μg/ml streptomycin in PBS ) as previously described [29] . One ml ( adults ) or 500 μl ( infants ) of nasal wash buffer was used for each collection . Penicillin and streptomycin were obtained from Invitrogen Canada ( Burlington , ON , Canada ) and BSA was from Wisent Inc . ( Saint-Bruno , QC , Canada ) . For milk collection , mammary glands were cleaned and a warm press was applied to increase the easy of milking the gland . Milk was collected and pooled from all mammary glands of one mother . Milk for vRNA and live viral load assessments were collected at different milking sessions . Milk was stored in RLT Buffer or directly frozen at -80°C . Tracheas , lungs , mammary glands , and nipples were collected on specified days post-inoculation from the euthanized ferrets . Prior to mammary gland collection , nipples were externally cleaned with ethanol and then the nipples were removed . Internal mammary gland tissue was harvested subsequent to nipple removal . Mother ferrets can have a variable number of active mammary glands per pregnancy/postpartum ( ~5–8 ) . Therefore , a variable number of mammary glands was assessed for viral loads per time point . As many mammary glands were run as possible to acquire the maximum amount of data . Although the number of glands was variable , the number of mother ferrets was always three ferrets per time point . Feces from infant ferrets were collected at the time of anal temperature collection . Each feces was placed directly in RLT buffer or placed directly at -80°C from the animals’ anus to avoid surface contamination . Samples were stored in -80°C before processing for viral loads or RNA . Samples for RNA were either placed in RNA later ( milk and feces ) or homogenized in TRIzol ( Tissues ) . Sample aliquots for histopathology were stored in formalin until processing . Viral replication in the upper , middle , and lower respiratory tract and mammary tissues were assessed by endpoint titration of nasal washes or homogenized tissue samples from the inoculated ferrets in MDCK cells ( TCID50 ) using haemagglutination as the readout for positive wells as previously described[29] . Briefly , nasal washes were diluted 1:10 in vDMEM ( Dulbecco's modified Eagle's medium containing 1% BSA , 25mM glucose , 1mM sodium pyruvate , 4mM glutamine , 100 U/ml penicillin , 100 μg/ml streptomycin , 50 μg/ml gentamycin , and 1 μg/ml TPCK-Trypsin ) followed by the half log serial dilution in quadruplicate with vDMEM on MDCK cells in 96-well flat-bottom plates ( SARSTEDT , Inc . , Saint-Leonard , QC , Canada ) . Tissues were processed similarly and homogenized 1:10 ( w/v ) in sDMEM ( vDMEM excluding 1% BSA ) on ice and centrifuged before titration of the homogenates on MDCK cells . Before inoculation , MDCK cells were maintained in the log-phase at low-passage numbers and grown in cDMEM ( DMEM containing 10% fetal bovine serum , 25 mM glucose , 1 mM sodium pyruvate , 6 mM glutamine , 1 mM non-essential amino acids , 100 U/ml penicillin , and 100 μg/ml streptomycin ) . The day before viral sample titration , 2x104 MDCK cells were seeded into each well to reach 95% confluence on the next day . After a two-hour incubation of titrated viral samples with MDCK cells at 37°C , 5% CO2 , samples were aspirated and replaced with fresh vDMEM followed by a six-day incubation at 37°C , 5% CO2 . On Day 6 post-incubation , supernatants were examined for presence of virus by haemagglutination of 0 . 5% turkey erythrocytes ( Lampire Biological Laboratories , Pipersville , PA , USA ) . The viral titers were determined as the reciprocal of the dilution resulting in 50% HA positivity in the unit of TCID50/ml represented in log10 base . Viral titers in cell culture supernatants were also determined to investigate capacity of mammary epithelial cells to produce infectious virus in vitro . Cell culture supernatants were loaded directly and serially diluted as described above . Assay upper and lower limits of detection depended on sample availability and/or expected viral titer and varied between experiment types . Lower limits of detection are indicated in all viral load figures by a dashed line . Upper limits of detection were 106 . 75 TCID50/ml for mammary epithelial cell supernatant assessments , 107 . 75 and 1013 . 75 for milk assessments in Fig 5D and Fig 9F , respectively , and 107 . 25 for all nasal wash and tissue assessments except for S1C Fig ( 107 . 75 ) . Cell culture reagents were obtained from Invitrogen Canada or Wisent except for TPCK-Trypsin ( Sigma-Aldrich Canada Ltd . , Oakville , ON , Canada ) . Animals were euthanized on appropriate days and the lung or mammary gland tissues were harvested , perfused and fixed in formalin followed by paraffin embedding and sectioning as described [29] . Tissue slides were then stained with hematoxylin and eosin for histopathology assessment . Nursing-mothers of mock-inoculated 4-week-old infants were used for health and age control . Immunostaining was performed using a polyclonal goat anti-influenza antibody ( 1:2000 ) ( Abcam ( ab20841 ) , Cambridge , United Kingdom ) . STAT3 protein was detected with a rabbit polyclonal anti-STAT3 ( LSBio , Cat # LS-B4693 ) using CIT6 Antigen Retrieval at 1:300 primary antibody dilution for 1 hour incubation . STAT5 protein was detected with a rabbit polyclonal anti-STAT5 ( Abcam , Cat # ab68465 ) using TE9 Antigen Retrieval at a concentration of 1:100 primary antibody dilution and a 1 hour incubation . High resolution scans were performed using an Aperio ScanScope XT , Leica Biosystems , Nußloch , Germany . Milk was expressed from the mammary glands of lactating mother ferrets . Ten μg of milk was load per lane and proteins were separated by SDS-PAGE and transferred to PVDF membrane . To visualize beta-casein protein , the membrane was blotted with 1:500 rabbit polyclonal anti-bovine beta-casein ( Cat# 251309 , Abbiotec ) and followed with 1:2000 anti-rabbit IgG-HRP ( Santa Cruz sc-2030 ) . To ensure equal loading of each sample , a protein estimation was conducted using a Pierce BCA Protein Assay Kit ( Life Technologies ) . Proteins were detected with a chemiluminescence protocol and exposed . Images were exposed to Thermo Scientific CL-XPosure Film ( Waltham , MA , USA ) and developed using a Konica Minolta Model SRX-101A ( Tokyo , Japan ) machine . Collected lung and mammary tissue was placed directly in TRIzol reagent ( Life Technologies , Burlington , Ontario ) for immediate homogenization and subsequently processed according to the manufacturer’s instructions as previously done [32] . Milk RNA was extracted using an RNEasy Kit ( Qiagen ) . Fecal RNA was extract using QIAshredder ( Qiagen ) before subsequent processing by RNEasy Kit ( Qiagen , Hilden , Germany ) . Total RNA was converted into cDNA using ImProm-II reverse transcription system ( Promega Inc . , Madison , WI , USA ) followed by qRT-PCR . qRT-PCR was performed on the ABI Prism 7900HT system ( Applied Biosystems , Foster City , CA , USA ) . Blood was collected in PAXgene Blood RNA tubes followed by extraction using a PAXgene Blood RNA Kit ( PreAnalytiX , Hombrechtikon , Switzerland ) , per the manufacturer’s instructions . Quadruplicate PCR reactions were run for specified target genes , each containing 4 μl 1 . 25 ng/μl cDNA , 0 . 5 μl 4μM forward and reverse primers , and 5 μl SYBR green PCR master mix ( Applied Biosystems Inc . , Carlsbad , CA , USA ) . PCR reactions were run at an annealing temperature of 60°C for 35 cycles for blood , tissue , and cell lysates . PCR reactions for milk and fecal RNA were done at 60°C for 40 cycles due to the difficulty of extracting RNA from milk and feces . Applied Biosystems Sequence Detection Systems Version 2 . 4 software was used for raw data collection ( Applied Biosystems , Foster City , CA , USA ) . Target gene expression levels were normalized to house-keeping gene β-actin , and quantified by the relative standard curve method . RNA extracted from mammary glands of nursing-mothers ( 2009 H1N1 and mock inoculated infants ) and influenza infected adult lungs were subjected to gene expression analysis by microarray . Mammary glands were determined to be virus infected glands if qRT-PCR found vRNA >10 copies per 5 ng of RNA and histological evidence of virus infection . Mammary glands were determined to be Bystander glands if vRNA was undetectable by qRT-PCR . Total cellular RNA from tissues was collected and purified using TRIzol reagent and methods , as described above . Complementary RNA ( cRNA ) was generated using 3’ IVT Express Kit ( Affymetrix , Santa Clara , CA , USA ) and microarray analysis was performed using Affymetrix GeneChip Canine Genome 2 . 0 Array ( Affymetrix , Santa Clara , CA , USA ) , as previously described [40] . We have previously used and validated canine arrays for the investigation of ferret global gene expression profiling [60–64] . We demonstrated high levels of homology ( average of 89% identity ) between 30 canine and ferret nucleotide sequences [60] . Data sets for H1N1+ mammary glands , bystander mammary glands , and lungs were pre-processed independently with quantile normalization , variance stabilization , and log2 transformation . For genes represented by multiple probes on the array , the probe with highest total signal in H1N1+ mammary gland and lung datasets were selected for analysis . Two parallel analyses were applied A H1N1+ and bystander mammary gland datasets . Global clustering analyses were performed by one-way hierarchical clustering ( Pearson’s correlation ) of all significantly differentially regulated genes ( p-value<0 . 05 Student’s t-test unpaired , equal variances , fold change ≥ |1 . 5| fold ) and functional annotation of generated clusters . Global pathway gene enrichment analyses were performed by functional annotation of genes exhibiting significant changes in expression at either Days 3/4 or 6/7 in either dataset , using the KEGG Pathway database . DAVID Bioinformatics Resource v6 . 7 , described previously [40] was used for functional annotation and enrichment score profiling . Clustergrams in Fig 7B were manually selected as representative genes for signaling pathways and gene networks prominently represented in global analyses . Genes included in clustergrams were identified as significantly differentially regulated members of related KEGG-defined signaling pathways with the exception of Macrophage and Milk Production clustergrams which were generated by manual selection of representative genes . Full clustergrams of all significantly differentially regulated genes for each KEGG-signaling pathway identified in Fig 7 are included in S2 Fig Accompanying data can be found in the Supplementary Materials . Raw ( . CEL files ) and normalized microarray data were compiled in accordance with Minimum Information About a Microarray Experiment ( MIAME ) guidelines and uploaded to the public data repository Gene Expression Omnibus ( GEO ) for public dissemination ( www . ncbi . nlm . nih . gov/geo/ ) . These datasets can be found under the accession numbers GSE63082 ( mammary gland ) , GSE63083 ( lung ) and GSE63084 ( both ) at http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE63084 . In vitro studies were performed similarly as reported previously [65] . MCF-10A , MFC-7 , and MDA-MB-231 cells were purchased from ATCC . The cells were seeded at 50 , 000 cells/well in 6 well plates for RNA and viral load analysis . Cells were seeded at 75 , 000 cells per well in Nunc Lab-Tek II Chamber Slide System , 2 well glass slides ( Thermo Scientific ) for confocal microscopy . Cells were inoculated with A/Cal ( H1N1 ) at a dose of 104 ( ELISA assay calculation for MO1 = 1 ) TCID50 ( determined by MDCK titration ) per 104 cells ( 106 . 5 TCID50 HA assay calculation ) . For confocal , cells were fixed in 4% PFA in PBS at 24 hours post-inoculation and stained for filamentous actin using Phalloidin ( Molecular Probes ) , DNA using SYTOX green ( Molecular Probes ) , and influenza A virus NP protein using a mouse monoclonal [AA5H] to Influenza A Virus Nucleoprotein ( Abcam; ab20343 ) at 1:100 for 60 min . This was followed by staining with Alexa Fluor 647 Donkey Anti-Mouse secondary ( Jackson ImmunoResearch ) . Prior to staining cells were permeablized using Triton X-100 in PBS . Slides were imaged by confocal microscopy using a Zeiss LSM 710 NLO , using objective C-Apo 63x/1 . 4 NA ( oil ) . For RNA and viral load , cell lysates and supernatants were collected at 3 , 24 , 48 , and 72 h post-inoculation for RNA extraction and live viral load quantification , respectively . The baseline control was determined by incubating the inoculum in assay wells without cells for 72 h . to assess virus decay and viral attachment to the wells . qRT-PCR and viral load were performed as described in above methods section . Unpaired , unequal variance , two-tail Student’s t-test or one-way ANOVAs were conducted . The Log-rank ( Mantel-Cox ) Test was used to determine significant differences in animal survival . A p value of ≤ 0 . 05 was considered statistically significant .
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Influenza is known as a respiratory infectious disease . Breastfeeding allows for frequent microbial exchange between infant and mother . Although infants , pregnant women and breastfeeding mothers are more susceptible to severe respiratory disease following influenza virus infection , the mechanisms of disease severity in infants and mothers is poorly understood . We were interested in understanding the immune responses , pathogenicity , and transmission dynamics in the infant-mother system . With this aim we developed an infant-mother ferret influenza model . Influenza infection in infants led to virus transmission to mothers causing severe disease and mortality . Unexpectedly , influenza-infected infant ferrets transmitted the virus to mother mammary glands leading to live influenza virus in expressed breast milk . Gene regulation analysis in the mammary gland showed reduction of milk production genes such as Prolactin and increased genes involved with breast shutdown . Genes associated with cancer development were significantly increased including JUN , FOS and BRCA2 . We found human breast cells were able to be infected with the influenza virus suggesting the importance of our results to human health . Our data suggests influenza transmission can occur through the mammary glands initiating immunological and pathological events contributing to influenza disease severity impacting infant and maternal health .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Influenza Transmission in the Mother-Infant Dyad Leads to Severe Disease, Mammary Gland Infection, and Pathogenesis by Regulating Host Responses
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Inorganic arsenic ( iAs ) is a carcinogen , and exposure to iAs via food and water is a global public health problem . iAs-contaminated drinking water alone affects >100 million people worldwide , including ~50 million in Bangladesh . Once absorbed into the blood stream , most iAs is converted to mono-methylated ( MMA ) and then di-methylated ( DMA ) forms , facilitating excretion in urine . Arsenic metabolism efficiency varies among individuals , in part due to genetic variation near AS3MT ( arsenite methyltransferase; 10q24 . 32 ) . To identify additional arsenic metabolism loci , we measured protein-coding variants across the human exome for 1 , 660 Bangladeshi individuals participating in the Health Effects of Arsenic Longitudinal Study ( HEALS ) . Among the 19 , 992 coding variants analyzed exome-wide , the minor allele ( A ) of rs61735836 ( p . Val101Met ) in exon 3 of FTCD ( formiminotransferase cyclodeaminase ) was associated with increased urinary iAs% ( P = 8x10-13 ) , increased MMA% ( P = 2x10-16 ) and decreased DMA% ( P = 6x10-23 ) . Among 2 , 401 individuals with arsenic-induced skin lesions ( an indicator of arsenic toxicity and cancer risk ) and 2 , 472 controls , carrying the low-efficiency A allele ( frequency = 7% ) was associated with increased skin lesion risk ( odds ratio = 1 . 35; P = 1x10-5 ) . rs61735836 is in weak linkage disequilibrium with all nearby variants . The high-efficiency/major allele ( G/Valine ) is human-specific and eliminates a start codon at the first 5´-proximal Kozak sequence in FTCD , suggesting selection against an alternative translation start site . FTCD is critical for catabolism of histidine , a process that generates one-carbon units that can enter the one-carbon/folate cycle , which provides methyl groups for arsenic metabolism . In our study population , FTCD and AS3MT SNPs together explain ~10% of the variation in DMA% and support a causal effect of arsenic metabolism efficiency on arsenic toxicity ( i . e . , skin lesions ) . In summary , this work identifies a coding variant in FTCD associated with arsenic metabolism efficiency , providing new evidence supporting the established link between one-carbon/folate metabolism and arsenic toxicity .
Exposure to inorganic arsenic ( iAs ) through consumption of contaminated drinking water is a major global health problem . Over 130 million individuals worldwide are exposed at levels >10 μg/L , including ~50 million in Bangladesh , where natural contamination of ground water is a well-known public health issue [1] . Arsenic is a human carcinogen [2] , and chronic exposure to iAs through drinking water exceeding 50–100 μg/L is associated with various types of cancer in multiple populations [3 , 4] including the United States [5] . Arsenic exposure has also been linked to diabetes [6] , cardiovascular disease [7] , non-malignant lung disease [8] , and overall mortality [9] . Arsenic-induced skin lesions are an early sign of arsenic exposure and toxicity [10] and are a risk factor for subsequent cancer [11] . Once absorbed into the blood stream , iAs can be converted to mono-methylated ( MMA ) and then di-methylated ( DMA ) forms of arsenic , with methylation facilitating the excretion of arsenic in urine [12] . This metabolism is believed to occur primarily in the liver [13] . The relative abundance of these arsenic species in urine ( iAs% , MMA% , DMA% ) varies across individuals and represents the efficiency with which an individual metabolizes arsenic . Arsenic metabolism is influenced by lifestyle and demographic factors [14] , as well as inherited genetic variation . Prior genome-wide association ( GWA ) [15 , 16] , linkage [17] , and candidate gene studies [18] have shown that variation in the 10q24 . 32 region near the AS3MT gene ( arsenite methyltransferase ) influences arsenic metabolism efficiency , with two independent association signals observed in this region among exposed Bangladeshi individuals . These metabolism-related single nucleotide polymorphisms ( SNPs ) appear to impact the production of DMA ( not the conversion of iAs to MMA ) [14] , and DMA%-increasing alleles are also associated with reduced risk for arsenic-induced skin lesions via a SNP-arsenic ( i . e . , gene-environment , GxE ) interaction [16] . Other than 10q24 . 32/AS3MT , we currently know of no other regions of the human genome that contain variants that show robust and replicable evidence of association with arsenic metabolism efficiency [14] , although studies of heritability suggest that additional variants are likely to exist [19 , 20] . In order to identify additional genetic variants that influence arsenic metabolism efficiency , we conducted a whole-exome study of associations between nonsynonymous , protein coding variation and arsenic metabolism efficiency .
Using DNA from individuals participating in HEALS ( Health Effects of Arsenic Longitudinal Study ) , we conducted exome-wide association analyses for each of the three major arsenic species measured in urine , using percentages of total arsenic as our primary phenotypes ( iAs% , MMA% , and DMA% ) . For this analysis , we restricted to 1 , 660 genotyped HEALS participants ( among 2 , 949 HEALS participants with Illumina exome array data ) with available data on arsenic species in urine . After SNP QC ( see methods ) , we had data on 19 , 992 variants with MAF >1% , and ~90% of these were missense variants . Among these SNPs , rs61735836 ( chr21:47572637 based on hg19 ) showed a clear association with all three arsenic species percentages ( Fig 1A–1C ) . P-values for this association were P = 8x10-13 for iAs% , P = 2x10-16 for MMA% , and P = 6x10-23 for DMA% . The minor allele ( A ) was associated with decreased DMA% and increased MMA% and iAs% ( Fig 1D–1F ) , consistent with the directions of association previously observed for SNPs in the AS3MT region . Results for all 19 , 992 variants are in Supporting Files S1-S3 . Like AS3MT , this association was most relevant to the second methylation step , as it showed a strong association with the secondary methylation index ( SMI = DMA/MMA ) , but not the primary methylation index ( PMI = MMA/iAs ) ( S1 Table ) . Similarly , after applying principal components ( PC ) analysis to arsenic species percentages as previously described [14] , rs61735836 showed strong association with PC1 ( representing production of DMA ) but not PC2 ( representing conversion of iAs to MMA ) ( S1 Table ) . Individuals carrying two minor alleles ( AA ) as compared to one ( AC ) appear to have even lower DMA% , suggesting a potential additive effect of the A allele; however , our sample size of minor allele homozygotes was small ( n = 12 ) , limiting our ability to examine differences between these two groups ( S1 Table ) . The association of rs61735836 with arsenic species was similar across groups stratified by sex and age ( S2 Table ) , and rs61735836 did not show evidence of interaction with either of the AS3MT SNPs previously identified in this population ( rs9527 and rs11191527 ) in relation to DMA% or skin lesions status ( S3 Table ) . The probe intensity data for rs61735836 is shown in S1 Fig , with very distinct clusters indicating high-quality data for this SNP . We then conducted exome-wide association analyses of arsenical skin lesion status ( the most common sign of arsenic toxicity ) using data on 2 , 401 cases and 2 , 472 lesion-free controls ( from both HEALS and BEST , the Bangladesh Vitamin E and Selenium Trial ) . While there was no notable departure from the expected null distribution , the low-efficiency allele for FTCD SNP rs61735836 ( A ) was associated with increased skin lesion risk ( per allele OR = 1 . 25; P = 5x10-4; risk allele carrier OR = 1 . 35 , P = 1x10-5 ) ( S2 Fig ) . Results for all 19 , 992 variants are in S4 File . This observation is similar to what has been observed for metabolism-related variants in the AS3MT region and suggests rs61735836 impacts arsenic toxicity risk through its impact on arsenic metabolism efficiency . In this manner , this variant would be expected to reduce urinary arsenic elimination and thereby increase the internal or biologically effective dose of arsenic . The MAF for rs61735836 was 0 . 077 in our data , highly consistent with the MAFs of 0 . 064 and 0 . 079 observed in the 1 , 000 Genomes Project ( 1KG ) Bangladesh ( BEB ) population and South Asian ( SAS ) super-population , respectively . The MAF for this variant is less than <21% in all human populations with available data in the Geography of Genetic Variants browser [21] and is most common in East Asian populations ( S3 Fig ) . After combining our exome array results with our previously reported GWA results for genome-wide SNPs [15 , 16] ( HumanCytoSNP-12 array imputed to ~8 . 2 million SNPs using 1KG phase 3 v5 ) , we observed that rs61735836 is the only variant in this region showing strong evidence of association ( Fig 2 ) . This is consistent with the observation that rs61735836 is not in linkage disequilibrium ( LD ) ( r2>0 . 1 ) with any nearby variant in 1KG South Asian ( SAS ) populations . This SNP is in mild LD with nearby variants in the 1KG African ( AFR ) super-population ( r2~0 . 27 ) ( S4 Fig ) , with the strongest LD observed in the ESN ( Esan in Nigeria ) population ( r2 = 0 . 43 with rs184976755 ) . SNP rs61735836 was not genotyped in our prior GWA study [15 , 16] , and therefore could not be imputed due to the lack of LD with nearby variants . Among the 5 additional exonic variants in FTCD that passed QC ( all missense ) , none showed association with any of our arsenic species measures ( P>0 . 01 ) . Using data from HEALS , we tested rs61735836 for evidence of interaction with baseline arsenic exposure in relation to risk for arsenic-induced skin lesions ( which were primarily incident lesions diagnosed after baseline ) . As an exposure measure , we used the arsenic concentration measured in the drinking well that each individual reported as their primary water source at baseline ( prior to arsenic mitigation efforts in the HEALS cohort [22] ) . A test of multiplicative interaction produced a non-significant sub-multiplicative interaction estimate ( OR = 0 . 86 , P = 0 . 42 ) , while a test of additive interaction produced a non-significant supra-multiplicative interaction ( RERI = 0 . 49; P = 0 . 10 ) ( Table 1 ) . To further assess the impact of rs61735836 on arsenic metabolism , we obtained data on arsenic species in blood ( as opposed to urine ) for 155 of our genotyped HEALS cohort members . These HEALS participants had existing data on arsenic metabolites in blood due to their participation in additional arsenic-related studies focused on folic acid and/or creatinine supplementation [23 , 24] and oxidative stress [25] . Consistent with our observed association with arsenic species in urine , the minor allele of rs61735836 ( A ) showed evidence of association with decreased DMA% ( P = 0 . 02 ) , increased MMA% ( P = 0 . 41 ) , and increased iAs% ( P = 0 . 02 ) , with arsenic species measured prior to any intervention ( S4 Table ) . Among these 155 participants , 109 also had data on arsenic species in blood collected 12 weeks after the start of a supplementation intervention . Under the assumption that the interventions do not modify the impact of rs61735836 on arsenic metabolism efficiency ( an assumption we make with considerable uncertainty ) , we can also examine these associations using these post-intervention measures . Using a mixed-effects model to analyze data from both time points , we observed that the A allele is associated with decreased DMA% ( P = 0 . 005 ) , increased MMA% ( P = 0 . 01 ) , and increased iAs% ( P = 0 . 15 ) ( S4 Table ) , consistent with results based on arsenic species measured in urine . SNP rs61735836 resides in exon 3 of FTCD ( Formiminotransferase cyclodeaminase ) , a gene predominantly expressed in liver [26 , 27] ( S5 Fig ) , the tissue in which the majority of arsenic metabolism is believed to occur [13] . FTCD codes for a 541-amino-acid protein that forms a homo-octameric enzyme involved in histidine catabolism . SNP rs61735836 codes for a valine to methionine substitution at codon 101 ( p . Val101Met ) ( Fig 3 ) . The major ( G ) and minor ( A ) alleles correspond to valine and methionine , respectively . Codon 101 codes for an amino acid in the formiminotransferase N-subdomain and resides between secondary structure elements β4 and α4 . This codon is highly conserved [27] with methionine being the predominant amino acid in all other vertebrates , including the Neanderthal and Denisovan sequences ( with the exception of lamprey , which is Valine ) ( Fig 3 ) . This suggests the derived Valine codon ( G allele ) has gone to near fixation in humans at some point after the modern-archaic human split , suggesting selection on a functional mutation ( G ) that confers a selective advantage . We do not yet understand the mechanism by which rs61735836 presumably affects arsenic metabolism; however , there are several mechanisms by which rs61735836 may affect FTCD function . First , because the minor/ancestral allele A produces a start codon ( Met ) , this allele may create an alternative translation start site that would produce a truncated FTCD protein . The minor allele A/Met creates the first 5´-proximal Kozak consensus sequence in the FTCD gene ( [A/G]xxAUGG ) . While translation generally initiates at the first 5´ AUG , the efficiency with which this AUG is recognized is influenced by the presence of a Kozak consensus sequence [28] . For 5´-proximal AUG codons that do not reside in a Kozak consensus sequence , ribosomes can fail to initiate translation at that site , and continue scanning for downstream start codons ( i . e . , “leaking scanning” ) [29] . There are three start codons upstream of rs61735836 , but none are a Kozak consensus sequence , including the canonical start site ( S6 Fig ) . Second , the V → M amino acid substitution may alter the structure of the protein , potentially through protein folding or octamer formation , thereby altering the efficiency with which the FTCD enzyme functions . However , this substitution is not strongly predicted to be damaging according to SIFT ( “tolerated” with a score of 1 . 0 ) , PolyPhen-2 ( benign with a score of 0 . 029 ) , CADD ( 0 . 77 with a PHRED-like scaled score of 9 . 3 ) , and ClinVar ( likely benign ) . Third , exon 3 is just downstream of several transcription factor binding sites and chromatin marks indicative of enhancers and promoters , and the exon itself is contained within a weak promoter in the HepG2 liver cancer cell line ( S7 Fig ) . This suggests that it is possible that rs61735836 could affect initiation of transcription or represent a translation start site specific to an FTCD isoform that lacks the canonical start codon . However , among the 14 FTCD isoforms observed in GTEx liver tissue , no transcripts lacking exon 1 include exon 3 ( S8 Fig ) . Furthermore , rs61735836 is not associated with FTCD expression in any GTEx tissue , including liver , and is not reported to be an FTCD isoform QTL , suggesting that the effect of this SNP is likely due to the amino acid substitution . The enzyme encoded by FTCD catalyzes the two consecutive final reactions of the L-histidine degradation pathway , which links histidine catabolism to one-carbon/folate metabolism ( Fig 4 ) [27] . First , the formiminotranserase domain of FTCD catalyzes the transfer of a formimino group from N-formiminoglutamate ( FIGLU ) to tetrahydrofolate ( THF ) , freeing glutamate and adding a one-carbon substituent at the oxidation level of formic acid to THF . Second , the cyclodeaminase domain catalyzes the removal of ammonia from formimino-THF , generating 5 , 10-methenylTHF [30 , 31] . MTHFD1 catalyzes the interconversion of 5 , 10-methenylTHF to either 5 , 10-methyleneTHF or to THF ( via 10-formylTHF ) , both of which can enter the folate cycle and be used for synthesis of 5-methylTHF . Histidine has been proposed as a potential source 5 , 10-methenyl-THF in some tissues [32]; however , the relative contribution of histidine to the one-carbon pool is currently unclear , and contribution may vary across tissues [33] . Additional potential roles of FTCD include catalyzing the conversion of THF to 5-formyl-THF and conversion of 5-formyl-THF to 5 , 10-methenyl-THF [34 , 35] . The one-carbon cycle is critical for arsenic metabolism , because 5-methyl-THF ( primarily originating from dietary sources , but also generated from histidine catabolism ) is essential to the production of S-adenosylmethionine ( SAM ) which provides methyl groups for methyltransferase reactions , including methylation of arsenic ( Fig 4 ) . Methylation of arsenic is catalyzed by AS3MT , a known arsenic susceptibility/metabolism gene [15 , 18] . The methionine cycle is also linked to the production of glutathione ( GSH ) , which may increase the speed of arsenic reduction ( i . e . , arsenate ( AsV ) to arsenite ( AsIII ) ) , which occurs prior to methylation of arsenic by AS3MT . Variation in folate status/intake and one-carbon metabolism have long been hypothesized to influence arsenic metabolism [36] , and randomized studies have provided strong evidence that folate supplementation increases arsenic metabolism efficiency and reduces blood arsenic concentrations [23 , 37] . However , prior candidate gene association studies of polymorphisms in one-carbon metabolism genes and arsenic metabolism have provided only suggestive or null findings [38 , 39] , and no prior studies examined variation in FTCD . Interestingly , a recent GWA study of 124 arsenic-exposed women living in the northern Argentinean Andes identified associations between SNPs in the 21q22 . 3 region and urinary DMA% ( P = 1 . 2x10-5 ) and MMA% ( P = 1 . 2x10-5 ) ( Schlebusch et al [40] ) . The SNPs showing the strongest associations reside in the LSS , MCM3AP , and YBEY genes , which are in the range of ~30 to ~150 kb upstream of ( and telomeric to ) FTCD . While this previously reported signal is nearby the signal we report , the two signals appear distinct . Our association involves a single coding SNP in FTCD that is in very low LD with all surrounding SNPs , while the Schlebusch et al . association involves many SNPs in a LD block that spans several genes ( with no association observed for SNPs within FTCD itself ) . Thus , it appears unlikely these two signals are due to the same causal variant . However , it is possible that the causal variants underlying these associations impact the function of the same gene ( s ) . As of January 31 , 2019 , the FTCD gene has not been reported in any GWA study of human traits ( according to the NHGRI-EBI GWAS catalog ) . Due to the very weak LD between rs61735836 and nearby variants , this variant cannot be accurately imputed in most populations; it must be directly genotyped . However , commercially available arrays that lack “exome content” ( https://genome . sph . umich . edu/wiki/Exome_Chip_Design ) do not include rs61735836 . Among arrays used in prior GWA studies , 25 ( out of 56 ) Illumina arrays and 1 ( out of 20 ) Affymetrix array include rs61735836 ( based on LDlink [41] ) . Thus , a large fraction of prior GWA studies have not measured or imputed rs61735836 , including all studies conducted prior to the development of the exome content . Rare mutations in FTCD cause various forms of FTCD deficiency ( OMIM: 229100 ) , an autosomal recessive disorder which is the second most common inborn error of folate metabolism [31 , 42] . Severe forms have been reported to cause mental and physical retardation , anemia , and elevated serum folate , while less severe cases have been reported to have developmental delay and elevated levels of FIGLU in urine [30] , which accumulates due to FTCD deficiency ( Fig 4 ) . Recent work has demonstrated that individuals homozygous for putative loss-of-function mutations in FTCD have clearly detectable levels of FIGLU in urine in the absence of histidine loading ( which is normally very low or undetectable ) , in the range of 5 to 195 mmol per mol creatinine [43] . To assess the potential impact of rs61735836 on urine FIGLU , we measured FIGLU in baseline urine samples for 60 of our HEALS participants ( 20 for each of the three rs61735836 genotype categories ) using tandem mass spectrometry in the laboratory of Dr . Devin Oglesbee as described previously [43] . We observed no evidence for elevated FIGLU among carriers or non-carriers of the G allele , with no participant having a FIGLU >0 . 25 mmol/mol creatinine ( S9 Fig ) . This finding suggests that impact of rs61735836 on FTCD function is less severe than the impact of loss of function mutations on FIGLU . Combining data on FTCD SNP rs61735836 with the two previously-reported arsenic metabolism SNPs in the AS3MT region ( rs9527 and rs11191527 ) [15 , 16] , we can explain ~10% of the phenotypic variation in DMA% for our HEALS participants . Mendelian randomization analyses of all three variants ( using the inverse-variance weighted meta-analysis method [44] ) provides strong evidence of a causal effect of arsenic metabolism efficiency ( as measured by DMA% ) on skin lesion ( OR = 0 . 89 for a 10% increase in DMA%; P = 6x10-8 ) ( Fig 5 ) . We observe similar results when using ( a ) either iAs% or MMA% as a measure metabolism efficiency and ( b ) alternative MR methods implemented in the MendelianRandomization R package [44] ( S10 Fig ) . These MR results are consistent with prior observational studies [14 , 45–48] showing that high DMA% ( and SMI ) are generally associated with decreased skin lesion risk , while high iAs% , MMA% , and PMI are generally associated with increased skin lesion risk . These observational studies also indicate that , among the various arsenic metabolism measures , MMA% is most consistently associated with increased risk for skin lesions and several types of cancer [49] . Consistently , in vitro studies indicate MMAIII is likely to be the most toxic of all metabolites of inorganic arsenic [50 , 51] . Thus , the primary finding from this work and our prior studies—that producing DMA more efficiently ( and therefore depleting iAs and MMA ) reduces skin lesion risk—could be attributed to a ) enhanced excretion of arsenic from the body in the form of DMA and/or b ) lower percentages of the most toxic metabolites ( e . g . MMAIII ) among all arsenic species in the body . FTCD SNP rs61735836 showed suggestive evidence of additive GxE interaction , results that are directionally consistent with previously reported additive interaction results for AS3MT genotypes [16] . For both loci , the expected interaction between SNP and arsenic exposure in relation to skin lesions is much more apparent on the additive scale compared to the multiplicative scale . This is an important observation considering these SNPs must modify the effect arsenic on skin lesion risk , a conclusion we draw based on the fact that these lesions do not occur in the absence of arsenic exposure . In other words , this variant cannot affect skin lesion risk among unexposed individuals , so GxE must be present . However , because we have few truly unexposed individuals in our study , we are unable to assess GxE on the present vs . absent exposure scale . In addition , it is possible that we are not well-powered to detect GxE due to the low MAF of rs61735836 and the relatively small number of genotyped cases having exposure data obtained prior to arsenic mitigation efforts ( n = 443 ) . In summary , this work identifies a protein-altering variant in FTCD ( rs61735836 ) that is associated with both arsenic metabolism efficiency and risk for arsenic-induced skin lesions , the most common sign of arsenic toxicity . Future studies can use this variant , in conjunction with AS3MT variants , to study the effects of arsenic exposure ( through food , water , or other sources ) and metabolism efficiency on health outcomes believed to be affected by arsenic ( e . g . , cancer and cardiovascular disease ) , even in the absence of data on arsenic exposure . This work provides evidence of links among histidine catabolism , one-carbon/folate metabolism , and arsenic metabolism , which is intriguing in light of the strong prior evidence supporting a role for folate status and one-carbon metabolism in arsenic metabolism efficiency [36] , including randomized studies of folate supplementation in humans [23 , 37] . However , additional research is needed to understand ( 1 ) if and how this SNP impacts the relative distribution of folate metabolites and ( 2 ) the potential mediating role of folate on the association between rs61735836 and arsenic metabolism efficiency . A better understanding of these effects could enable the use of rs61735836 as a tool for studying the many human diseases with hypothesized connections to folate and one-carbon metabolism ( e . g . , cancer , vascular disease , cognitive decline , neural tube defects ) [52–54] .
This research was approved by the Institutional Review Board of the University of Chicago ( IRB16-1236 ) . Verbal informed consent was obtained from all participants . The DNA samples used in this work were obtained at baseline interview from individuals participating in one of the two following studies: the Health Effects of Arsenic Longitudinal Study ( HEALS ) [55] and the Bangladesh Vitamin E and Selenium Trial ( BEST ) [56] . HEALS is a prospective study of health outcomes associated with arsenic exposure through drinking water in a cohort of adults in Araihazar , Bangladesh , a rural area east of the capital city , Dhaka . A cohort of ~12 , 000 participants was recruited in 2000–2002 , and ~8 , 000 additional participants were recruited in 2006–2008 . Over 6 , 000 wells in the study area have been tested for arsenic using graphite furnace atomic absorption spectrometry and individuals reported the primary well from which they drank . Trained study physicians conducted in-person interviews , clinical evaluations ( including ascertainment of skin lesions ) , and spot urine collection at baseline and follow-up visits ( every two years ) . BEST is a 2×2 factorial randomized chemoprevention trial ( n = 7000 ) evaluating the effects of vitamin E and selenium supplementation on non-melanoma skin cancer ( NMSC ) risk . BEST participants are residents of Araihazar ( the same geographic area as HEALS ) , Matlab , and surrounding areas . BEST uses many of the same study protocols as HEALS , including arsenic exposure assessment and biospecimen collection . All BEST participants had existing arsenic-related skin lesions at baseline . The exome-wide association study of arsenic species percentages was conducted using urinary arsenic metabolite and exome chip SNP data on 1 , 660 individuals randomly selected from HEALS . Exome-wide association analyses of arsenic-induced skin lesions were conducted using exome chip SNP data on 2 , 401 cases and 2 , 472 lesion-free controls ( from both HEALS and BEST ) . This case-control sample includes 1 , 660 HEALS participants with arsenic metabolite data . Analyses of blood arsenic metabolites were conducted using 155 cohort members for whom we had existing data on arsenic species measured in blood . These data on blood arsenic species were generated in the context of various HEALS ancillary studies: the Nutritional Influences on Arsenic Toxicity ( NIAT ) Study [23] , the Folate and Oxidative Stress ( FOX ) Study [25] , and the Folic Acid and Creatinine Trial ( FACT ) [24] ( data courtesy of Gamble , MV and Graziano , JH ) . Among these 155 participants , 147 were included in the case-control analysis of skin lesions , and 87 were included in the analysis of arsenic metabolites in urine . We assessed SNP-arsenic interaction using data on HEALS participants with individually-measured arsenic exposure ( i . e . , arsenic concentration of their primary drinking well at baseline ) . These exposure measures were taken prior to arsenic mitigation efforts [22]; thus , these measures represent longer-term , historical exposure levels . The majority of the HEALS participants ( ~95% ) were lesion-free at baseline . Similarly , among our genotyped HEALS participants , only 66 of the 443 skin lesion cases were prevalent cases . The remaining 377 were incident skin lesions cases ( ascertained at biennial follow-up visits by trained study physicians using a structured protocol [55] ) . All BEST participants had skin lesions at baseline , because a skin lesion diagnosis was part of the BEST eligibility criteria [56] . In this study , skin lesion cases were defined as individuals with any type of arsenic-induced lesion , including keratosis , melanosis , and/or leukomelanosis . Study protocols were approved by the Institutional Review Boards of The University of Chicago and Columbia University , the Ethical Review Committee of the International Center for Diarrheal Disease Research , Bangladesh , and the Bangladesh Medical Research Council . Informed consent was obtained from all participants . Using DNA from individuals participating in HEALS ( Health Effects of Arsenic Longitudinal Study ) and BEST ( the Bangladesh Vitamin E and Selenium Trial ) , we genotyped 4 , 939 Bangladeshi individuals ( HEALS n = 2 , 949; BEST n = 1 , 983 ) using Illumina’s exome array v1 . 1 . Prior to QC , our dataset consisted of 242 , 901 variants . We removed samples with >3% missing SNPs ( n = 6 ) , gender mismatches ( n = 22 ) , and duplicate individuals ( n = 25 ) . We removed SNPs with call rate <97% ( 176 SNPs ) , monomorphic SNPs ( n = 27 , 687 ) , and 166 SNPs deviating from Hardy-Weinberg Equilibrium ( P<10−10 ) . None of the SNPs that pass this HWE threshold show HWE P-values <10−7 when relative pairs are removed from the dataset . We removed SNPs with a minor allele frequency ( MAF ) <1% ( n = 178 , 015 ) . Among the 19 , 992 post-QC variants , there were 17 , 919 missense , 141 nonsense , 1 , 260 synonymous , and 672 non-exonic variants . All post-QC variants were included in our analysis . A similar QC procedure for our participants’ existing genome-wide data on ~300 , 000 SNPs measured using the Illumina HumanCytoSNP-12 v2 . 1 array has been described previously [15 , 16] . As previously described [45] , arsenic species in HEALS urine samples were separated using high-performance liquid chromatography ( HPLC ) and detected using inductively coupled plasma-mass spectrometry ( ICP-MS ) with dynamic reaction cell ( DRC ) . Percentages of iAs , MMA and DMA among all arsenic species were calculated after subtracting arsenobetaine and arsenocholine ( i . e . , nontoxic organic arsenic from dietary sources ) from total arsenic . All data on arsenic species in blood were generated using ICP-MS-DRC coupled to HPLC , as described previously for NIAT and FOX [23 , 57] ( the FACT data is not yet published ) . Blood samples were processed in the same way for each of these studies , and this processing has been described previously in detail [23] and follows the method of Csanaky and Gregus [58] . For quality control purposes , samples with known concentrations of arsenic species were regularly analyzed . Two samples were run at the beginning of every working day and throughout the day , after every 10 samples , as previously described [23] . The limit of detection for each metabolite of interest was 0 . 2 μg/L . We have previously reported intra-assay CVs for this assay ( from FOX ) for AsIII , AsV , MMA , and DMA ( 0 . 9% , 11 . 5% , 3 . 6% , and 2 . 6% , respectively ) as well as inter-assay CVs ( 3 . 7% , 23 . 2% , 2 . 9% , and 3 . 5% , respectively ) [57] . Arsenic exposure in HEALS was assessed by measuring total arsenic concentration in individuals’ urine and their primary drinking well at baseline ( 2000–2002 ) [55] . We conducted exome-wide association analyses for each of the three arsenic species measured in urine ( iAs% , MMA% , and DMA% ) restricting to 1 , 660 HEALS participants with available data on arsenic species in urine . We conducted exome-wide association analyses of arsenical skin lesion status ( the most common sign of arsenic toxicity ) using data on 2 , 401 cases and 2 , 472 lesion-free controls ( from both HEALS and BEST ) . All participants included in these analyses have existing genome-wide data on ~300 , 000 SNPs based on the Illumina HumanCytoSNP-12 v2 . 1 array , as described previously [15 , 16] . For association analysis , we used GEMMA ( Genome-wide Efficient Mixed Model Association ) [59] to account for cryptic relatedness , as many of our participants have a relative in the study . For the random effects model implemented in GEMMA , we used a kinship matrix based on ~260 , 000 genome-wide SNPs , as described previously [15] . We also used GEMMA for case/control association testing; we approximated odds ratios ( ORs ) by first dividing the beta coefficient by [x ( 1 –x ) ] , where x is the proportion of cases in our sample , in order to estimate the beta from a logistic model . This quantity was exponentiated to obtain an OR . Multiplicative interaction was tested by including an interaction between arsenic exposure tertiles ( coded 0 , 1 , 2 ) and rs61735836 ( coded 0 , 1 , or 2 minor alleles ) in a logistic regression . Using the results from this logistic regression , additive interaction was estimated as the relative excess risk for interaction ( RERI ) using the delta method for confidence interval estimation [60 , 61] . SNP-SNP interaction was tested by including an interaction between two SNPs , coded as minor allele counts , in linear or logistic regression models . In order to analyze the effect of SNPs on arsenic species in blood , including measures taken at multiple time points for the same individuals , we used a mixed-effects model with a random intercept for each individual to account for the fact that 109 individuals appear twice in the dataset ( having both baseline and follow-up/post-intervention measurements ) . Mendelian randomization analyses based on summary statistics were conducted using the inverse-variance weighted meta-analysis method as implemented in the MendelianRandomization R package [44] , in addition to a maximum likelihood method , the median methods , and Egger regression [44] . Allele frequencies and linkage disequilibrium ( LD ) patterns were examined using LDlink [41] and the Geography of Genetic Variants browser [21] .
|
Chronic exposure to arsenic through food and drinking water is a serious global health issue , as arsenic can increase risk for cancer , cardiorespiratory diseases , and other chronic conditions . Ingested arsenic absorbed into the blood stream is metabolized ( through reduction and methylation reactions ) in order to facilitate excretion in urine and removal from the body . Individuals differ with respect to the efficiency of this metabolism , in part due to inherited genetic variation . The only region of the genome known to contain variation that impacts arsenic metabolism efficiency is 10q24 . 32 , and these variants likely alter the function of the nearby gene AS3MT ( arsenite methyltransferase ) . In order to identify new genetic variants that affect arsenic metabolism , we measured variation in protein-coding regions across the entire genome for >4 , 800 individuals with varying levels of exposure to arsenic through naturally-contaminated drinking water in Bangladesh . Using this data , we identified a variant in the FTCD gene ( formiminotransferase cyclodeaminase ) that is associated with arsenic metabolism efficiency and risk for arsenic-induced skin lesions . This genetic variant alters the FTCD amino acid sequence , potentially disrupting a cryptic protein translation start site in exon 3 . FTCD codes for an enzyme involved in histidine catabolism and one-carbon/folate metabolism; thus , our result provides new evidence supporting the well-established hypothesis that the folate/one-carbon cycle plays an important role in arsenic-related disease .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"methods"
] |
[
"genome-wide",
"association",
"studies",
"medicine",
"and",
"health",
"sciences",
"body",
"fluids",
"protein",
"metabolism",
"pathology",
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"laboratory",
"medicine",
"alleles",
"toxicology",
"urine",
"toxicity",
"signs",
"and",
"symptoms",
"genome",
"analysis",
"genomics",
"lesions",
"chemistry",
"arsenic",
"genetic",
"loci",
"biochemistry",
"diagnostic",
"medicine",
"blood",
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] |
2019
|
A missense variant in FTCD is associated with arsenic metabolism and toxicity phenotypes in Bangladesh
|
The thalamus plays a critical role in the genesis of thalamocortical oscillations , yet the underlying mechanisms remain elusive . To understand whether the isolated thalamus can generate multiple distinct oscillations , we developed a biophysical thalamic model to test the hypothesis that generation of and transition between distinct thalamic oscillations can be explained as a function of neuromodulation by acetylcholine ( ACh ) and norepinephrine ( NE ) and afferent synaptic excitation . Indeed , the model exhibited four distinct thalamic rhythms ( delta , sleep spindle , alpha and gamma oscillations ) that span the physiological states corresponding to different arousal levels from deep sleep to focused attention . Our simulation results indicate that generation of these distinct thalamic oscillations is a result of both intrinsic oscillatory cellular properties and specific network connectivity patterns . We then systematically varied the ACh/NE and input levels to generate a complete map of the different oscillatory states and their transitions . Lastly , we applied periodic stimulation to the thalamic network and found that entrainment of thalamic oscillations is highly state-dependent . Our results support the hypothesis that ACh/NE modulation and afferent excitation define thalamic oscillatory states and their response to brain stimulation . Our model proposes a broader and more central role of the thalamus in the genesis of multiple distinct thalamo-cortical rhythms than previously assumed .
The thalamocortical network plays a central role in cerebral rhythmic oscillations [1–4] and abnormal thalamocortical rhythms have been associated with disorders such as depression , schizophrenia and Alzheimer’s disease [5–7] . Understanding the cellular and circuit mechanisms of thalamocortical oscillations thus constitutes a crucial first step to comprehend the network impairments underlying neurological and psychiatric disorders . However , the mechanisms by which the thalamocortical network generates distinct states of oscillatory patterns remain highly debated [8–11] . One important question is whether the thalamus , originally believed to be the “pacemaker” of thalamocortical oscillations [7 , 8 , 12] , is indeed able to independently generate multiple distinct brain rhythms or whether the thalamus requires interaction with the cortex [13–16] . Answering this question not only provides the basis for a mechanistic understanding of brain oscillations , but also will provide important insights in the design of effective mechanism-based brain stimulation techniques that specifically target abnormal thalamocortical dynamics . Experimental evidence suggests that the thalamus is capable of independently generating multiple , distinct oscillatory states . In the cat lateral geniculate nucleus ( LGN ) , in vitro and in vivo studies have identified a subset of thalamocortical cells ( TCs ) that generate high-threshold bursting at theta ( θ ) and alpha ( α ) frequency bands and thus may mediate the cellular mechanism of both θ and α oscillations [12 , 17 , 18] . Coupled with gap junctions [12 , 17] , high-threshold bursting TC cells provide synchronized excitatory inputs to local interneurons and reticular cells that entrain the majority of TC cells ( i . e . , non-high threshold bursting TC cells ) into the α rhythm via feed-forward and feedback inhibition [18] . Besides θ/α oscillations , the thalamus is also critically involved in the genesis of the slow delta rhythm and spindle oscillations that appear at different stages of non-rapid eye movement ( NREM ) sleep [2 , 19 , 20] . Moreover , the thalamus is able to produce high frequency oscillations in both β and γ bands ( 20–60 Hz ) in the neonatal rat whisker sensory system [21] , during attentional processing in cats [22] and during cognitive tasks in humans [23] . Consistently , experimental data indicated that fast rhythms ( 30–40 Hz ) could be synchronized with an intrathalamic mechanism [24] . It is not known whether the same neural substrate and circuitry for θ and α oscillations could also mediate other oscillatory patterns and what controls the transition among these oscillatory states . Thalamic processing is subject to the action of modulatory neurotransmitters including acetylcholine ( ACh ) , norepinephrine ( NE ) , serotonin ( 5-HT ) , histamine ( HA ) and dopamine ( DA ) [25] . Of these neurotransmitters , cholinergic and noradrenergic modulation plays the key and best understood role in shaping the oscillatory state of the thalamocortical network [26] . Consistently , the α rhythm is both induced by muscarinic cholinergic receptor activation in slices of cat LGN [17] and supported by cholinergic innervation in vivo [18] . Fast rhythmic activities in the β/γ frequency band ( 20–60 Hz ) are observed in cat thalamocortical cells [11] and promoted by cholinergic projection from the brainstem [27] . Besides , slow δ oscillations are believed to be mediated by hyperpolarization of TC neurons with diminished activation of both the cholinergic and noradrenergic systems [28 , 29] . In addition to neuromodulation , the specific type of thalamic oscillation is also dependent on the level of afferent excitation . For example , the amplitude of the α oscillation is maximal when the eyes are closed and therefore input form the retina is low [30] . Hence , generation and transition of distinct thalamic oscillations depend critically on neuromodulation and afferent excitation , yet a unified model has been lacking so far . To close this gap , we developed a biophysical , conductance-based model of the thalamic network constrained by extensive experimental data to test the hypothesis that generation and transition of distinct thalamic oscillations are functions of both ACh/NE neuromodulation and afferent excitation under various physiological conditions . By varying only these two model parameters , ACh/NE neuromodulation and afferent excitation , we demonstrated that the thalamic network is capable of generating multiple distinct oscillatory states ( δ , spindle , α/θ and γ/β oscillations ) in absence of cortical input . We elucidated the cellular and circuit mechanisms for each oscillatory state by manipulating the network connectivity and key model parameters . Simulation results suggest that generation of distinct thalamic oscillations is a result of both intrinsic oscillatory cellular properties and specific network connectivity patterns . The manifestation of multiple distinct oscillations in one unified biophysical thalamic model enabled us to examine the impact of rhythmic stimulation on thalamic network dynamics . By applying periodic stimulation to the thalamic model during three major oscillatory states ( δ , α and γ oscillations ) , we observed that entrainment of thalamic oscillations is highly state-dependent in that the same stimulation induced much stronger and more prominent entrainment during γ oscillations than δ and α oscillations due to the different oscillatory mechanisms . Our findings emphasize the importance of considering the rich role of endogenous oscillations in thalamus for the study of thalamo-cortical rhythms and highlight the need to consider the network state when modulating brain oscillations with periodic stimulation waveforms .
By varying the potassium leak conductance modulated by ACh/NE and the maximal input conductance corresponding to different levels of afferent excitation in all four types of thalamic neurons ( Table 1 ) , we were able to generate four distinct oscillatory states ( δ , spindle , α , and γ oscillations ) in the thalamic network . In the model , the increasing level of ACh/NE during the transition from deep sleep to wakefulness corresponded to lower potassium leak conductance in HTC , RTC and RE cells , but higher potassium leak conductance in INs ( Table 1; also see Methods ) . All four types of thalamic neurons received random Poisson distributed inputs mediated by AMPA receptors and the maximal input conductance was a fixed constant value associated with the AMPA synaptic channels . To quantify the network activity during the four oscillatory states , we calculated the average firing rates and synchronization index of four different groups of thalamic neurons ( HTC , RTC , IN & RE , Fig 3A and 3B ) along with the correlation between different neuronal populations ( Fig 3C and 3D ) . Overall , the average firing rates of RTC cells were lower than HTC cells for all oscillatory states , because RTC cells were less excitable than HTC cells due to a much smaller high-threshold T-type Ca2+ current ( ICa/HT , S1 Table ) . In addition , RTC cells received inhibition from both INs and REs , while HTC cells received inhibition from REs only ( Fig 1A ) . The firing rates of both HTC and RTC cells increased from δ to spindle oscillations and decreased during α oscillations followed by a large increase during γ oscillations ( Fig 3A ) . This is because the bursting frequency of TC cells increased from δ to spindle oscillations ( δ: 3 . 7 Hz; spindle: 7 . 9 Hz ) and during the transition from spindle to α oscillations , the oscillation frequency was similar ( spindle: 7 . 9 Hz; α: 9 . 2 Hz ) , but RTC cells switched from bursting to single spiking while the number of spikes per burst reduced in HTC cells ( spindle: 3 . 2; α: 2 . 1 ) . The TC firing rates were highest during γ oscillations among all four oscillatory states since TC cells received strong afferent drive ( Table 1 ) . During the transition from γ to α oscillations , the firing rates of INs increased ( γ: 9 . 8 Hz; α: 16 . 6 Hz; Fig 3A ) while the firing rates of REs reduced ( γ: 26 . 6 Hz; α: 8 . 2 Hz; Fig 3A ) . This is consistent with the experimental observation that LGN interneurons in cats exhibited an increase in firing rate during α oscillations ( compared to non-α state; presumably γ state in our model ) , whereas TRN neurons showed a decrease in firing rate [18] . Our simulations suggest that such differential effects resulted from the following three factors: ( 1 ) INs were less excitable than RE cells in the high ACh/NE state owing to a larger potassium leak conductance ( Table 1 ) ; ( 2 ) INs received inputs from HTCs only while REs received inputs from both HTC and RTC cells ( Fig 1A ) . Consequently , the large increase in RTC firing during γ oscillations led to substantial increase in RE spikes; ( 3 ) HTC cells switched from HTBs during α oscillations to tonic spiking during γ oscillations , which reduced the effectiveness of excitatory drive on INs , as mentioned earlier . Combined with STD , INs switched from strong bursting to irregular mix of bursting and tonic spiking ( compare Fig 2C1 with Fig 2D1 , upper middle ) . As a result , the IN firing rate reduced during γ oscillations compared with α oscillations . The generation of distinct oscillations in the thalamic network depends on synchronization of different neuronal populations . To evaluate the degree of neuronal synchrony in the thalamic network , we calculated the synchronization index ( SI ) of the four neuronal populations during different oscillatory states ( Fig 3B ) . The SI of both HTC and RTC cells maintained at relatively high level during δ , spindle and α oscillations ( > 0 . 87; Fig 3B ) . During γ oscillations , the SI of RTC cells substantially decreased to 0 . 09 , while that of HTC cells only slightly reduced to 0 . 76 ( Fig 3B ) because of gap junctions . The SI of INs decreased moderately from δ to spindle/α oscillations and reduced greatly during γ oscillations ( δ: 0 . 98; spindle: 0 . 76; α: 0 . 78; γ: 0 . 03; Fig 3B ) . Similarly , the SI of REs showed a decreasing trend from δ to γ oscillations , but the reduction during γ oscillations was smaller ( δ: 0 . 97; spindle: 0 . 90; α: 0 . 69; γ: 0 . 23; Fig 3B ) due to the inter-RE gap junctions and inhibition . These results indicate that the thalamic network had the highest level of synchrony during δ oscillations followed by spindle and α oscillations and the synchronization level was the lowest during γ oscillations . We next computed the cross-correlation between different groups of neuronal populations during the four oscillatory states ( Fig 3C ) . First , the correlation was consistently highest during δ oscillations and lowest during γ oscillations for all six neuronal population pairs . Second , the correlation during spindle oscillations was either comparable to ( e . g . , HTC-RTC ) or moderately lower than ( e . g . , RTC-RE ) α oscillations ( Fig 3C ) . Lastly , the correlation between HTC cells and RE neurons during γ oscillations was substantially higher than other population pairs ( Fig 3C ) , consistent with higher level of synchrony of these two neuronal assembles during γ oscillations ( Fig 3B ) . On average , the neuronal correlation was largest during δ oscillations followed by α and spindle oscillations and the correlation was lowest during γ oscillations ( Fig 3D ) . Overall , as the ACh/NE modulation level and synaptic input increased , the thalamic network switched from low frequency oscillations ( δ oscillations ) to higher frequency oscillations ( spindle and α oscillations ) , and to fast frequency oscillations ( γ oscillations ) . Correspondingly , the membrane potentials of TC neurons gradually depolarized and the firing patterns of HTC cells switched from LTBs to rebound LTBs to HTBs and to tonic spiking , while those of RTC neurons changed from LTBs to rebound LTBs and to tonic spiking . Hence , with minimal change of parameters ( the potassium leak conductance and the synaptic input strength; Table 1 ) , the thalamic network was able to generate multiple distinct and stable oscillatory states that appear under different behavioral and cognitive conditions [2 , 18 , 19 , 22 , 23] . Next , we dissected the cellular and circuit mechanisms for each oscillatory state by manipulating the major network connectivity and varying key model parameters . To understand how δ oscillations were generated in the thalamic network , we first removed the gap junction connections among HTC cells . Removal of gap junctions led to large variation in HTC burst timing as the LTB frequencies of individual cells differed from each other because of intrinsic heterogeneity ( i . e . , different leak conductance ) and external noise input ( Fig 4A , top ) . It also reduced RTC synchrony through the HTC-RTC gap junctions ( compare Fig 4A with Fig 2A2 , lower middle ) . Consequently , after a few partially synchronized δ cycles , both HTC and RTC cells broke into two subpopulations separated by the IN and RE inhibition . In one cycle , a majority of HTC cells burst with a small percentage of RTC cells while in the next cycle , a majority of RTC cells burst with a small percentage of HTC cells ( Fig 4A ) . As a result , the network oscillation frequency doubled to about 6 . 7 Hz ( S11 Fig ) . Interestingly , if the HTC-RTC gap junctions were additionally removed , the frequency doubling effect was not observed ( Fig 4B ) and the network oscillation frequency just slightly increased to about 4 Hz ( S11 Fig ) . Although the δ rhythm was maintained without any TC gap junctions , the degree of network synchrony was substantially reduced compared with the control condition ( compare Fig 4B with Fig 2A2 ) . To examine the role of synaptic inhibition in thalamic network synchronization , we first blocked the HTC→IN projections to remove the feedforward IN inhibition on RTC cells ( gap junctions intact ) . We found that RTC cells remained well synchronized ( Fig 4C ) . We next blocked the TC→RE connections to eliminate the RE feedback inhibition on TC cells . Similarly , RTC bursting was still well phase-locked to the δ rhythm ( Fig 4D ) . If , however , both the HTC→IN and TC→RE connections were blocked , RTC bursts became largely desynchronized , except the subset of RTC cells that formed gap junctions with HTC cells ( Fig 4E ) . This suggests that either IN or RE inhibition is required for the synchronization of RTC cells . Lastly , when the HTC-RTC gap junctions were additionally removed ( besides IN and RE inhibition ) , RTC bursting became completely desynchronized ( Fig 4F ) . Therefore , thalamic δ oscillations are generated intrinsically by TC cells and are synchronized by both gap junctions and synaptic inhibition . In the thalamic network , spindle oscillations were triggered by transient synchronized RE burst firing which produced rebound LTBs in TC cells . Besides RE inhibition , RTC cells also received IN inhibition which could elicit rebound LTBs . To differentiate the role of IN and RE inhibition in spindle oscillations , we blocked the HTC→IN and TC→RE projections , respectively , to eliminate IN and RE bursts . When IN burst firing was removed , HTC and RTC cells fired only three and one bursts respectively ( Fig 5A ) , indicating that IN inhibition contributed to spindle oscillations by hyperpolarizing RTC cells . Nevertheless , the lack of IN inhibition could be easily compensated by increased inhibition from RE neurons . In the absence of IN inhibition , when the inhibitory RE→TC synaptic weight increased only 33% ( from 3 nS to 4 nS ) , spindle oscillations persisted for about 4 seconds , twice the duration of the control case ( compare Fig 5B with Fig 2B1 ) . Thus , with sufficient RE inhibition , the TC-RE feedback loop was able to create and sustain spindle oscillations for a few seconds . On the other hand , when the TC→RE synapses were blocked , the TC-IN network generated four cycles of spindle oscillations before termination ( Fig 5C ) . This was because the IN bursts hyperpolarized RTC cells resulting in rebound RTC bursts . At the same time , RTC hyperpolarization facilitated rebound bursts in HTC cells through the HTC-RTC gap junctions , which further drove IN bursting . Nevertheless , in the absence of RE inhibition , the inhibitory IN→RTC synaptic weight needed to increase fourfold ( from 3 nS to 12 nS ) in order to sustain spindles for about 1 . 5 seconds ( Fig 5D ) . This contrasted to a much more prominent increase in spindle duration induced by a much smaller increase of the RE→TC synaptic weight ( compare Fig 5D with Fig 5B ) . Such difference suggests that RE inhibition plays a more major role than IN inhibition in generating spindle oscillations , consistent with experimental data [3 , 20 , 38] . Similar to the effect of increased RE→TC synaptic weight , when the TC→RE synaptic weight increased 50% ( AMPA: from 4 nS to 6 nS; NMDA: from 2 nS to 3 nS ) , RE neurons responded to TC inputs with more burst spikes and the duration of spindle oscillations increased substantially from about 2 seconds to 3 . 8 seconds ( S12 Fig ) . Hence , spindle duration was fine-tuned by the strength of RE inhibition . As RE inhibition gradually decreased over the course of spindles due to short-term depression ( STD ) , we hypothesized that removing STD at the TC→RE or RE→TC synapses could significantly extend or even sustain spindles indefinitely . Indeed , when STD at the TC→RE synapses was removed , spindle oscillations were sustained for about 4 seconds ( Fig 5E ) , whereas they continued beyond 7 seconds when STD at the RE→TC synapses was abolished ( Fig 5F ) . Thus in our model , spindle oscillations are terminated mainly by STD at the inhibitory RE→TC synapses , as suggested previously [39] . Our results are consistent with a recent experimental study showing that the duration of spindle oscillations depends critically on the inhibitory strength of RE neurons on TC cells [38] . Moreover , it suggests that the variable spindle duration observed in experiments may be a result of difference in RE excitability and heterogeneous synaptic strength between TC and RE neurons . Experimental data has identified the HTC cell as an important neuronal substrate for thalamic θ and α oscillations [12 , 17 , 18] . To test the importance of HTBs in generating α oscillations , we varied the maximal conductance density of the high-threshold T-type Ca2+ current ( gCa/HT ) in HTC cells from 1 mS/cm2 to 5 mS/cm2 ( default: 3 mS/cm2 ) . When gCa/HT was reduced to 1 mS/cm2 , HTC cells failed to produce HTBs ( Fig 6A1 , top ) . With a lack of excitation from HTC cells , IN neurons were mostly silent ( Fig 6A1 , upper middle ) . In the absence of feedforward inhibition from IN neurons , RTC cells fired random spontaneous activities at about 3 Hz without synchronization ( Fig 6A1 , lower middle ) . As a result , the α rhythm disappeared . On the other hand , when gCa/HT was increased to 5 mS/cm2 , depolarized HTC cells started to transition from the bursting mode to the tonic spiking mode and became desynchronized because of strong heterogeneous bursting dynamics ( Fig 6A2 , top ) . Consequently , INs fired random bursts ( Fig 6A2 , upper middle ) and suppressed the activity of RTC cells ( Fig 6A2 , lower middle ) . As a result , no synchronized α oscillations developed . These simulation results demonstrated that synchronized HTBs of HTC cells were essential for the generation of thalamic α oscillations , consistent with experimental observations [12 , 17] . Fig 6A3 plots the network oscillation frequency ( blue ) and the spectral peak power ( red ) as a function of gCa/HT . When gCa/HT increased from 1 . 5 to 4 . 5 mS/cm2 , the oscillation frequency moderately increased from 7 . 3 Hz to 9 . 8 Hz while the oscillation power remained relatively stable . At the values of 1 and 5 mS/cm2 , there was a large drop in oscillation power because no synchronized α oscillations occurred . Hence , thalamic α oscillations are limited to a relatively narrow frequency band ( 8–10 Hz ) where HTC cells fired robust high-threshold bursting . The HTB frequency of HTC cells is also dependent on the depolarization level ( S1 Fig; [7 , 17] ) . As such , changing the external drive to the network would alter the frequency of α oscillations . Indeed , when the random afferent inputs were removed from the thalamic network , HTC cells still fired spontaneous synchronized HTBs , but at a lower frequency ( 6 Hz; Fig 6B1 , top ) . Subsequently , the whole network was synchronized at about 6 Hz ( Fig 6B1 ) , which was within the θ frequency band . One the other hand , when the maximal synaptic input conductance to the whole network increased to 4 nS ( default: 1 . 5 nS ) , the HTB frequency of HTC cells increased to about 12 Hz , but there was only one spike per burst ( Fig 6B2 , top ) due to inactivation of the ICa/HT current . The random activity of both IN and RE neurons increased substantially because of reduced synchronized HTC excitation and increased random input drive , which substantially suppressed RTC firing ( compare Fig 6B2 with Fig 2C2 , lower middle ) . As a result , the α oscillation power was significantly reduced ( Fig 6B3 ) . The network oscillation frequency ( blue ) and power ( red ) as a function of the maximal input conductance ( ginput ) are shown in Fig 6B3 . As ginput increased from 0 to 4 nS , the oscillation frequency increased monotonically from 6 . 1 Hz to 11 . 6 Hz . By comparison , the oscillation power increased initially from 0 nS to 0 . 5 nS and stayed in the same level for values up to 2 nS before decreasing considerably for larger input strength . Therefore , reducing the excitatory drive to the thalamic network switched α oscillations to θ oscillations while increasing the excitation level moved it to the upper α frequency band , consistent with experimental observation that HTC cells could underlie both α and θ oscillations dependent on the depolarization level of HTC cells [7 , 12] . Nevertheless , although stronger depolarization of HTC cells increased α frequency , it reduced the α power by switching high-threshold bursting to tonic spiking ( Fig 6A2 and 6B2 ) . Consequently , thalamic α oscillations exhibit optimal power between 8–10 Hz ( Fig 6A3 and 6B3 ) . Our results thus explain why α oscillations decrease with more afferent excitation ( e . g . , eyes opening; [30] ) . The key for γ oscillations was that HTC cells maintained a high level of synchrony even in the presence of strong random afferent inputs ( Fig 2D2 , top ) . The HTC rhythmicity was propagated to RE neurons via excitatory projection and to RTC cells via gap junctions . RE synchrony was boosted by inter-RE gap junctions and inhibition , which moderately constrained RTC firing through the RE→RTC inhibitory synapses . If the gap junctions between HTC cells were removed during γ oscillations , HTC cells were completely desynchronized ( Fig 7A1 , top ) leading to unconstrained RE and RTC firing ( Fig 7A1 , lower middle and bottom ) . As a result , the sLFP spectral peak of either HTC or RTC cells was eliminated ( Fig 7A2 ) . Thus , gap junctions may play a critical role in synchronizing γ oscillations in the thalamus , as in the hippocampus [40] and neocortex [41] . Besides gap junctions , the negative feedback loop between excitatory ( e . g . , pyramidal cells ) and inhibitory neurons also plays an important role in fast oscillation synchronization [42 , 43] . To examine whether the feedback interaction between TC and RE cells could sustain γ oscillations as well , we increased the maximal RE→TC synaptic conductance fourfold ( default: 3 nS; 4-fold increase: 12 nS ) in the absence of HTC gap junction coupling . With much stronger RE inhibition , moderate degree of population rhythmicity emerged from the HTC , RTC and RE neurons ( Fig 7B1 ) . The sLFP frequency spectra revealed peaks of similar amplitude as controls , but at a lower frequency ( controls: 30 . 5 Hz; 4-fold increase of RE inhibitory strength: 23 . 2 Hz; Fig 7B2 ) because of increased RE inhibition . Also , without HTC gap junctions , the amplitude of spectral peaks was similar for both HTC and RTC cells since they had similar degree of spike synchronization ( Fig 7B1 , top and lower middle ) . This was in contrast to the control case where the spectral peak of HTC-sLFP was much higher than that of RTC-sLFP ( Fig 7B2 ) . As a slight increase of the RE inhibitory synaptic weight significantly prolongs spindle oscillations ( Fig 5B ) , such strong RE feedback inhibition ( i . e . , 4-fold increase of synaptic strength ) seems unlikely in the thalamic network . Therefore , we conclude that HTC gap junctions are required for the synchronization of thalamic γ oscillations . So far we have demonstrated that the thalamic network was able to generate multiple distinct oscillations dependent on ACh/NE modulation and afferent excitation . To further examine how the thalamic network transitions from one state to the other on a continuous basis , we divided the ACh/NE modulation into 11 levels evenly ranging from 0% to 100% and varied the maximal input conductance to TC cells from 0 up to 20 nS with a 0 . 5 nS step , which resulted in 451 different parameter combinations ( see Methods section ) . We then simulated the thalamic network with all possible combinations of ACh/NE and input levels and identified the oscillatory state of the network across the entire parameter space . The network oscillation frequency and spectral power heat maps are shown in Fig 8A and 8B respectively . The networks with higher oscillation frequency ( > 15 Hz ) were predominantly located above the principal diagonal of the 2D parameter space ( Fig 8A ) indicating that the network oscillated faster with higher levels of ACh/NE modulation and afferent excitation . The highest oscillation frequency with the maximal level of ACh/NE ( 100% ) and input ( 20 nS ) was 38 Hz ( note some oscillation frequencies close to 40 Hz right above the principal diagonal , but these network states were classified as non-oscillatory due to low oscillation power; see below ) . By comparison , the networks with higher oscillation power were primarily located below the principal diagonal with the maximal power residing in the bottom of the 2D space , corresponding to weak afferent excitation ( Fig 8B ) . This suggests that oscillations driven minimally by the afferent input ( e . g . , spindle oscillations ) are stronger than oscillations driven mostly by afferent excitation ( e . g . , γ oscillations ) . In addition , the oscillation power was minimal ( < 1 ) above the principal diagonal except for high levels of ACh/NE modulation ( > 70% ) . Next , we determined the oscillatory state of the thalamic network under all examined combinations of ACh/NE and input levels and plotted the prominent oscillatory regimes across the entire parameter space ( Fig 8C ) . Consistent with our modeling hypothesis ( Fig 1B ) and pervious analysis , we observed slow δ oscillations with low ACh/NE modulation ( < 30% ) and minimal afferent excitation ( < 1 nS ) , spindle oscillations with medium ACh/NE modulation ( 20%-80% ) and relatively weak afferent input ( < 5 nS ) , and α , β and γ oscillations under high ACh/NE modulation ( > 70% ) with weak , moderate and strong inputs respectively ( Fig 8C ) . The spindle oscillations under medium ACh/NE modulation were induced by a transient input to RE neurons as mentioned above ( e . g . , Fig 8E1 ) and the spindle regime moved to lower input area as the ACh/NE level increased ( Fig 8C ) . For example , spindle appeared at 3 nS with 20% ACh/NE compared with 0 nS with 50% ACh/NE . This was because with low ACh/NE , TC cells fired spontaneous low-threshold bursts ( LTBs ) so higher afferent input was required to inactivate the low-threshold Ca2+ current to generate the state that permitted the occurrence of spindles . By comparison , for higher ACh/NE levels ( 50%-90% ) , TC cells no longer generated spontaneous LTBs and the network was relatively quiet without or with little afferent input ( e . g . , Fig 2B1 ) . In addition to the externally-induced spindles ( e . g . , triggered by a transient input to RE neurons ) , we observed spontaneous spindle oscillations under low ACh/NE modulation ( < 30% ) and with higher level of afferent input ( 4 . 5–6 . 5 nS; Fig 8C ) . Such spontaneous spindle oscillations were induced spontaneously by the random afferent input and usually lasted for a few cycles ( S13 Fig ) . Also , besides α oscillations at high ACh/NE modulation ( 100% ) , there were prominent α oscillations under low or medium ACh/NE modulation ( < 60% ) and with relatively large afferent input ( > 8 nS ) . This was because large random afferent input activated the high-threshold bursting dynamics in HTC cells ( see below ) . The region of such low-modulation α oscillations shifted to lower input area and became narrower as the ACh/NE level increased ( Fig 8C; pink area in the left side ) . There were also sporadic α oscillations right above the region of spindles . During the transition from δ to spindles and from spindles to α oscillations , there existed two prominent θ oscillation regions ( light green area ) : the first one appeared in the lower left corner while the second one started at higher input levels ( 7–12 nS ) with 0% ACh/NE and gradually decreased to 0 nS with 100% ACh/NE , indicating lower afferent input was needed to drive the network to θ oscillations as ACh/NE increased . Besides the prime oscillatory regimes , there was also non-oscillatory area in the parameter map ( light blue area in Fig 8C ) characterized by very low oscillation power ( Fig 8B ) . The first major non-oscillatory region was relatively narrow and appeared between the low-input θ oscillations and spontaneous spindle oscillations under low ACh/NE modulation ( < 30% ) . By comparison , the second major non-oscillatory region was much wider which occurred above the principal diagonal extending from 0% up to 80% ACh/NE . One important characteristic of this non-oscillatory region was that it started at the highest input level ( 20 nS ) without ACh/NE modulation ( i . e . , %0 ACh/NE ) and expanded into lower input area as the ACh/NE modulation increased ( Fig 8C , upper light blue area ) . To understand the cellular basis of such oscillatory state transitions , we plotted the voltage traces of representative thalamic neurons with different afferent input drive at three levels of ACh/NE modulation ( 0% , 50% and 100%; Fig 8D–8F ) . As shown earlier , the thalamic network generated slow δ oscillations ( 1–4 Hz ) with minimal input ( e . g . , 0 . 1 nS ) in the low ACh/NE condition ( Fig 2A ) . When the afferent input slightly increased ( e . g . , 2 nS ) , the LTB frequency of TC cells increased to the θ band ( 4–8 Hz; Fig 8D1 ) , which underlay θ oscillations . Thus , model simulation suggested that both δ and θ oscillations could be mediated by the LTBs of TC cells depending on the input . With further increase of the afferent drive ( e . g . , 5 nS ) , the network entered the non-oscillatory state where HTC cells stopped bursting and RTC cells burst sparsely ( Fig 8D2 ) . This was because as the membrane depolarization increased , the low-threshold T-type Ca2+ current started to inactivate , reducing the intrinsic bursting dynamics of TC cells . Consequently , the spontaneous HTC bursts driven by random Poisson inputs were not able to induce synchronous bursts ( only burstlets ) but after-hyperpolarization in neighboring cells through gap junctions . As a result , the coupled HTC cells eventually became silent . Indeed , when the gap junctions were blocked , HTC cells were able to burst randomly ( S14 Fig ) . When the afferent drive increased beyond 6 . 5 nS ( e . g . , 10 nS ) , HTC cells started to burst synchronously again owing to the activation of the high-threshold T-type Ca2+ current and the bursting frequency went into the θ band again ( 7 . 3 Hz , Fig 8D3 ) . As the afferent input increased further ( e . g . , 15 nS ) , the burst frequency of HTC cells rose to the α band ( 9 . 2 Hz , Fig 8D4 ) . At the highest afferent input tested ( 20 nS ) , HTC cells switched from HTBs to spare tonic spiking because of inactivation of the high-threshold T-type Ca2+ current and the network became desynchronized in the presence of strong random inputs ( S15 Fig ) . To recapitulate , at the lowest level of ACh/NE ( 0% ) , as the afferent input increased , the thalamic network switched from slow δ oscillations to slow θ oscillations both mediated by the LTBs of TC cells . After a brief non-oscillatory state , the network exhibited spontaneous spindle oscillations followed by θ and α oscillations mediated by HTBs of HTC cells . The network eventually became desynchronized with strong afferent drive . With medium level of ACh/NE modulation ( 50% ) , the thalamic network exhibited externally- induced spindle activity for relatively weak input ( 0–3 nS; Fig 8E1 , also Fig 2B ) . The duration of spindles reduced with afferent excitation ( S16 Fig ) because depolarization increased the inactivation of the low-threshold Ca2+ current in TC cells making rebound LTBs less effective . Increasing the afferent input to 3 . 5 nS generated intermittent θ or spontaneous spindle-like oscillations with an oscillation frequency of 7 . 9 Hz ( Fig 8E2 ) . Specifically , the network driven by random Poisson inputs burst spontaneously for about 500 ms and stopped for about 500 ms before bursting again . Since the inter-burst interval ( ~500 ms ) was relatively short , such network behavior was classified as intermittent or transient θ/α oscillations . When the afferent input further increased to 5 nS , the network generated continuous oscillations in the θ band ( ~ 6 Hz ) which was mediated by HTBs of HTC cells ( Fig 8E3 ) . With large afferent drive ( e . g . , 15 nS ) , HTC cells switched from periodic HTBs to random sparse firing and the network became desynchronized ( Fig 8E4 ) . Similar to the non-oscillatory state in the low ACh/NE and high input condition ( 0% ACh/NE , 20 nS input; S15 Fig ) , the network desynchronization was caused by the inactivation of the high-threshold T-type Ca2+ current in the presence of random input drive ( i . e . , the gap junctions were no longer able to synchronize HTC cells with sparse random spikes ) . Notably , with medium ACh/NE modulation , the network entered the desynchronizing state at a much lower input level ( 0% ACh/NE: 20 nS; 50% ACh/NE: 9 . 5 nS; Fig 8C ) . This was due to the fact that with higher ACh/NE , the high-threshold T-type Ca2+ current started to inactivate at smaller input intensity . As a result , the non-oscillatory state expanded into lower input region when the ACh/NE modulation increased ( Fig 8C , upper light blue area ) . Under the condition of high ACh/NE modulation ( 100% ) , the thalamic network generated θ oscillations ( 6 . 7 Hz ) without any input ( 0 nS , Fig 8F1 ) due to spontaneous HTBs of HTC cells . The bursting frequency increased to α band ( 8–14 Hz ) when the afferent input slightly increased ( e . g . , 1 . 5 nS , Fig 8F2; similar to Fig 2C1 ) . With further increase of the afferent input ( e . g . , 10 nS ) , HTC cells switched from HTBs to tonic spiking and the network synchronized at the β frequency band ( 23 . 8 Hz; Fig 8F3 ) . With strong afferent excitation ( e . g . , 15 nS ) , the oscillation frequency of the thalamic network increased to the γ band ( 31 . 7 Hz , Fig 8F4; similar to Fig 2D1 ) , which gave rise to potential γ oscillations . Note that INs had no spiking activities for medium or large afferent inputs ( Fig 8F3 and 8F4 , upper middle ) , different from default γ oscillations ( Fig 2D1 , upper middle ) . This was because INs did not receive direct afferent inputs in producing the oscillation map , while INs received weak afferent inputs ( 1 . 5 nS , Table 1 ) in the default γ simulation . In addition , different from the desynchronizing state under the low and medium ACh/NE conditions ( S15 Fig and Fig 8E4 ) , strong afferent drive did not disrupt network synchrony in the high ACh/NE condition ( >80%; Fig 8F3 and 8F4 ) . This was because HTC cells were much more excitable with high ACh/NE modulation and fired rhythmic spiking activities that were synchronized by gap junctions even when the high-threshold T-type Ca2+ current was inactivated . Thus , the model suggests that fast β/γ oscillations only occur at high ACh/NE level in the thalamic network . Lastly , we applied periodic stimuli to the thalamic network to examine how distinct thalamic oscillations are modulated by rhythmic perturbations . The phasic pulsatile stimuli were introduced to the LGN and we assumed that all TC cells and IN neurons received the same stimulation input . To analyze the impact of stimulation on thalamic oscillation dynamics , we plotted the normalized color-coded frequency power spectrum of the sLFP ( frequency spectrum heat map ) in response to ascending stimulation ( 1–50 Hz ) with a fixed stimulation amplitude ( 0 . 2 nA ) for three major oscillatory states ( δ , α and γ oscillations , Fig 9A1–9C1 ) . In addition , to examine how the dominant oscillatory dynamics varied with the stimulation frequency , we plotted the dominant network oscillation frequency with normalized spectral peak as a function of the stimulation frequency for all three oscillatory states in Fig 9A2–9C2 . Consistent with a recent computational study of an abstract cortical model [44] , stimulation induced entrainment and resonance in the thalamic network model in all three oscillatory states . Importantly , we found that the occurrence of these phenomena was state-dependent . Entrainment , a response pattern where the intrinsic oscillations are locked to the simulation , was reflected by the highlighted spectral power along the diagonal with multiple harmonic and/or subharmonic components ( above and below the diagonal ) in the frequency spectrum heat map ( Fig 9A1–9C1 ) . Entrainment was much more prominent during stimulation of γ oscillations than δ and α oscillations indicated by the longer and higher diagonal power during γ oscillations ( compare Fig 9C1 with Fig 9A1 and 9B1 ) . The state-dependent entrainment effect was also evident in the dominant frequency plot where the entrained frequency range during primary entrainment ( 1:1 entrainment ) was much wider during stimulation of γ oscillations than δ and α oscillations ( compare Fig 9C2 with Fig 9A2 and 9B2 , top; enclosed by red ellipses ) . In addition , we observed discontinuous entrainment where the thalamic network switched between the entrained and unentrained states during stimulation of α oscillations , but not during the other two oscillatory states ( compare Fig 9B2 with Fig 9A2 and 9C2 , top; enclosed by red ellipses ) . The entrainment behavior of the thalamic network during δ oscillations is illustrated in Fig 9A3 . In response to 6 Hz stimulation , all four types of thalamic neurons fired highly synchronized bursts at the same stimulation frequency ( 6 Hz ) that were tightly phase-locked to the stimulation pulses . We also note that primary entrainment occurred when the stimulation frequency was close to but mostly higher than the endogenous frequency for all three oscillatory states ( Fig 9A2–9C2 , top; enclosed by red ellipses ) , suggesting that stimulation favored higher frequency entrainment . For example , primary entrainment occurred at 3–10 Hz during stimulation of δ oscillations ( endogenous frequency: 3 . 7 Hz ) and took place at 19–47 Hz during stimulation of γ oscillations ( endogenous frequency: 30 . 5 Hz ) . Besides primary entrainment , thalamic network oscillations were also shaped by subharmonic as well as harmonic entrainment ( indicated by blue , cyan and magenta ellipses , Fig 9A2–9C2 , top ) . The thalamic network activity during subharmonic entrainment of α oscillations is illustrated in Fig 9B3 . In response to 22 Hz stimulation , HTC cells burst at half of the stimulation frequency ( 11 Hz ) and the whole network synchronized at 11 Hz . By comparison , during ( the first ) harmonic entrainment of γ oscillations , HTC cells oscillated at twice the stimulation frequency ( S17 Fig ) . Stimulation of the LGN also induced resonance , an enhancement of oscillation power when the stimulation frequency was close to the endogenous frequency , its harmonics and/or subharmonics . The thalamic neuronal activity during primary resonance is exemplified in Fig 9C3 . When the LGN was stimulated at 35 Hz , close to the endogenous frequency ( 30 . 5 Hz ) , the spiking activities of all four types of neurons became more synchronized and rhythmic than without stimulation ( compare Fig 9C3 with Fig 2D2 ) . Resonance occurred in all three oscillatory states ( Fig 9A2–9C2 , bottom ) , but with substantial differences among the states . During stimulation of γ oscillations , the primary resonance peak was much wider than that during stimulation of δ or α oscillations ( compare Fig 9C2 with Fig 9A2 and 9B2 , bottom; enclosed by red ellipses ) , in line with much wider primary entrained frequency range during stimulation of γ oscillations . Notably , similar to entrainment , resonance was asymmetric with respect to the endogenous frequency and favored higher frequency stimulation . For instance , the primary resonance peak occurred at 5 and 6 Hz during stimulation of δ oscillations ( Fig 9A2 , bottom; enclosed by red ellipse ) and 10 and 11 Hz during stimulation of α oscillations ( Fig 9B2 , bottom; enclosed by red ellipse ) , both of which were higher than their respective endogenous frequencies ( 3 . 7 Hz and 9 . 2 Hz respectively ) . Such asymmetry was most prominent during stimulation of γ oscillations . Although the endogenous γ frequency was 30 . 5 Hz , stimulation induced maximal resonance ( > 60% ) between 35 and 41 Hz ( Fig 9C2 , bottom; enclosed by red ellipse ) . The strong bias towards higher frequency in both entrainment and resonance suggests that stimulation increases the endogenous frequency of thalamic oscillations by enhancing the excitability of TC neurons . In addition to resonance enhancement , we observed substantial power suppression during high frequency ( > 25 Hz ) stimulation of α oscillations ( Fig 9B2 , bottom; indicated by black arrow ) , but not during the other two oscillatory states . This was because high frequency stimulation switched HTC bursting to tonic spiking and suppressed RTC activities by increasing IN firing ( S18 Fig ) . Overall , stimulation of the thalamic network induces state-dependent entrainment and resonance , which are stronger during γ oscillations than δ and α oscillations .
The key for the genesis of multiple distinct oscillations is that TC cells exhibit multiple oscillatory bursting/spiking patterns depending on neuromodulation and afferent excitation level ( S1 Fig ) . In the low ACh/NE modulation state , TC cells are sufficiently hyperpolarized to generate low-threshold bursts ( LTBs ) in the δ frequency band mediated by the low-threshold T-type Ca2+ current [51 , 52] . Such low-frequency LTBs form the neuronal basis of δ oscillations ( Fig 10 ) . In the medium ACh/NE modulation state , although TC cells are not spontaneously bursting , hyperpolarization of TC cells by afferent input or inhibition evoked rebound LTBs after the release of hyperpolarization [53 , 54] . Such hyperpolarization-induced rebound LTBs are necessary for spindle oscillations ( Fig 10 ) . In the high ACh/NE modulation state , HTC cells fire high-threshold bursts ( HTBs ) in the θ/α frequency band that mediate θ or α oscillations depending on afferent excitation level ( Fig 10; [7 , 12] ) . Stronger depolarization of HTC cells by afferent input switches HTBs into high frequency tonic spiking enabling γ oscillations ( Fig 10 ) . Such high frequency tonic spiking of TC cells was supported by experimental data that TC neurons exhibited fast oscillatory discharge in the γ frequency band in LGN ( ~50 Hz; [11] ) and in the ventroanterior-ventrolateral ( VA-VL ) complex ( 20–40 Hz; [27] ) . Thus , the rhythmic burst/spiking properties of TC neurons provide the cellular mechanism underlying multiple distinct thalamic oscillations [55] . To generate oscillations at the population level , the rhythmic burst firing or tonic spiking of TC neurons has to be synchronized . We found that the specific connectivity of the thalamic network endows it with the capability to synchronize under different neuromodulatory states and over a wide range of frequencies . First , the existence of gap junctions plays a crucial role in the synchronization of network activity . In particular , the gap junctions between HTC cells [7 , 12 , 17 , 18] enable them to serve as a “pacemaker” or synchronization “engine” of the circuit that remain synchronized even in the presence of strong random noisy inputs . We showed that thalamic oscillations were either impaired or eliminated if the HTC gap junctions were removed , consistent with experimental observation [12 , 35] . Besides HTC gap junctions , the weaker gap junctions between HTC and RTC cells and the inter-RE gap junctions also contribute to the synchronization of the network . Second , the feedforward IN inhibition and feedback RE inhibition lead to synchronization of RTC cells . Different from HTC cells , there are no gap junctions among RTC neurons [7 , 17] . Nevertheless , synchronized HTC activity propagates to both IN and RE neurons , which constrain RTC firing via feedforward inhibition . As RTC cells also project excitatory inputs on RE neurons , they receive feedback RE inhibition as well which enhances the synchronization . During low frequency oscillations ( δ , spindle and α ) , we observed that either IN or RE inhibition was sufficient to synchronize RTC activity , while during fast frequency oscillations ( β/γ ) , simultaneous rhythmic IN and RE inhibition was needed for high level of RTC synchrony due to strong random noisy inputs ( S9 Fig ) . Thus , the presence of both IN and RE inhibition strengthens the synchronization of TC neurons . Third , multiple mechanisms contribute to the generation , synchronization and stability of thalamic oscillations . The specific connectivity of the thalamic circuit allows for the synergy of multiple mechanisms in the generation of synchronized oscillations . For example , δ oscillations are synchronized by both gap junctions and inhibition ( Fig 4 ) . During spindle oscillations , both the feedforward IN and feedback RE inhibition contribute to the rebound bursting of TC cells . In particular , the feedforward HTC→IN→RTC inhibition could also lead to feedback inhibition on HTC cells via the HTC-RTC gap junctions . Similarly , during fast β/γ oscillations , while the HTC gap junctions play a major role , the feedback RE inhibition could also contribute to fast oscillations if the inhibitory strength is sufficiently strong . We hypothesize that the existence of multiple synchronizing mechanisms coupled with the strong intrinsic oscillatory properties of TC cells enables the thalamic network to serve as a pacemaker during thalamocortical oscillations , consistent with experimental observation [56] . To further test the hypothesis that generation and transition of distinct thalamic oscillations are functions of both ACh/NE neuromodulation and afferent excitation , we varied the ACh/NE and input levels systematically to identify the prominent oscillatory regimes across the entire parameter space . The oscillation transition map ( Fig 8C ) confirmed the existence of multiple distinct oscillations under physiological conditions ( Fig 2B ) : δ oscillations with low ACh/NE modulation and minimal input ( deep sleep ) ; spindle oscillations with medium ACh/NE modulation and slight to weak inputs ( light sleep ) ; α/θ oscillations with high ACh/NE modulation and weak input ( relaxed wakefulness ) and γ/β oscillations with high ACh/NE modulation and strong input ( arousal and attention ) . One interesting and surprising finding from the oscillation transition map is that it reveals prominent α oscillations under low ACh/NE modulation and with large afferent input thus extending the original model hypothesis where α oscillations occur under high ACh/NE modulation and with low afferent input ( compare Fig 8C with Fig 1B ) . Note that α oscillations arising from these two different regions have different physiological implications . While α oscillations under high ACh/NE modulation correspond to the state of relaxed wakefulness , α oscillations under low ACh/NE modulation represent a non-physiological state in deep sleep where the thalamus receives strong afferent drive . In addition , the oscillation transition map reveals several important insights and predictions as to the distribution and transition of thalamic oscillations . First , there exist two separate regions for the generation of θ oscillations . The first region locates between δ and spindle oscillations and is mediated by the LTBs of TC cells , while the second region resides between spindle and α oscillations and is mediated by HTBs of HTC cells . Thus , the model predicts the existence of θ oscillations during the transition from spindle to δ oscillations . Also , persistent and coherent θ oscillations mediated by LTBs are definitive signatures of a number of neurological or psychiatric disorders including Parkinson’s disease , major depressive disorder ( MDD ) and schizophrenia [6 , 7] . Hence , the θ oscillation region mediated by LTBs could potentially correspond to a pathological state . Second , spontaneous spindle oscillations can be generated under relatively low ACh/NE modulation but with moderate level afferent input . Spindle oscillations can be initiated by a number of mechanisms including spontaneous firing from a more excitable region of the thalamus , spontaneous oscillating TC cells or cortical stimulation through excitation of RE cells [57] . In the default simulation ( Fig 2B ) , we used a brief input to RE cells to induce spindle oscillations . Here we showed that spindle oscillations could also be initiated by spontaneous firing of TC cells given that the random background inputs are relatively high , consistent with previous hypotheses [57] . Third , there exist transient θ/α oscillations under medium ACh/NE modulation and with moderate level of afferent input . During the transition from spindle oscillations to persistent θ oscillations under medium level ACh/NE ( e . g . , 50% ) , we observed transient θ/α oscillations which lasted for a few hundred milliseconds ( e . g . , Fig 8E2 ) . Such transient θ/α oscillations were induced by a surge of background inputs , sustained a few cycles through the TC-RE interaction and terminated because of the inactivation of the low-threshold Ca2+ current ( due to afferent input; returning back the resting state ) , similar to spontaneous spindle oscillations . Interestingly , recent experimental studies discovered transient or intermittent α/β events in the awake mammalian neocortex [58 , 59] . Combined experimental and computational evidence showed that such transient oscillations emerged from the integration of synchronous bursts of excitatory synaptic drive targeting proximal and distal dendrites of pyramidal neurons [59] . Thus , though both the thalamus and neocortex are capable of generating transient rhythms , their underlying mechanisms may be different . Fourth , the effect of afferent input is somewhat equivalent to ACh/NE modulation . For example , without afferent input ( 0 nS ) , as the ACh/NE modulation increased , the thalamic oscillatory state switched from δ oscillations to θ oscillations and to spindle oscillations and to θ oscillations again ( Fig 8C ) . Similarly , for 0% ACh/NE , as the afferent input increased , the thalamic oscillation switched from δ oscillations to θ oscillations and to spindle oscillations after a brief quiescent state followed by θ oscillations again ( Fig 8C ) . Lastly , fast frequency β or γ oscillations in the thalamus can only be generated under high ACh/NE modulation . This is because under low and medium ACh/NE modulation , large ( random ) afferent input desynchronizes the network when the HTBs of HTC cells switch to sparse tonic spiking . This is consistent with the experimental data that fast γ oscillations in the thalamus are supported by cholinergic projection from the brainstem [27] and acetylcholine release contributes to gamma oscillations in prefrontal cortex during attention [60] . We used a brief depolarizing input ( to RE neurons ) to trigger spindle oscillations in the default simulation ( Fig 2B ) . Such brief depolarization could arise from the cortex; for example , the initial portion of the cortical up state during slow oscillations ( <1 Hz ) triggers thalamic spindles [61 , 62] and cortical stimulation induces spindle oscillations in the thalamus [29] . Alternatively , such transient depolarization may result from a synchronized surge of random background inputs to TC or RE neurons , as discussed above . In either case , spindle oscillations are generated internally from the thalamic circuit in the absence of cortical modulation , as observed experimentally [63 , 64] . Besides , substantial depolarization of TC cells is required to generate γ rhythms in the thalamic network [27 , 37] . Such strong depolarization could result from a combination of sensory stimulus [11] , elevated cholinergic neuromodulation [27] and increased corticothalamic projection to the thalamus [24] . Independent of the source of depolarization , it should be emphasized that we modeled the TC depolarization ( in the high ACh/NE modulation state ) by increasing the synaptic strength of the random Poisson inputs to TC cells which did not contain any rhythmic structure . Hence , although the cortex may provide necessary inputs to trigger spindles and depolarize TC neurons during γ oscillations , it is through the intra-thalamic mechanisms that the thalamic model produces distinct states of oscillatory patterns , in agreement with experimental findings [2 , 24 , 27] . Our modeling results suggest that the thalamus could be a driving force for thalamocortical oscillations , consistent with experimental observations [7 , 8 , 46] . Nevertheless , this prediction does not preclude the existence of cortically-originated oscillations in the thalamocortical systems . Indeed , δ oscillations are found to contain both thalamic and cortical components [37] and the cortical δ waves persist in the absence of the thalamus [65] . Similarly , α oscillations could be of cortical origin mediated by layer 5 pyramidal cells [10] . Also , corticothalamic feedback strongly modulates spindle oscillations [33] and synchronizes spindle waves to widespread cortical areas [63] . Moreover , γ oscillations in both the visual and motor cortex could arise from intracortical mechanisms [9 , 66] . Thus , it is possible that independent neural generators exist for distinct thalamic and cortical oscillations and the corticothalamic feedback synchronizes the thalamic and cortical rhythms into coherent thalamocortical oscillations [3 , 37 , 67] . Indeed , it has been proposed that three cardinal oscillators ( one cortical oscillator and two thalamic oscillators ) underlie the generation of the slow ( < 1 Hz ) electroencephalogram ( EEG ) rhythm of NREM sleep through intricate dynamic interactions [68] . Future studies are needed to examine how independent cortical and thalamic oscillators interact dynamically to create coherent rhythms in the thalamocortical system . Rhythmic stimulation has become an important and promising technique to study the causal role of oscillations in brain function [69 , 70] and as therapeutic intervention to treat neurological and psychiatric disorders [71 , 72] . As such , the responses of neuronal circuits to periodic perturbation have been examined in a number of experimental [73 , 74] and computational/theoretical studies [44 , 75–79] . Most of the existing theoretical models focused on the nonlinear dynamics of neurons using simplified neuronal models ( e . g . , integrate-and-fire neurons [75–77] ) and did not consider the interaction between rhythmic input and intrinsic neuronal dynamics . One notable exception was a cortical network model developed by Tiesinga [78] that investigated the dependence of LFP resonance on different biophysical time scales of the neuronal circuit . Since the Tiesinga model focused on pyramidal interneuron network gamma ( PING ) in the cortex , it remains unclear how the stimulation interacts with endogenous neural dynamics and depends on the physiological state of the thalamus . The manifestation of multiple distinct oscillations in one unified biophysical thalamic model enabled us to examine the impact of rhythmic stimulation on thalamic network dynamics . When subject to periodic stimulation , the thalamic network displayed two most prominent response patterns , entrainment and resonance , as shown previously in more abstract neuronal models [44 , 75–77] . Importantly , such response patterns were highly state dependent in that stimulation of γ oscillations induced much stronger entrainment and resonance than δ and α oscillations ( Fig 9 ) . We hypothesize that this is because γ oscillations are mainly driven by afferent excitation and the network synchrony or endogenous oscillation power is much lower compared with δ or α oscillations which are driven by intrinsic mechanisms ( i . e . , neuronal bursting; Fig 2 ) . Thus , during fast γ oscillations , the thalamus can more effectively relay sensory inputs than slow δ and α oscillations ( i . e . , easier to be entrained ) . Our simulation results are consistent with experimental observations that reduced α oscillation power enables entrainment [80] and fast γ band oscillations facilitate visual information processing [73 , 81] . In addition to entrainment and resonance , high frequency stimulation induced strong suppression on α oscillations , which was not observed during stimulation of δ and γ oscillations for the same stimulation amplitude ( Fig 9 ) . Taken together , our model is the first to demonstrate how the response patterns of the thalamic network to periodic stimulation depend on the physiological state or intrinsic dynamics of constituent neurons . As the particular oscillation state of the thalamus is set by both ACh/NE modulation and afferent excitation , our model thus suggests that ACh/NE and afferent excitation also define the thalamic response to brain stimulation . Thalamic oscillations have been modeled by a number of previous studies , either in isolated thalamus [57 , 82–84] or integrated thalamocortical network [15 , 85–93] . Several novel features distinguish our model from the existing thalamic models . First , our model incorporated a newly identified , special subclass of TC cells , high-threshold bursting TC cells ( HTCs ) and the gap junctions among HTCs [12 , 17 , 18] . In addition , the model contained gap junctions between HTCs and relay-mode TC cells ( RTCs ) [17] and gap junctions among reticular neurons [94 , 95] , which enhanced the thalamic network synchrony . The only existing model that consisted of HTCs and gap junctions was developed by Vijayan and Kopell [84] to study α oscillation and its role in stimulus processing . However , the Vijayan and Kopell model did not model INs explicitly and did not include the gap junctions between HTCs and RTCs and among RE cells . Also , the Vijayan and Kopell model did not consider the action of NE on α oscillations . As a result , the RE neurons were silent due to ACh inhibition during the muscarinic ACh receptor- induced α activity ( Fig 1B of [84] ) . By comparison , our model considered the combined action of ACh and NE so RE cells fired tonic spiking during α oscillations ( Fig 2C ) . Second , our thalamic model integrates multiple distinct oscillations ( δ , spindle , α/θ , and γ/β ) into one unified framework , in a similar spirit to a previous model of carbachol-induced δ , θ and γ oscillations in the hippocampus [96] . Notably , all four neuronal models ( HTC , RTC , IN and RE ) in the thalamic network were parameterized carefully to produce experimentally observed firing patterns under different neuromodulatory states ( S1 Text , S1 Fig and S2 Fig ) , enabling it to generate distinct , neuromodulation-dependent oscillation patterns . In contrast , most of the existing thalamic models focused on only one or two specific oscillatory patterns such as α oscillations [84] , spindle oscillations [15 , 57 , 83 , 89 , 90] , δ and spindle transition [82 , 85] , spindle and gamma oscillations [88] or spindle and slow ( < 1 Hz ) oscillations [92] . Other thalamocortical models have studied the transition from slow sleep oscillations ( < 1 Hz ) to asynchronous waking state [86 , 87] . It should be noted that a recent thalamocortical model from the Bazhenov group also investigated the generation and transition of multiple distinct oscillations during the sleep stages: spindle in NREM 2 , slow δ oscillations during NREM 3 and α or mu-like rhythm during REM sleep [93] . There are several differences between our model and the Krishan et al . model . First , the Krishan et al . model focused δ band activity mostly on slow oscillations ( 0 . 5–1 Hz ) generated from the cortex , while our model considered regular δ oscillations ( 1–4 Hz ) originated from thalamic TC cells . Second , the α or mu-like rhythm in the Krishan et al . model was generated by sparse synchronized single spiking in the cortex , while the α oscillation in our model was produced by synchronized high-threshold bursting in thalamic HTC cells . Third , our thalamic model was able to produce fast frequency γ/β oscillations , while the Krishan et al . model did not consider fast frequency oscillations . Last , we only varied two model parameters ( potassium leak conductance and synaptic input conductance ) to generate multiple oscillations , while more parameter change was needed to switch oscillation from one state to the other in the Krishan et al . model . Lastly , our model is the first to consider the co-regulation of neuromodulation and afferent input in thalamic oscillatory state transition . The effects of neuromodulation on thalamic oscillatory state transition have been examined in a number of models ( e . g . , [87 , 92 , 93] ) , but these previous models mostly focused on the sole action of neuromodulation . By comparison , we investigated the combined effect of neuromodulation and afferent input and demonstrated that the generation and transition of thalamic oscillations are functions of both neuromodulation and afferent excitation . As for any scientific study , there are several limitations to our work . First , as mentioned above , in addition to acetylcholine and norepinephrine , thalamic processing is subject to other neuromodulator action such as histamine ( HA ) , serotonin ( 5-HT ) and adenosine [25] . Similar to ACh and NE , most of these neuromodulators target the potassium leak current ( IKL ) in TC and RE neurons [25] . For example , application of HA leads to a slow depolarization in TC cells by decreasing the IKL [97] . Also , application of 5-HT in vitro strongly depolarize RE neurons via reduction of the IKL , an effect similar to noradrenergic modulation [98] . Thus , the net effect of these neuromodulators seems to be a depolarization of both TC and RE neurons and we hypothesize that activation of multiple neuromodulatory systems strengthens the neuromodulatory control of thalamic oscillatory state transition . Second , cholinergic and noradrenergic inputs modulate other ionic currents in thalamic cells other than IKL . For example , the hyperpolarization-activated cation current IH in TC cells is modulated by NE [99] and variation of IH conductance density was able to switch the oscillatory pattern between δ and spindle-like oscillations in a TC cell model [82] . Also , ACh may influence the muscarinic current IM and affect excitatory synaptic strength in the thalamus , similar to its modulatory effect in the cortex [100] . We only considered the modulation of IKL since it is the major target of cholinergic and noradrenergic inputs [25 , 26 , 101] and we attempt to model the transition of multiple oscillatory states with minimal change of parameters . Thus , our model can be considered as a minimal model of thalamic oscillation transition and inclusion of other ionic currents modulated by ACh/NE can be studied in future modeling studies . Third , in our model , δ oscillations occur during deep sleep stage , which corresponds to low ACh/NE modulation and minimal afferent excitation ( Fig 8C ) , consistent with experimental observation [29 , 31] . A recent experimental work in nonhuman primates demonstrates that in primary auditory cortex δ and γ oscillations co-occur during attentive processing while α and β oscillations occur during periods of inattention [102] . The existence of δ oscillations in the study could be due to the fact that both the auditory and visual stimuli are presented in the δ frequency band ( 1 . 6 Hz and 1 . 8 Hz respectively ) . In addition , our modeling results agree with the experimental data [102] that α oscillations occur during inattention while γ oscillations occur during attention . Future study is needed to examine δ and γ phase coupling during attention in a thalamocortical model subject to rhythmic δ band stimulation . Lastly , as the major goal of this study is to determine whether an isolated thalamus is capable of generating multiple distinct oscillation patterns , it presently does not include the cortex . The absence of the cortex prevents us modeling certain oscillatory pattern that is of cortical origin such as slow oscillations ( < 1 Hz; [86 , 87 , 103] ) . Nevertheless , a deeper mechanistic understanding of thalamic oscillations enables the systematic investigation of the cellular and circuit mechanisms of thalamocortical rhythms in the future .
The thalamic network consisted of both the lateral geniculate nucleus ( LGN ) and the reticular nucleus ( TRN ) ( Fig 1A ) . The LGN contained two major cell types: thalamocortical ( TC ) cells and local interneurons ( INs ) , and the TRN contained reticular ( RE ) cells . The TC cells were further divided into high-threshold bursting TC ( HTC ) cells and relay-mode TC ( RTC ) cells , based on whether TC cells can generate high-threshold bursting or not [12 , 18] . In the cat LGN , HTC cells account for about 25%-30% of the whole TC population [12 , 17] , while INs constitute about 25% of the total neuronal population in all dorsal thalamic nuclei of cats and primates [104] . Accordingly , the thalamic network contained 49 ( 7×7 ) HTC cells , 144 ( 12×12 ) RTC cells , 64 ( 8×8 ) INs and 100 ( 10×10 ) RE neurons , all placed in a two-dimensional grid . The major modeling results were robust to the network size when the synaptic weight and connectivity density were scaled accordingly . The network connectivity between the four types of neurons was illustrated in Fig 1A . HTC cells were connected with gap junctions [12 , 17] and provided feedforward excitation to INs , which in turn delivered feedforward inhibition to RTC cells [18] . A small percentage ( 20% ) of RTC cells were also connected with HTC cells via gap junctions [17] . Both HTC and RTC cells sent glutamatergic synapses to RE neurons , while receiving GABAergic feedback inhibition from the RE population [20 , 105] . RE neurons were connected with both gap junctions [94 , 95] and GABAergic synapses [106 , 107] . Lastly , a small percentage ( ~10% ) of RE neurons project GABAergic synapses to local interneurons [108] . In the model , all gap junction connections were local and the maximal distance between two electrically coupled neurons was two units ( the distance between two adjacent neurons in horizontal or vertical direction was assumed to be one unit ) . Each HTC cell formed gap junctions with neighboring HTC cells with a random connection probability . Also , 20% of RTC cells ( randomly selected ) formed gap junctions with neighboring HTC cells and 20% of RE cells ( randomly selected ) formed gap junctions with neighboring RE cells . For all gap junction connections , the connection probability was taken to be 30% within the local region . By comparison , all chemical synapses were global in this relatively small network . The connection probability ( 0 . 3 ) was higher for the TC-IN connections ( including HTC→IN and IN→RTC projections ) than that ( 0 . 2 ) for the TC-RE connections ( including HTC→RE , RTC→RE , RE→HTC and RE→RTC projections ) because TC cells show higher correlation with INs than with RE cells during α oscillations in cats [18] . A connection probability of 0 . 2 was used for the RE→RE synapses , while a much smaller probability ( 0 . 05 ) was used for the RE→IN synapses according to experimental data [108] . Following previous “point” models of thalamic cells [57 , 82–84 , 86 , 109] , all single cell models in the thalamic network contained one single compartment and multiple ionic currents described by the Hodgkin-Huxley formulism [110] . The current balance equation was given by: CmdVdt=−gL ( V−EL ) −gKL ( V−EKL ) −∑Iint−10−3∑IsynA+10−3IappA ( 1 ) where Cm = 1μF/cm2 is the membrane capacitance for all four types of neurons , gL is the leakage conductance ( nominal value: gL = 0 . 01 mS/cm2 for all four types of cells ) and gKL is the potassium leak conductance modulated by both ACh and NE ( see Table 1 and below for details ) . EL is the leakage reversal potential ( EL = −70 mV for both HTC and RTC cells; EL = −60 mV for both IN and RE neurons ) , and EKL is the reversal potential for the potassium leak current ( EKL = −90 mV for all neurons ) . Iint and Isyn are the intrinsic ionic currents ( in μA/cm2 ) and synaptic currents ( in nA ) respectively and Iapp is the externally applied current injection ( in nA ) . The following total membrane area ( A ) was used to normalize the synaptic and externally applied currents in Eq ( 1 ) : HTC and RTC cells: 2 . 9×10−4 cm2 [109]; INs: 1 . 7×10−4 cm2 [111]; RE cell: 1 . 43×10−4 cm2 [57 , 86] . All active ionic conductances were modeled using the Hodgkin-Huxley formalism [110] . Specifically , the ionic current for channel i , Ii , was modeled as Ii = gimphq ( V − Ei ) , where gi was its maximal conductance density , m its activation variable ( with exponent p ) , h its inactivation variable ( with exponent q ) , and Ei its reversal potential . The ICAN current utilized a slightly modified equation [114]: ICAN = gCANM ( [Ca]i ) m ( V − ECAN ) , where M ( [Ca]i ) = [Ca]i/ ( 0 . 2 + [Ca]i ) is a Michaelis-Menten function and [Ca]i denotes the intracellular calcium concentration . The kinetic equation for the gating variable x ( m or h ) satisfied a first-order kinetic model: dxdt=ϕxx∞ ( V , [Ca]i ) −xτx ( V , [Ca]i ) ( 2 ) where ϕx is a temperature-dependent factor , x∞ is the voltage or Ca2+- dependent steady state and τx is the voltage or Ca2+- dependent time constant in msec . Equivalently , Eq ( 2 ) can be written as: dxdt=ϕx ( αx ( V , [Ca]i ) ( 1−x ) −βx ( V , [Ca]i ) x ) ( 3 ) where αx and βx are the voltage or Ca2+- dependent rate constants with dimension of msec-1 . The maximal conductance densities of all ionic currents and the kinetic parameters of all gating variables for all four types of neurons are listed in S1 and S2 Tables , respectively . The sodium reversal potential was set to ENa = 50 mV and the potassium to EK = -90 mV . The reversal potentials for IH and ICAN currents were EH = -43 mV [113] and ECAN = 10 mV [114] respectively . The calcium reversal potential ( ECa ) was dynamically determined by the Nernst equation in all cell types in the model [118]: ECa=RT2Flog ( [Ca2+]o[Ca2+]i ) ( 4 ) where R = 8 . 31441 J/ ( mol°K ) , T = 309 . 15°K , F = 96 , 489 C/mol , and [Ca2+]o = 2 mM . Intracellular calcium was regulated by a simple first-order differential equation of the form [118 , 119]: d[Ca2+]idt=−ICazFw+[Ca2+]rest−[Ca2+]iτCa ( 5 ) where ICa is the summation of all Ca2+ currents , w is the thickness of the perimembrane “shell” in which calcium is able to affect membrane properties ( 0 . 5 μm ) , z = 2 is the valence of the Ca2+ ion , F is the Faraday constant , and τCa is the Ca2+ removal rate ( 10 ms for HTC , RTC and IN cells; 100 ms for RE cells ) . The resting Ca2+ concentration was set to be [Ca2+]rest = . 05 μM . The gap junction current was computed as Igap = ( Vpost − Vpre ) /Rg , where Vpre and Vpost are the membrane potentials of the presynaptic and postsynaptic neurons respectively . Gap junctional coupling was stronger among HTC cells than between HTC and RTC cells [17] . Accordingly , the gap junction resistance Rg was smaller for the HTC-HTC synapses ( 100 MΩ ) than for the HTC-RTC synapses ( 300 MΩ ) . The coupling strength between RE cells was set to be the same as that between HTC and RTC cells ( Rg = 300 MΩ ) . These gap junction resistance values were selected to match the experimental data [12 , 17 , 94] . In the model , glutamatergic synaptic current was mediated by both AMPA and NMDA receptors , while GABAergic synaptic current was mediated by GABAA receptors . The synaptic current was calculated by the following equation [119 , 120]: Isyn=gsynsB ( V ) ( V−Esyn ) ( 6 ) where gsyn is the maximal synaptic conductance and Esyn is the synaptic reversal potential . The default maximal conductances were: gAMPA = 6 nS and gNMDA = 3 nS for HTC→IN synapses , and gAMPA = 4 nS and gNMDA = 2 nS for the TC→RE synapses . The synaptic strength from inhibitory neurons ( INs and REs ) to TC cells was assumed to be higher than that among inhibitory neurons: gGABA = 3 nS for IN→RTC and RE→TC synapses while gGABA = 1 nS for both RE→IN and RE→RE synapses . Esyn = 0 mV for AMPA and NMDA currents , and Esyn = -80 mV for GABAA receptors in TC cells , while Esyn = -70 mV for GABAA receptors in RE neurons [106 , 121] . The function B ( V ) , which implements the Mg2+ block for NMDA currents , was defined as B ( V ) = 1/ ( 1 + exp ( − ( V + 25 ) /12 . 5 ) [86 , 112] . For AMPA and GABAA currents , B ( V ) = 1 . The gating variable s represents the fraction of open synaptic ion channels and obeys a first-order kinetics [82 , 83 , 122]: dsdt=α[T] ( 1−s ) −βs ( 7 ) where [T] is the concentration of neurotransmitter in the synapse and α and β are forward and backward binding rates . The neurotransmitter is assumed to be a brief pulse that has duration of 0 . 3 ms and amplitude of 0 . 5 mM following an action potential in the presynaptic neuron [57] . The channel opening rate constants ( α and β ) are given as: α = 0 . 94 ms-1 , β = 0 . 18 ms-1 for AMPA receptor current , α = 1 ms-1 , β = 0 . 0067 ms-1 for NMDA receptor current and α = 10 . 5 ms-1 , β = 0 . 166 ms-1 for GABAA receptor current . These values were taken from previous modeling studies [57 , 86 , 118] . A synaptic delay of 2 ms was introduced in all chemical synapses . Short-term synaptic depression was implemented in all chemical synapses and was modeled by scaling the maximal conductance of a given synaptic channel by a depression variable D , which represented the amount of available “synaptic vesicles” [86 , 87] . The variable D was updated according to a simple phenomenological rule [86 , 123]: D=1− ( 1−Di ( 1−U ) ) exp ( −t−tiτ ) ( 8 ) where U = 0 . 07 is the fraction of resources used per action potential , τ = 700 ms is the time constant of recovery of the synaptic vesicles . Di is the value of D immediately before the ith presynaptic spike and ti is the timing of the ith spike event . All neurons in the thalamic network received independent Poisson-distributed spike inputs at an average rate of 100 Hz ( results maintained unchanged if higher input rates were used when also scaling down the maximal synaptic input conductance ) . These random inputs represented both extrinsic sources of background noise and asynchronous visual input . This input was exclusively mediated by AMPA receptors modeled as an instantaneous step followed by an exponential decay with a time constant of 5 ms [124] . The synaptic input weights ( i . e . , maximal synaptic conductance ) for all neuronal types during different oscillatory states are given in Table 1 . Spindle oscillations were triggered by a transient input ( 100 ms × 100 pA ) injected into RE neurons which represented a cortical UP state or a surge of synchronized background inputs . To introduce heterogeneity into the model neurons , the leakage conductance ( gL ) of all neurons in the network was drawn from a uniform distribution within ±25% of the default value ( i . e . , 0 . 0075–0 . 0125 mS/cm2 ) . This leak conductance variation , random synaptic connectivity , and random external inputs constituted the model noise in the thalamic network . Our central modeling hypothesis is that the generation and transition of distinct thalamic oscillatory states are functions of both ACh/NE neuromodulation and afferent excitation level ( Fig 1B ) . Main motivations were the fact that different oscillatory states appear under different behavioral conditions in the sleep-wakefulness cycle ( Table 1 ) and that the transition from sleep to wakefulness is controlled mainly by activation of both cholinergic and noradrenergic neuromodulatory systems [25 , 26 , 125] . The thalamic oscillatory state transition is also a function of afferent excitation since thalamic neurons receive stronger afferent inputs during wakefulness than sleep due to activation of the sensory systems . Accordingly , we modeled four distinct oscillations ( δ , spindle , α and γ ) under three different ACh/NE modulation states ( low , medium and high ) corresponding to deep sleep , light sleep and awake conditions ( Table 1 ) . Specifically , δ oscillations were modeled in low ACh/NE modulation state with minimal afferent excitation; spindle oscillations were modeled in the medium ACh/NE modulation state with slight afferent excitation; and α and γ oscillations were modeled in the high ACh/NE modulation state with weak and strong afferent excitation respectively ( Table 1 ) . The two levels of afferent excitation in the high ACh/NE modulation state modeled two different behavioral conditions: awake with eyes closed ( where α oscillations are maximal ) and awake with eyes open and with attention . The effect of ACh/NE modulation was modeled by varying the potassium leak conductance in all four types of thalamic neurons while different afferent excitation was modeled by changing the maximal synaptic conductance of the random Poisson inputs to thalamic cells ( Table 1 ) . The rationale and selection of specific parameter values during each oscillatory state for both the potassium leak conductance and synaptic input conductance are described below . Acetylcholine ( ACh ) and norepinephrine ( NE ) alter the intrinsic excitability of thalamic neurons mainly by modulating the potassium leak current [25 , 26 , 101 , 126–128] . Both ACh and NE directly depolarize TC cells via blocking the potassium leak current [25 , 101 , 127] . By comparison , ACh inhibits LGN local interneurons and RE cells by activating the potassium leak current via muscarinic receptor activation [126 , 128] . In contrast , application of NE or stimulation of the locus coeruleus enhances the excitability of RE neurons by reducing the potassium leak current [25 , 26 , 127] . The combined effect of ACh and NE on RE cells is inferred from experimental data showing that a progressive hyperpolarization occurred in RE neurons during the transition from arousal to quite wakefulness and to deeper states of EEG-synchronized sleep [61 , 129] . We thus assumed that the excitatory effect of NE dominated the inhibitory effect of ACh on RE neurons so that the potassium leak current was decreased during transition from sleep to wakefulness . Also , since the action of NE on LGN interneurons remains unknown [25 , 26] , the ACh/NE neuromodulatory effect on interneurons was assumed to be mediated by cholinergic action only . We selected a periodic pulsatile stimulus that conceptually resembles the waveform of deep brain stimulation ( DBS ) or repetitive transcranial magnetic stimulation ( rTMS ) [44] . The stimulation was assumed to be global: all neurons in the LGN ( TC & IN cells ) received the same stimulus pattern . Stimulation consisted of a train of 10 ms brief square current pulse applied at different frequencies ranging from 1 Hz to 50 Hz with a 1 Hz/step increment . The stimulation amplitude was fixed at 0 . 2 nA . Stimulation was performed on three major oscillatory states ( δ , α and γ oscillations ) and stimulation at each frequency lasted for 1 second . The thalamic network model was coded with C++ . All simulation were performed using the fourth-order Runge-Kutta ( RK4 ) method with a fixed time step of 0 . 02 ms . Shorter simulation step did not change the results . The major simulation results were validated in a separate model implementation using the Brian simulator [131] . Simulations were run on a Dell Linux workstation under Ubuntu . The model source codes are available in the ModelDB database ( https://senselab . med . yale . edu/modeldb/ ) .
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Computational modeling has served as an important tool to understand the cellular and circuit mechanisms of thalamocortical oscillations . However , most of the existing thalamic models focus on only one particular oscillatory pattern such as alpha or spindle oscillations . Thus , it remains unclear whether the same thalamic circuitry on its own could generate all major oscillatory patterns and if so what mechanisms underlie the transition among these distinct states . Here we present a unified model of the thalamus that is capable of independently generating multiple distinct oscillations corresponding to different physiological conditions . We then mapped out the different thalamic oscillations by varying the ACh/NE modulatory level and input level systematically . Our simulation results offer a mechanistic understanding of thalamic oscillations and support the long standing notion of a thalamic “pacemaker” . It also suggests that pathological oscillations associated with neurological and psychiatric disorders may stem from malfunction of the thalamic circuitry .
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2017
|
Unified thalamic model generates multiple distinct oscillations with state-dependent entrainment by stimulation
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Selenium is an important trace element that occurs in proteins in the form of selenocysteine ( Sec ) and in tRNAs in the form of selenouridine . Recent large-scale metagenomics projects provide an opportunity for understanding global trends in trace element utilization . Herein , we characterized the selenoproteome of the microbial marine community derived from the Global Ocean Sampling ( GOS ) expedition . More than 3 , 600 selenoprotein gene sequences belonging to 58 protein families were detected , including sequences representing 7 newly identified selenoprotein families , such as homologs of ferredoxin–thioredoxin reductase and serine protease . In addition , a new eukaryotic selenoprotein family , thiol reductase GILT , was identified . Most GOS selenoprotein families originated from Cys-containing thiol oxidoreductases . In both Pacific and Atlantic microbial communities , SelW-like and SelD were the most widespread selenoproteins . Geographic location had little influence on Sec utilization as measured by selenoprotein variety and the number of selenoprotein genes detected; however , both higher temperature and marine ( as opposed to freshwater and other aquatic ) environment were associated with increased use of this amino acid . Selenoproteins were also detected with preference for either environment . We identified novel fusion forms of several selenoproteins that highlight redox activities of these proteins . Almost half of Cys-containing SelDs were fused with NADH dehydrogenase , whereas such SelD forms were rare in terrestrial organisms . The selenouridine utilization trait was also analyzed and showed an independent evolutionary relationship with Sec utilization . Overall , our study provides insights into global trends in microbial selenium utilization in marine environments .
Selenium ( Se ) is an essential trace element that exerts a number of health benefits yet is required only in small amounts [1]–[3] . It is incorporated into selenoproteins , many of which are important antioxidant enzymes , in all three domains of life , and occurs in these proteins in the form of selenocysteine ( Sec ) , the twenty-first amino acid in the genetic code [4]–[6] . Sec insertion is specified by a UGA codon , which is normally read as a stop signal . The decoding of UGA as Sec requires a translational recoding process that reprograms in-frame UGA codons to serve as Sec codons [5]–[8] . The mechanisms of selenoprotein biosynthesis have been the subject of numerous studies [5] , [7]–[12] . The translation of selenoprotein mRNAs requires both a cis-acting selenocysteine insertion sequence ( SECIS ) element , which is a hairpin structure residing in 3′-untranslated regions ( 3′-UTRs ) of selenoprotein mRNAs in eukaryota and archaea , or immediately downstream of Sec-encoding UGA codons in bacteria [7] , [13]–[16] , and several trans-acting factors dedicated to Sec incorporation [7] , [17] . In recent years , an increase in the number of genome sequencing projects combined with the rapidly emerging area of microbial metagenomics provided vast amount of gene and protein sequence data . However , selenoprotein genes are almost universally misannotated in these datasets because of the dual function of UGA codon . To address this problem , a variety of bioinformatics approaches have been developed and used for selenoprotein searches in both prokaryotes and eukaryotes [18]–[24] . With these programs , researchers successfully identified complete sets of selenoproteins ( selenoproteomes ) of individual organisms and environmental samples [20]–[26] . In early 2007 , three papers from the J . Craig Venter Institute were published reporting the results of the first phase of the large-scale metagenomic sequencing project – Global Ocean Sampling ( GOS ) expedition , a comprehensive survey of bacterial , archaeal and viral diversity of the world's oceans [27]–[29] . The general objective of this project was to expand our understanding of the microbial world by studying the gene complement of marine microbial communities . A metagenomics approach was used to sequence DNA isolated from selected sites of the aquatic microbial world . The previous Sargasso Sea project [30] , which reported environmental shotgun sequencing of marine microbes in the nutrient-limited Sargasso Sea , was considered as a pilot study for the subsequent GOS project . The GOS dataset encompasses 44 sequenced samples from diverse aquatic ( largely marine ) locations which contain a total of ∼7 . 7 million sequencing reads and more than 8 billion nucleotides [29] . These data not only provide opportunities for the identification and characterization of genes , species and communities , but have potentially far-reaching implications for biological energy production , bioremediation , and creating solutions for reduction/management of greenhouse gas levels . Within this framework , identification and characterization of selenoproteins in such a huge metagenomic dataset can shed light on the roles of Se in marine microbial communities . Previously , we examined the microbial selenoproteome of the Sargasso Sea via searches for Sec/cysteine ( Cys ) pairs in homologous sequences [25] . This method performed well and further research has shown that it is reliable in identifying selenoproteins in both organism-specific and environmental genomes [24] , [26] , [31] . In this study , we utilized a similar approach to analyze the distribution and composition of marine selenoproteins in the GOS shotgun dataset . More than 3 , 600 selenoprotein genes were detected , which is ten times the number of selenoproteins in the Sargasso Sea study . Several novel prokaryotic selenoprotein families were predicted . We further analyzed the dataset in various ways deriving insights into global trends in Se utilization .
Computational analysis of 44 sequenced GOS samples identified 3 , 506 selenoprotein sequences that belonged to previously described selenoprotein families ( Table 1 , all sequences are available in supplemental Dataset S1 ) . We also identified 58 , 225 Cys-containing homologs of these selenoproteins in the GOS sequences . Canonical correlation analysis of their occurrence based on sample size ( i . e . , total number of sequenced reads for each sample ) showed a strong correlation between the number of Cys-containing homologs and sample size ( correlation coefficient , CC , is 0 . 98 ) , but selenoproteins showed a weak correlation ( CC is 0 . 59 ) , suggesting widely different utilization of Sec in GOS samples ( Figure 1 ) . The samples were then clustered in various ways based on geographic location , water temperature ( tropical or temperate ) , and salinity ( sea water , fresh water , estuaries , or hypersaline lake ) . GS00c ( Sargasso Sea Station 3 , 425 selenoproteins , 12 . 1% of all detected selenoproteins ) , GS31 ( coastal upwelling near Galapagos Islands , 269 selenoproteins , 7 . 7% ) and GS17 ( Yucatan Channel in Caribbean Sea , 257 selenoproteins , 7 . 3% ) had the highest numbers of selenoproteins ( Figure 2A ) . Normalized occurrence of selenoproteins is shown in Figure 2B ( on average , GOS samples had 0 . 047% reads containing selenoprotein genes ) . We designated samples as selenoprotein-rich ( 6 samples ) if they contained 1 . 5 times the average level and selenoprotein-poor ( 11 samples ) if they had twice less the average level of selenoproteins ( Figure 2B ) . Geographically selenoprotein-rich and -poor samples did not cluster with each other , arguing against significant geographic differences in Sec utilization within the areas examined by the GOS project ( Figure 3 ) . It should be noted that except for the Sargasso Sea samples ( GS00a–GS01c ) , all other samples were collected in daytime between August 2003 and May 2004 , but most of them were collected in a narrow time period ( November 2003∼March 2004 , see Table 1 for sample date and time ) . Therefore , seasonal and yearly shifts in microbial community were considered to be small . However , it would be of interest to examine the contribution of seasonal factors to changes in the detected microbial selenoproteome once sufficient sampling becomes available . It has been reported that GOS samples grouped based on sequence similarity and taxonomy correlate with environmental parameters of GOS sites , particularly with regard to water temperature and salinity [29] . We found that except for sample GS09 , all selenoprotein-rich samples belonged to the marine “tropical & Sargasso” group which had an average sampling temperature at 25 . 5°C . Also , all samples from the Gulf of Mexico and Caribbean Sea ( GS15–GS19 ) showed elevated levels of selenoproteins ( Figure 2B ) , suggesting an active utilization of Sec in this area . In contrast , more than half of selenoprotein-poor samples ( 6 out of 11 ) were derived from temperate water area ( 12 . 1°C in average ) . This observation is consistent with our previous hypothesis that the use of Sec increases at higher temperature [32] . Besides , 5 of 7 nonmarine aquatic samples were selenoprotein-poor and the remaining two were borderline selenoprotein-poor ( Figure 2B ) . These nonmarine samples were geographically distant ( Figure 3 ) and located in different temperature zones . Further analysis of these samples with regard to habitat and environment suggested one likely factor , salinity , which was different between marine ( including both open ocean and coastal areas ) and nonmarine environments . Except for GS33 which was sampled from a hypersaline lagoon ( salinity is 63 . 4 ppt ) and showed low species richness [29] , [33] , all nonmarine aquatic samples were characterized by very low salinity ( <4 ppt ) [29] . This observation suggested that fresh water or low-salinity aquatic environments may work against Sec utilization . Although more extensive sample classification was difficult because of their number , water depth , fresh water input , proximity to land and filter size all appeared to affect Sec abundance to some extent . For example , the filter for most samples was 0 . 1∼0 . 8 µm , which concentrated mostly bacterial and archaeal microbial populations [29] . However , among the Sargasso Sea samples , GS01a , GS01b and GS01c were three subsamples from the same site , representing three distinct size fractions ( 3 . 0–20 , 0 . 8–3 . 0 , and 0 . 1–0 . 8 µm , respectively ) . This feature explains the fact that GS01a was relatively poor in selenoproteins even though it was located in the area rich in selenoproteins . Similarly , GS25 , another selenoprotein-poor sample , was collected using a larger filter ( 0 . 8–3 . 0 µm ) . No conclusion could be made regarding the relationship between nutrients ( such as carbon , nitrogen and phosphorus ) and Sec utilization . For example , in the nutrient-limited Sargasso Sea , both selenoprotein-rich ( GS00c ) and selenoprotein-poor ( GS00a ) samples were found . Similar observations were observed for coastal waters and estuaries where nutrients are more abundant , and for the open ocean where nutrients are limited . Additional factors , such as organism density , ecosystem complexity , light for phototrophs and fixed carbon/energy for chemotrophs may ultimately affect Sec utilization in microbial communities and warrant further studies once additional sequences become available . Selenoproteins detected through the homology-based procedure ( see details in Materials and Methods ) belonged to 51 previously described selenoprotein families ( Table 2 , details are shown in Table S1 ) . Most of these families had much more Cys-containing homologs than selenoproteins in the GOS dataset . All selenoprotein families previously detected in the Sargasso Sea were identified in the current GOS dataset , including prominent selenoproteins: SelW-like , selenophosphate synthetase ( SelD ) , proline reductase PrdB subunit , peroxiredoxin ( Prx ) , thioredoxin ( Trx ) , glutaredoxin ( Grx ) and a variety of Prx-like/Trx-like/Grx-like proteins [25] . Other selenoproteins included a UGSC-containing protein ( one of the major selenoprotein families in GOS samples , U is a one letter designation for Sec ) and several selenoproteins identified in various metagenomic sequencing projects [26] , [31] . In addition , we identified a large number of distant homologs of Prx-like/Trx-like selenoproteins . In order to analyze them against previously identified Prx-like/Trx-like proteins , we clustered these proteins into different subfamilies based on conserved domain classification ( Pfam/COG ) , motif features and phylogenetic analysis . Several selenoproteins were represented by single sequences only , e . g . , glycine reductase selenoprotein A ( GrdA ) and heterodisulfide reductase subunit A ( HdrA ) . In this case , sequencing errors that generated in-frame TGA codons could not be excluded; however , the fact that they corresponded to known selenoproteins and also possessed strong SECIS elements strongly suggested that they were true selenoproteins . 20 selenoprotein families were represented by more than 40 selenoprotein sequences and accounted for more than 94% of all selenoprotein sequences . Similar to the selenoproteome of the Sargasso Sea , the most abundant selenoprotein families were SelW-like , SelD , UGSC-containing protein , Prx , PrdB , and different subfamilies of Prx-like/Trx-like/Grx-like proteins . The current version of GOS selenoproteome has become the largest selenoproteome identified to date , and its analysis greatly expands our understanding of Sec utilization in microbial marine communities . Most selenoproteins with known function are oxidoreductases , and among 51 selenoprotein families detected in GOS samples , 33 ( 2887 sequences , 82 . 3% ) were homologs of known thiol oxidoreductases or possessed Trx-like fold ( Table 2 ) . Many of these selenoproteins contained a conserved UxxC/UxxS/CxxU/TxxU redox motif . In a small number of known selenoprotein genes , new Sec positions were identified . For example , a new redox motif ( CxxU ) was detected in Trx-like 1 family ( COG0526 , TrxA , thiol-disulfide isomerase and thioredoxins ) which normally contains a UxxC motif ( i . e . , in all previously identified sequences ) ( Figure 4A ) . In addition , several UxxU-containing sequences were detected in a Prx-like 2 family ( low similarity to pfam04592 , Selenoprotein P N-terminal region ) , which is a very distant homolog of known Prxs and has no strong homolog in any of the sequenced prokaryotic genomes ( Figure 4B ) . To further investigate the relationship between occurrence of selenoprotein families and sample features ( e . g . , marine versus nonmarine ) , we analyzed the most abundant selenoprotein families in each GOS sample separately ( Table 3 ) . Excluding the samples containing a small number of selenoproteins ( ≤15 ) , the majority selenoprotein families showed a similar occurrence in marine and nonmarine aquatic samples . In contrast , several selenoprotein families appeared to be differentially distributed . For example , SelW-like protein was generally the most abundant selenoprotein family in marine samples , whereas the UGSC-containing protein was most frequently utilized in nonmarine samples . As discussed above , salinity appears to be a factor that influences ( perhaps indirectly ) selenoprotein utilization . Figure 5 shows the occurrence of 20 most abundant selenoprotein families based on habitat . T-test was used to assess occurrence of each of these families in marine and nonmarine habitats . This analysis showed that occurrence of selenoprotein families in group I ( selenoproteins with lower occurrence in nonmarine samples , Figure 5 ) and II ( selenoproteins with lower occurrence in marine samples ) were statistically different between marine and nonmarine samples ( p<0 . 01 ) . Besides known selenoproteins , we identified 7 new selenoprotein families ( Table 4 , all sequences are available in supplemental Dataset S2 ) . They were represented by 2–11 individual TGA-containing sequences except for a hypothetical protein GOS_C which had 74 selenoprotein sequences . Among 7 new families , four either contained a domain of known function or were homologous to protein families with known/predicted functions . Particularly interesting was identification of ferredoxin-thioredoxin reductase ( FTR ) catalytic subunit and trypsin-like serine protease homologs . FTR is a key enzyme of the ferredoxin/thioredoxin system , which catalyzes reduction of thioredoxins with light-generated electrons [34]–[36] . Two Cys residues constitute a redox-active disulfide bridge functional in the reduction of Trx [37] . We identified two FTR selenoprotein sequences , including one ( JCVI_READ_1093012271142 ) which contained two predicted Sec residues exactly corresponding to the two redox-active Cys residues ( Figure 6A ) . Location of these Secs indicates functionality of these residues . Trypsin is a well-known serine protease which catalyzes the hydrolysis of peptide bonds . No redox function has been reported for members of this family . We found 9 selenoprotein sequences containing the trypsin-like domain ( COG5640 , secreted trypsin-like serine protease ) and the predicted Sec corresponded to a conserved Cys residue within this domain , suggesting a potential redox function for this Cys ( Figure 6B ) . No functional evidence could be obtained for hypothetical proteins GOS_A∼GOS_C . However , a Trx-like fold and a conserved UxxC motif were present in GOS_C , suggesting that this protein may have a thiol-based redox function . Multiple alignments of these new selenoproteins and their Cys-containing homologs ( Figure 6A–6G ) highlight sequence conservation of Sec/Cys pairs and their flanking regions . New selenoproteins contained stable bacterial SECIS-like elements downstream of Sec-encoding TGA codons ( Figure 7 ) . In addition , we detected several TGA-containing sequences , which showed similarity neither to known and new selenoproteins nor to each other . Some of them contained candidate SECIS elements . However , no definitive conclusions could be made regarding these sequences because of the possibility of sequencing errors . Future experimental verification is needed for these selenoprotein candidates . Previous analyses revealed that several selenoprotein families occur in both prokaryotes and eukaryotes , e . g . , SelW-like , GPx and deiodinase [25] . Recently , additional such selenoprotein families were identified , e . g . , methionine sulfoxide reductase A ( MsrA ) , Prx , SelL ( a Prx-like protein ) , arsenite S-adenosylmethyltransferase ( PRK11873 , arsM ) and several Prx-like/Trx-like proteins [31] , [38]–[40] . Most eukaryotic species containing these selenoproteins are aquatic organisms ( such as green algae and fish ) . In the GOS sequence dataset , more than 90% sequences are derived from bacteria whereas only 2 . 8% could be definitively assigned to the eukaryotic domain [27] . To distinguish bacterial and eukaryotic selenoproteins , we employed several approaches including phylogenetic analyses and investigation of eukaryotic SECIS elements . Our results suggested that all detected new and known selenoproteins that occur in both prokaryotes and eukaryotes could be assigned to the bacterial domain . In addition , several eukaryotic selenoproteins were detected in different GOS samples by homology analysis using known eukaryotic selenoproteins , including protein disulfide isomerase ( PDI ) , SelM , SelT , SelU and thioredoxin reductase ( data not shown ) . Although most of the reads containing these selenoprotein genes were too short to investigate the presence of eukaryotic SECIS element in 3′-UTR , phylogenetic analyses and the absence of bacterial SECIS elements suggested that these sequences are eukaryotic . Interestingly , a new eukaryotic selenoprotein family , gamma-interferon-inducible lysosomal thiol reductase ( GILT ) , was also detected . GILT is a key enzyme to facilitate complete unfolding of proteins destined for lysosomal degradation by releasing structural constraints imposed by intra- and inter-chain disulfide bonds [41] , [42] . No homologs of this protein are known in prokaryotes . In this study , we identified three selenoprotein sequences for this family . A eukaryotic SECIS element predicted by SECISearch [18] was found in the 3′-UTR of one selenoprotein gene , providing additional evidence that they are eukaryotic GILT selenoproteins . Multiple alignment of GILT sequences and the predicted eukaryotic SECIS element are shown in Figure 8 . We identified novel domain fusions in several selenoprotein families . One example involved Prx that was fused with a distant homolog of PP2C-type phosphatase ( smart00331 , PP2C_SIG , Figure 9A ) . The PP2C-type phosphatase superfamily includes several subgroups , such as RsbU that contains an additional N-terminal domain ( pfam08673 , RsbU_N ) and acts as a positive regulator of the activity of σB , the general stress-response σ factor of gram positive microorganisms [43] , [44] . Other PP2C-type phosphatase subfamilies include PrpC , SpoIIE , RsbP and RsbX [45]–[49] , in which the PP2C-type phosphatase domains are fused with different domains ( Figure 9B ) . We further checked the occurrence of this distant PP2C-type phosphatase in all sequenced bacteria and found orthologs only in a limited number ( no more than 20 ) of organisms in different bacterial phyla and fused with different domains ( Figure 9C ) . Phylogenetic analyses suggested that the Prx-fused phosphatases form a separate group within the PP2C-type phosphatase superfamily ( Figure 10 ) . Multiple alignments showed that several conserved residues are specific for this subgroup , especially a Cys residue which is present in all members of the Prx-fused subgroup but absent in other PP2C-type phosphatase subfamilies ( Figure 11 ) . This conserved Cys may also have a redox function . Surprisingly , one marine gliding bacterium , Microscilla marina , the only organism containing the Prx-fused phosphatase domain in Bacteroidetes , possessed a large number of such proteins . Compared to other organisms which contained only 1–2 members , 159 individual sequences containing this phosphatase subfamily were identified in M . marina , all of which had the conserved Cys residue and were fused with different domains , suggesting a particular importance of this distant PP2C-type phosphatase subfamily in this marine organism . Additional examples of domain fusions are shown in Figure S1 . Functions of most of these domains are not clear . However , as a rule , at least one conserved Cys was present in these sequences , suggesting a potential redox activity . For example , the UGSC-containing protein which likely has a Trx-like fold was fused with a conserved domain ( designated Unknown_1 , Figure S1A ) . Unknown_1 protein was also present in a limited number of aquatic organisms . Another example involved the fusion of a Prx-like 3 and Unknown_3 domain ( Figure S1D ) . There were three conserved Cys residues in Unknown_3 , including a conserved CxxC motif which may have a thiol-based redox function . Previously , we detected two fusions of SelD: ( i ) NADH dehydrogenase ( COG1252 , Ndh , FAD-containing subunit ) fusion [32] and ( ii ) Cys sulfinate desulfinase ( COG1104 , NifS ) fusion ( unpublished data ) . The Ndh-SelD fusion proteins were detected in several bacteria most of which were aquatic organisms . Such fusions were also observed in several lower eukaryotes , such as in Ostreococcus . In all detected fusion sequences , a conserved CxxC motif was present in the predicted active site of the SelD domain . However , this motif is very rare ( <5% ) in single-domain SelD proteins . The NifS-SelD fusion was only detected in Geobacter sp . FRC-32 ( an anaerobic , iron- and uranium-reducing deltaproteobacterium ) , and a CxxU motif was present in the active site of the SelD domain . Functions of the two fusion SelDs are not fully clear , but are expected to be involved in selenophosphate synthesis . In the GOS dataset , we detected hundreds of Ndh-SelD fusion proteins ( all containing the CxxC motif ) , which accounted for approximately 40% of all detected Cys-containing SelDs . In contrast , no NifS-SelD fusion was detected . Interestingly , we found that ∼5 . 6% of single-domain selenoprotein SelDs contained a CxxU motif . Figure 12 shows a multiple alignment of Ndh-SelD fusion proteins and other Sec/Cys-containing SelDs in both sequenced organisms and GOS samples . We also found several sequence reads containing two neighboring selenoprotein genes , including ten Prx/SelW sequences , one Prx/Prx-like 2 and one Prx-like 1/AhpD-like 2 sequences . Phylogenetic analysis showed that these Prx and SelW sequences were clustered in a small phylogenetic group , suggesting that they come from closely related organisms . Further analyses are needed to examine a possible functional link between these selenoproteins . In some prokaryotes , Se ( in the form of selenophosphate ) is also used for biosynthesis of a modified tRNA nucleoside , 5-methylaminomethyl-2-selenouridine ( mnm5Se2U ) , which is located in the wobble position of the anticodons of tRNALys , tRNAGlu , and tRNA1Gln [50]–[52] . The proposed function of mnm5Se2U involves codon-anticodon interactions that help base pair discrimination at the wobble position and/or translation efficiency [52] , [53] . A 2-selenouridine synthase ( YbbB ) is necessary to replace a sulfur atom in 2-thiouridine in these tRNAs with selenium [54] . Here , we investigated the occurrence of YbbB to assess the selenouridine utilization trait in the GOS samples . A total of 865 YbbB genes were identified in GOS sequences . Occurrence of YbbB in individual samples is shown in Figure 13A . In most GOS samples , the number of reads containing YbbB gene was proportional to the sample size ( CC is 0 . 87 ) . However , several samples appeared to have a significantly different distribution of YbbB . Similarly to selenoprotein classification of GOS samples , we clustered them into selenouridine-rich and selenouridine-poor . Previously , we have suggested a relatively independent relationship between Sec and selenouridine utilization [32] . In the current study , we examined correspondence between selenoprotein-rich/poor samples and selenouridine-rich/poor samples ( Figure 13B ) . Two selenoprotein-poor samples ( GS00a and GS33 ) were selenouridine-rich , whereas one selenoprotein-rich sample ( GS51 ) appeared to be a selenouridine-poor sample , implying no strong relationship of the two Se utilization traits in GOS samples . Also , no significant difference was observed for the occurrence of the selenouridine utilization trait in other selenoprotein-rich/poor samples , further suggesting a relatively independent relationship between them . Considering that Se supply in the sea water should be equal to co-occurring Sec-utilizing and selenouridine-utilizing organisms , substantial microbial taxonomic diversity might explain differences in Se utilization in different areas of the sea . No clear relationship was also found between selenouridine utilization and habitat types or geographic location . Except for GS01a ( a sample collected with a large filter ) , GS12 ( from the estuary close to Chesapeake Bay , MD ) was the only sample in which both Se utilization traits were limited . We also found high utilization of both traits in GS17 ( Caribbean Sea , Yucatan Channel ) .
In recent years , a number of metagenomic sequencing projects were carried out that enabled researchers to identify genes in both abundant and non-abundant microbes in a particular environment , providing a powerful tool to examine community organization and metabolism in natural microbial communities [30] , [55]–[57] . Similarly , identification of selenoprotein genes in these datasets may help in understanding the role of Se in microbial populations . In this study , we have used shotgun data from a recent GOS expedition [27]–[29] to characterize the distribution and composition of the selenoproteome in this largest to date marine metagenomic dataset . Our results highlight importance of Se utilization within marine microbial communities and provide a comprehensive analysis of Se-dependent proteins which are utilized by marine microorganisms . The GOS project produced a total of 7 . 7 million random sequence reads from the North Atlantic Ocean , the Panama Canal , and East and central Pacific Ocean gyre . In order to identify all selenoproteins in the GOS dataset we employed a procedure that analyzed Sec/Cys pairs in homologous sequences . A total of 3 , 506 sequences which belonged to 51 previously described prokaryotic selenoprotein families , and 102 sequences that corresponded to 7 new selenoprotein families were identified . Compared to smaller scale non-aquatic metagenomic projects , such as whale fall community and Waseca County farm soil metagenome [56] and human distal gut microbiome [57] , the GOS project produced hundreds of times more selenoproteins . Our current study generated by far the largest selenoproteome reported to date . If selenoproteins and their Cys-containing homologs are randomly used in marine microbes , the number of selenoproteins would be expected to be proportional to the number of sequence reads in GOS samples . However , whereas the correlation was good for Cys homologs , it was weak for selenoproteins . We normalized the occurrence of selenoproteins in each sample and found that all selenoprotein-rich samples originated from the sea water and almost all from the tropical sea areas . In contrast , half of the selenoprotein-poor samples were obtained from nonmarine aquatic environments ( including fresh and hypersaline water ) , and half of the marine selenoprotein-poor samples came from temperate sea areas . Thus , our data suggest that water salinity and temperature may influence Sec utilization . However , the fact that the occurrence of selenoproteins in some samples collected from sites with similar temperature and salinity was somewhat different suggests that additional factors may also affect Sec utilization . Moreover , other features of GOS samples ( e . g . , water depth , fraction filter and light intensity ) may also result in bias when comparing the samples . Among 51 previously characterized selenoprotein families , most were homologs of known thiol oxidoreductases or possessed Trx-like fold , consistent with the idea of redox function for selenoproteins in marine microorganisms . Twenty selenoprotein families , including SelW-like , SelD , Trx-like 1 and UGSC-containing proteins , were found to be the major selenoprotein families in GOS samples and represented approximately 95% of all detected selenoprotein sequences . Except for SelD , FdhA and UshA-like ( COG0737 , UshA , 5′-nucleotidase/2′ , 3′-cyclic phosphodiesterase and related esterases ) , all of these families contained conserved Cys-based redox motifs which are involved in a variety of redox functions . Comparison of the distributions of these major selenoprotein families in marine and nonmarine environments showed that a small number of selenoproteins exhibited significantly different occurrence in the two types of habitat . For example , SelW-like , DsbA 1 , Prx-like 2 , Prx-like 3 and Trx-like 3 were much more abundant in marine samples whereas UGSC-containing , AhpD-like 2 and Prx-like ( UGC-containing ) proteins were more abundant in nonmarine samples . Therefore , salinity and other factors affected the use of Sec , but this influence is not necessarily unidirectional and depends on specific selenoproteins affected . Seven new selenoprotein families were identified . Except for hypothetical protein GOS_C , which was represented by 74 selenoprotein sequences in the GOS dataset , occurrence of other new selenoprotein families was limited . Among these new families , FTR is a well-characterized enzyme involved in disulfide reduction in Trx . However , previous studies could not detect any Sec-containing form for this enzyme . In addition , several Sec-containing sequences were predicted for a trypsin-like family , suggesting a potential redox function for a particular Cys residue in this well-known serine protease family . Although functions of other new families are unclear , the fact that a CxxU motif was present in both FmdB putative regulatory protein family and putative secreted serine protease MucD , and that a UxxC motif was present in a hypothetical protein GOS_C , implied a thiol-related redox function . It has been reported that a small fraction ( less than 3% ) of reads in the GOS dataset is of eukaryotic origin ( e . g . , small-sized green algae ) . We did detect several eukaryotic selenoproteins , including a new selenoprotein family , GILT . Homologs of this protein family were only detected in eukaryotes . A eukaryotic SECIS element was detected in the 3′-UTR in one selenoprotein sequence . Although eukaryotic organisms containing the Sec-containing GILT are not known , future studies will likely identify such organisms . Domain fusions could help identify functionally-related proteins . We identified several new fusion events involving selenoproteins . Compared to their more common forms present in most organisms , these selenoproteins contained additional upstream or downstream domains fused into a single protein chain . Fusion events were observed for a variety of Trx-fold-containing selenoproteins , including Prx , Prx-like 2 , Prx-like 3 and UGSC-containing protein . Function of most of these fused domains is not clear; however , single or multiple conserved Cys residues were present in these domains , suggesting a potential redox function of these residues . In addition , almost half of the Cys-containing SelDs detected in the current GOS dataset were Ndh-SelD fusion proteins , all of which contained a conserved CxxC motif in the active sites . The abundance of Ndh-SelD fusion proteins in GOS samples suggests that this fusion SelD plays an important role in selenophosphate biosynthesis in marine/aquatic organisms . Given that Se is also utilized for biosynthesis of selenouridine in bacteria , distribution of the selenouridine trait was assessed by analyzing occurrence of YbbB in GOS samples . We identified selenouridine-rich and selenouridine-poor samples , which were not the same as Sec-rich/poor samples , suggesting that the two Se utilization traits are functionally independent ( but of course both depend on supply of Se ) . This observation is consistent with the previous hypothesis that Sec and selenouridine utilization traits are relatively independent even though both traits require SelD for selenophosphate biosynthesis [32] . In addition , no strong relationship was found between selenouridine utilization and habitat types ( marine or nonmarine ) or geographic location . Although both Se traits require Se supply or thus could influence evolution of each other , additional factors appear to play more important roles in the evolution and utilization of individual Se utilization traits . In this study , we report a comprehensive analysis of Sec utilization in marine microbial samples of the GOS expedition by characterizing the GOS selenoproteome . This is the first time that the microbial selenoprotein population is described in a global biogeographical context . Our analysis yielded the largest selenoprotein dataset to date , provided a variety of new insights into Sec utilization and revealed environmental factors that influence Sec utilization in the marine microbial world .
Shotgun reads of the GOS project were downloaded from the CAMERA ( Community Cyberinfrastructure for Advanced Marine Microbial Ecology Research and Analysis ) website at http://camera . calit2 . net . This dataset contains a total of 7 , 709 , 422 genomic sequences derived from 57 samples ( 13 samples are not fully sequenced ) , which cover a wide range of distinct surface marine environments as well as a few nonmarine aquatic samples [29] . The genomic sequences combined had 8 . 148 billion nucleotides . In addition , we downloaded the non-redundant ( NR ) protein database from the NCBI ftp server . It contained a total of 4 , 644 , 764 protein sequences . BLAST [58] was also obtained from the NCBI . Previously , we developed and employed a set of programs for automated selenoprotein searches [24]–[26] . However , since this approach is based on an exhaustive search of all possible Cys/Sec pairs for each Cys-containing sequence in the NR database , the computation procedure can become very expensive when the target sequence dataset is very large , as is the case in the GOS database . Therefore , we utilized an Open Science Grid ( OSG ) management system which is dedicated to supporting scientific research through the use of advanced networking technology and high performance computing [59] . We employed Condor-G software [60] , a powerful and full-featured task broker , to manage such a high throughput computing project on large collections of distributively owned computing resources . In addition , we used the Prairiefire , a 128-node , 256-processor Beowulf cluster supercomputer at the Research Computing Facility of the University of Nebraska – Lincoln . We used a strategy which we had successfully used in selenoprotein searches in other metagenomic datasets: Sargasso Sea and symbiotic microbial consortium of the marine oligochaete Olavius algarvensis [24]–[26] . Briefly , each Cys-containing sequence in the NR protein database was searched against the GOS dataset for top 1000 homologs using TBLASTN with E-value below 10 ( this step is the most time-consuming and was performed completely on the OSG system ) . Cys/TGA pairs were then selected and a minimum open reading frame ( ORF ) for each TGA-containing nucleotide sequence ( TGA was translated to Sec , U ) was obtained . After that , BLASTX and RPS-BLAST programs were used to analyze the conservation of TGA-flanking regions in all six reading frames as well as the presence of conserved domains . Remaining sequences were clustered based on sequence similarity and location of predicted Sec using BL2SEQ with an E-value below 10−4 . All clusters were further searched against NCBI NR protein and microbial genomic databases to identify conserved Cys-containing homologs . Sequences in the remaining clusters were manually analyzed for occurrence of bacterial SECIS elements using bSECISearch program [21] , and were classified into known selenoproteins and candidate selenoproteins ( i . e . , clusters having at least two Sec-containing sequences ) . In addition , an independent BLAST homology search for selected Sec-containing representatives of all previously identified prokaryotic selenoprotein families was performed . Finally , distinct representatives of all identified selenoprotein sequences were used to iteratively search against the GOS dataset for identification of additional distant Sec-containing homologs . We used CLUSTALW [61] with default parameters for multiple sequence alignments . Phylogeny was analyzed by PHYLIP programs [62] . Neighbor-joining ( NJ ) trees were obtained with NEIGHBOR and the most parsimonious trees were determined with PROTPARS . Robustness of these phylogenies was evaluated by two additional algorithms: maximum likelihood ( ML ) analysis with PHYML [63] and Bayesian estimation of phylogeny with MrBayes [64] .
|
Selenium ( Se ) is an essential micronutrient due to its requirement for biosynthesis and function of the 21st amino acid , selenocysteine ( Sec ) . Sec is found in the active sites of selenoproteins , most of which exhibit redox function , in all three domains of life . In recent years , genome sequencing projects provided a large volume of nucleotide and protein sequence information . Identification of complete sets of selenoproteins ( selenoproteomes ) of individual organisms and environmental samples is important for better understanding of Se utilization , biological functions of this element , and changes in Se use during evolution . Here , we describe a comprehensive analysis of the selenoproteome of the microbial marine community derived from the Global Ocean Sampling ( GOS ) expedition . More than 3 , 600 selenoprotein gene sequences belonging to 58 protein families were detected and analyzed . Our study generated the largest selenoproteome reported to date and provided important insights into microbial Se utilization and its evolutionary trends in marine environments .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"and",
"genomics/microbial",
"evolution",
"and",
"genomics"
] |
2008
|
Trends in Selenium Utilization in Marine Microbial World Revealed through the Analysis of the Global Ocean Sampling (GOS) Project
|
CCR5 plays immune functions and is the coreceptor for R5 HIV-1 strains . It exists in diverse conformations and oligomerization states . We interrogated the significance of the CCR5 structural diversity on HIV-1 infection . We show that envelope glycoproteins ( gp120s ) from different HIV-1 strains exhibit divergent binding levels to CCR5 on cell lines and primary cells , but not to CD4 or the CD4i monoclonal antibody E51 . This owed to differential binding of the gp120s to different CCR5 populations , which exist in varying quantities at the cell surface and are differentially expressed between different cell types . Some , but not all , of these populations are antigenically distinct conformations of the coreceptor . The different binding levels of gp120s also correspond to differences in their capacity to bind CCR5 dimers/oligomers . Mutating the CCR5 dimerization interface changed conformation of the CCR5 homodimers and modulated differentially the binding of distinct gp120s . Env-pseudotyped viruses also use particular CCR5 conformations for entry , which may differ between different viruses and represent a subset of those binding gp120s . In particular , even if gp120s can bind both CCR5 monomers and oligomers , impairment of CCR5 oligomerization improved viral entry , suggesting that HIV-1 prefers monomers for entry . From a functional standpoint , we illustrate that the nature of the CCR5 molecules to which gp120/HIV-1 binds shapes sensitivity to inhibition by CCR5 ligands and cellular tropism . Differences exist in the CCR5 populations between T-cells and macrophages , and this is associated with differential capacity to bind gp120s and to support viral entry . In macrophages , CCR5 structural plasticity is critical for entry of blood-derived R5 isolates , which , in contrast to prototypical M-tropic strains from brain tissues , cannot benefit from enhanced affinity for CD4 . Collectively , our results support a role for CCR5 heterogeneity in diversifying the phenotypic properties of HIV-1 isolates and provide new clues for development of CCR5-targeting drugs .
CC Chemokine Receptor 5 ( CCR5 ) is a G Protein-Coupled Receptor ( GPCR ) that regulates immune functions [1–3] . Members of the GPCR family exist in structurally diverse forms resulting from differential post-translational modifications and oscillation between different conformational and oligomerization states [4–8] . CCR5 itself oscillates between inactive and active conformations [9 , 10] . The existence of multiple CCR5 populations has been inferred from the observation that structurally distinct CCR5 ligands recognize different proportions of the receptor at the cell surface [11–15] . The nature and/or quantity of the CCR5 conformations may vary between different cell types [11 , 14] . CCR5 conformation is regulated by several factors , including signaling effectors and membrane lipid composition [12 , 13 , 16 , 17] , the receptor homo- and hetero-oligomerization [18–20] and the binding of ligands [21–26] . Computational analysis predicted that CCR5 can adopt an ensemble of twenty low-energy conformations , each of which being differentially favored by distinct ligands and receptor mutations [27] . However , the nature and the functional properties of the different CCR5 populations remain elusive . CCR5 is also hijacked by R5-tropic strains of HIV-1 for entry into immune cells [28–30] . HIV-1 entry begins with the interaction of the surface subunit ( gp120 ) of the viral envelope glycoprotein ( Env ) with cellular CD4 . Regions in gp120 ( the so-called bridging sheet and the V3 loop ) are then created/exposed and interact with distinct domains of CCR5 used as coreceptor , including the N-tail and the second extracellular loop ( ECL2 ) . This triggers fusion between the viral and cell membranes [31–36] . HIV-1 Env is subjected to high intra- and inter-patient structural diversity [37–39] . Sequence variability in Env allows HIV-1 to escape host immune responses and develop resistance to entry inhibitors . Distinct viral isolates may also differ in the way they utilize CCR5 [40] . However , it is not demonstrated whether different HIV-1 strains use differentially the diverse CCR5 populations and if so , whether a link exists between the nature of the CCR5 forms used by a virus and its role in HIV infection . Actually , the capacity of CCR5 ligands to inhibit HIV-1 entry may not correlate with the extent to which they bind to CCR5 [11] , hence indirectly suggesting that HIV-1 itself uses only subsets of cell surface receptors . Here we show that distinct HIV-1 strains may differ in the nature of the CCR5 molecules to which they bind , and this is likely to steer some of their phenotypic properties such as cellular tropism and capacity to escape inhibition by CCR5 ligands . This study thus highlights the relationship between CCR5 structural plasticity and diversification of HIV-1 phenotypic properties and has implications on the development of HIV-1 entry inhibitors targeting CCR5 .
To investigate whether different HIV-1 strains bind differentially to distinct CCR5 subpopulations , we first measured binding of 35S-labeled gp120s to CCR5-expressing HEK 293T cells ( HEK-R5 cells ) in the presence of soluble CD4 ( sCD4 ) , as previously detailed [41] . Most of these gp120s were from biological virus clones isolated from peripheral blood mononuclear cells ( PBMCs ) of four treatment-naïve , HIV-positive individuals longitudinally followed-up . From each individual , PBMCs were collected at two time points: during the chronic stage of infection ( Envs #1 , 25 , 38 , and 50 ) and around the time of AIDS diagnosis ( Envs #10 , 34 , 48 , 58 , and 59 ) . These Envs were confirmed to be R5-tropic by phenotypic tropism assays . The other R5 Envs used here are Bx08 and 1f ( S4 Fig ) [9 , 42] , and the macrophage- ( M- ) and T-cell ( T- ) tropic Envs JR-FL and JR-CSF [43] . HEK-R5 cells express high enough amounts of CCR5 ( 200 , 000 receptors/cell , [9] ) so that binding of HIV-1 gp120s could be determined with a high ratio between specific binding ( i . e . binding to CCR5 ) and non-specific binding ( legend of Fig 1 ) . They also exhibit heterogeneous conformations of CCR5 at their surface [9 , 12] . Saturation binding experiments of 35S-gp120s to HEK-R5 cells revealed that the maximum number of binding sites ( Bmax ) varied from one gp120 to another , indicating that the glycoproteins do not recognize the same amounts of the CCR5 molecules ( Fig 1A , 1B and 1E and S1 Table ) . JR-FL and JR-CSF gp120s , although exhibiting different cellular tropisms , showed similar binding levels ( Fig 1B ) . Scatchard transformation of binding data was linear for most of the gp120s ( Fig 1C ) , suggesting that they bind to a homogeneous population of receptors . Equilibrium dissociation constants KD of most of the gp120/CCR5 interactions are in the range of few to some tenths of nM ( S1 Table ) . These values are similar to those of the lab-adapted gp120s BaL , JR-FL and YU2 [44–46] . Two exceptions are gp120 #58 that displays non-saturable , low-affinity binding to CCR5 and gp120 #10 whose Scatchard plot is non-linear , suggesting that the glycoprotein binds with different affinities to multiple CCR5 populations ( Fig 1D ) . We next investigated whether the gp120s show divergent binding levels to CD4 . Competition binding assays of the anti-CD4 mAb Q4120 to CD4-expressing HEK cells indicated that the gp120s have similar nM affinity constants KI for the receptor ( Fig 1F , inset and S1 Table ) . These affinity constant values are comparable to those of other R5 gp120s ( YU2 , BaL , JR-FL and JR-CSF ) reported by us [41] and others [47–50] . Then , we measured the binding levels of the 35S-gp120s to HEK-CD4 cells ( S1 Fig ) or membranes from these cells ( Fig 1F ) at a concentration of the gp120s equal to their KI , i . e . a concentration at which half of the CD4 molecules is occupied . In contrast to CCR5 , the gp120s had comparable levels of binding to CD4 . Next we investigated if the different capacities of the gp120s to bind to CCR5 could be due to the fact that they are not properly folded when bound to CD4 . By surface plasmon resonance experiments , we measured the binding of the gp120s to the mAb E51 ( Fig 1G ) , which targets regions in gp120 that are induced by CD4 and overlap the CCR5 binding site [42] . The gp120s bound efficiently to the mAb in the presence , but not in the absence , of sCD4 . This indicates that sCD4 has triggered conformational changes in the gp120s exposing the CCR5 binding sites . Overall , these results indicate that the divergent binding levels of gp120s to CCR5 do not result from impaired binding to CD4 or incorrect structural modifications induced upon CD4 binding . We speculated that the different gp120s could bind differentially to distinct CCR5 populations that exist in varying amounts at the cell surface . To address this issue , we first investigated whether the extents to which the different gp120s bind to CCR5 are cell-type dependent . Indeed , the nature and the relative proportions of the CCR5 populations may vary between cell types [11 , 14] . We thus measured the binding levels of 35S-gp120s to CCR5-expressing U87 cells ( S2A Fig ) . Some gp120s labeled equal amounts of the receptors in HEK-R5 and U87-R5 cells ( gp120 #1 , 25 and 38 ) while others showed either increased ( gp120 #34 and 50 ) or decreased ( gp120 #10 , 48 and 58 ) binding to U87-R5 cells , compared to HEK-R5 cells . We next compared the binding of 35S-gp120 #25 and #34 to CCR5 on intact ( S2C Fig ) or membranes from ( S2B Fig ) activated CD4+ T-lymphocytes and monocyte-derived macrophages ( MDMs ) . The differences in the levels of binding between gp120 #25 and 34 were less marked with T-cells than with MDMs . Altogether , these data indicate that the different gp120s differentially target distinct CCR5 populations whose relative proportions vary between cell types . We next studied if the differences in the binding levels between the different gp120s are related to differential recognition of antigenically distinct conformations of CCR5 [11 , 13–15] . We thus performed competition binding assays of the 35S-gp120s to HEK-R5 or U87-R5 cells by the anti-CCR5 mAbs CTC5 , 2D7 or 45531 . These mAbs react with distinct motifs and conformations of CCR5 ( S3A Fig and refs [11 , 14] ) . For the sake of clarity , detailed analysis of the results is presented in the accompanying supplemental S1 Text . Briefly , the main conclusions that have emerged from the experiments in S3B , S3C and S3D Fig are the following . First , we observed that different gp120s may be differentially sensitive to inhibition by some of the mAbs . We interpreted this as strong indication that the gp120s bind differentially to distinct CCR5 conformations , which differ in their ability to be recognized by the mAbs . We also found that some gp120s are only partially inhibited by some mAbs , suggesting that these gp120s recognize at least two distinct CCR5 populations , one interacting with these mAbs , the other that does not . Alternative explanation can however be considered ( S1 Text ) . Finally , sensitivity of the gp120s to the mAbs varies between HEK-R5 ( S3C Fig ) and U87-R5 ( S3D Fig ) cells , suggesting that the gp120s do not bind the same CCR5 conformations in both cell types . Altogether , these results show that the gp120s may target differentially distinct antigenic populations of CCR5 , and this can contribute to their divergent binding levels to CCR5-expressing cells ( Fig 1 ) . In S3 Fig however , it is also apparent ( in particular with U87-R5 cells ) that some gp120s can share similar sensitivities to anti-CCR5 mAbs while showing divergent binding levels to CCR5 ( e . g . compare S2A and S3D Figs and see S1 Text ) . This indicates that distinct CCR5 populations can vary in the nature of the gp120s to which they bind while sharing similar antigenic properties . Next , we investigated whether the distinct CCR5 forms that bind gp120s are also used by viruses for entry into cells . NL4-3-derived virus clones pseudotyped with some of the different full-length ( gp160 ) Envs used above , or gp160 from the JR-FL or JR-CSF strains , were investigated for their sensitivity to inhibition by the mAbs 2D7 , 45531 or CTC5 in single-round infection assays of U87-CD4-CCR5 cells ( S3E Fig ) . MAb 2D7 showed the most effective antiviral activity among the tested mAbs ( ≥ 75% inhibition for all viruses ) . This contrasts with the fact that it inhibits more weakly the binding of gp120s to U87-R5 cells , as compared to 45531 and CTC5 ( S3D Fig ) . In the same line , 45531 and CTC5 efficiently prevented the different Envs from binding to U87-R5 cells ( S3D Fig ) , but they only slightly inhibited infection by some of the pseudotyped viruses ( viruses #1 , #34 , #58 and JR-FL ) . Thus , each of the three mAbs may differ in its ability to inhibit gp120 binding and HIV-1 viral entry . One hypothesis that explains this result is that only a subset of the CCR5 forms to which gp120s bind are used for viral entry . This is in agreement with previous results suggesting that HIV-1 uses only some of the antigenic forms of CCR5 for entry into cells [11] . In addition , we found here that the CCR5 forms that are used by the different viruses are heterogeneous and can be discriminated on the basis of their differential reactivity to mAbs 45531 and CTC5 , but not to 2D7 . The binding experiments with gp120s suggest , however , that differences in sensitivity of viruses to mAbs may be only partly reflective of heterogeneity of the CCR5 forms used for viral entry . To confirm that different gp120s bind to/stabilize distinct CCR5 subsets , we measured the binding of 35S-gp120 #34 to HEK-R5 membranes in the presence of unlabeled gp120 #10 , #25 , #34 , #50 or #58 ( Fig 2A , 2B , 2C and S1 Table ) . As detailed in S2 Text , results indicated that gp120 #10 and gp120 #50 bind with distinct affinities to multiple 35S-gp120 #34-binding receptors . In contrast , gp120 #58 failed to displace the binding of 35S-gp120 #34 , indicating that gp120 #58 and 35S-gp120 #34 do not bind to the same CCR5 populations . We also observed that gp120 #25 and gp120 #34 were equally potent and efficient in displacing the binding of 35S-gp120 #34 ( Fig 2A ) . This result was unanticipated because gp120 #25 binds less receptors than gp120 #34 in the saturation experiments ( Fig 1 ) . Similar results with other gp120s are shown in S4 Fig . As discussed in S2 Text , this suggests that both gp120s bind to the same receptor populations but with divergent stoichiometries . In the context where CCR5 exists as dimers/oligomers [20 , 51] , a model recapitulating the above results considers that HIV-1 gp120 could form both 1:2 and 2:2 stoichiometric complexes with CCR5 dimers , the frequency of each of these two situations depending on the nature of the gp120 . In the first situation ( 1:2 stoichiometry ) , a single gp120 would interact concomitantly with both protomers of the CCR5 dimer , each of them providing different interaction sites for the gp120 ( e . g . the N-tail could originate from protomer 1 and ECL2 from protomer 2 ) , hence forming the whole binding site for the glycoprotein . In the second situation ( 2:2 stoichiometry ) , the two protomers would provide two identical and independent binding sites per dimer for the gp120 . We propose that gp120 #25 favors the 1:2 stoichiometry at the expense of the 2:2 stoichiometry , and conversely for gp120 #34 . This would explain how gp120 #25 could inhibit the binding of gp120 #34 by a competitive mechanism while showing a lower Bmax value in saturation binding assays . This model also considers oligomeric CCR5 as a particular CCR5 population that could play a major role in diversification of the modes of gp120/CCR5 interactions ( NB: For simplicity , oligomeric CCR5 will be referred to as “dimer” or “oligomer” in the rest of the paper ) . To investigate the role of CCR5 di-/oligo-merization in gp120 binding , we compared the ligand binding properties of FLAG/SNAP-tagged wild-type ( WT ) and L196K CCR5 . Indeed , we recently identified that Leu-196 is part of the CCR5 dimerization interface [52] . The L196K mutation results in receptor dimers that are less stable and involve alternative dimerization interfaces , as compared to WT CCR5 ( [52] and Fig 3B ) . Receptors were expressed in HEK 293 cells and membranes containing similar amounts of either WT- or L196K-CCR5 ( Fig 3A ) were prepared . In this context , L196K-CCR5 had a lower capacity to bind the chemokine 125I-CCL3 than WT-CCR5 ( Fig 3C , see the legend for details ) . This suggests that CCR5 dimerization plays a role in high-affinity binding of agonist chemokines . Alternatively , the mutation might alter the equilibrium between high- and low-affinity conformations of the receptor , regardless of its dimerization status . The gp120 binding levels varied differentially between WT- and L196K-CCR5 . In saturation binding assays to WT-CCR5 ( Fig 3D ) , 35S-gp120 #25 labeled fewer receptors than 35S-gp120 #34 . Strikingly , we observed the reverse situation with L196K-CCR5 . As compared to WT-CCR5 , we found a rise in the Bmax value of 35S-gp120 #25 to L196K-CCR5 , while that of 35S-gp120 #34 was dramatically decreased . Other gp120s behaved similarly to gp120 #34 or gp120 #25 , albeit to varying degrees , in that they bind less or more efficiently L196K-CCR5 , respectively , than WT-CCR5 ( Fig 3E ) . The model in Fig 4 recapitulates these results . It posits that CCR5 dimers ( D ) and monomers ( M ) coexist , in agreement with current knowledge on GPCR dimerization [6 , 53] , and that the proportion of dimers is reduced in L196K-CCR5-expressing cells ( in line with Fig 3B and ref . [52] ) . Gp120 binding to WT- and L196K-CCR5 gave similar KD ( S1 Table ) and proceeded according to a one-site binding model ( Fig 3D ) , indicating that the gp120s bind both receptors with similar affinities , regardless of their oligomerization state . In this context , considering that gp120 #25 and #34 target the same CCR5 molecules ( Fig 2A ) , the results in Fig 3D suggest that both gp120s have different interaction stoichiometries with receptor dimers that vary differentially between WT- and L196K-CCR5 . While results are consistent with gp120 #25 favoring the 1:2 at the expense of the 2:2 stoichiometry with WT-CCR5 dimers ( Fig 4A ) , the reverse situation seems to take place with L196K-CCR5 dimers ( Fig 4B ) , and conversely for gp120 #34 ( Fig 4 , see the legend for details ) . The results in Fig 3E suggest that similarly the other gp120s may establish different stoichiometries with WT- and L196K-CCR5 . These results thus suggest that changes in the gp120/dimer interaction stoichiometries contribute to the apparent differences in the binding capacities of R5 gp120s to CCR5 ( Fig 1 ) . Further experiments suggested that WT- and L196K-CCR5 differ in how they bind the gp120s because of different ECL2 conformations . Indeed , both receptors bind differentially the anti-ECL2 mAbs 2D7 and 45531 , in a cell type-dependent manner ( Fig 3F ) . In the same line , the binding levels of gp120s to CCR5 depend on the composition of the tip of the gp120 V3 loop , i . e . the main determinant for interaction with ECL2 ( Fig 3E ) . This suggests that gp120s form different stoichiometric complexes with CCR5 dimers owing to differences in the conformation of their V3 loop . We speculated that favoring CCR5 dimerization might decrease the binding level of gp120s more efficiently when they bind dimers in a 1:2 stoichiometry . We thus performed binding experiments of 35S-gp120s to the L196C-CCR5 mutant , which has an increased propensity to form symmetrical dimers and higher-order oligomers [52] . The extent to which 35S-gp120 #25 binds L196C-CCR5 was dramatically reduced , as compared to WT-CCR5 ( Fig 3E ) . In contrast , the L196C mutation poorly changed the binding of 35S-gp120 #34 , reinforcing the notion that 35S-gp120 #25 and #34 bind CCR5 dimers preferentially in 1:2 and 2:2 stoichiometries , respectively . We next investigated whether the differences in the way dimerization influences gp120 binding to CCR5 translate into differences in efficiency of viral entry . Using the BlaM-vpr/CCF2 virus-cell fusion assay [42] , we measured the ability of NL4-3-derived virus clones pseudotyped with gp160 #25 ( virus #25 ) or #34 ( virus #34 ) to fuse with A3 . 01 T-cells ( CD4+/CCR5- ) expressing comparable levels of WT- or L196K-CCR5 ( Fig 5B ) . Fusion to WT-CCR5-expressing cells occurred at the same rate for both viruses ( S5 Fig ) and reached a plateau value ( Fmax ) that is ≈ 30 percent-fold lower for virus #25 ( grey colored curve ) compared to virus #34 ( green colored curve ) ( Fig 5A ) . We then speculated that increasing CCR5 expression might rescue the fusion capacity of virus #25 . To address this issue , we coupled the fusion assay with immunolabeling of CCR5 and then analyzed fusion to cell populations expressing low , intermediate or high levels of CCR5 ( Fig 5B , 5C , 5D and 5E ) . Fmax for virus #25 was minimal with low-CCR5-expressing cells , representing less than 40% of that of virus #34 ( Fig 5C ) , and then gradually increased with the CCR5 expression level up to values approximating those of virus #34 ( Fig 5D and 5E ) . Fmax for virus #34 also increased with the CCR5 expression level but less deeply , as compared to virus #25 . Overall , these data indicate that fusion of virus #25 is more dependent on the CCR5 expression level than fusion of virus #34 , in agreement with fewer Env #25 molecules binding T-cells as compared to Env #34 ( S2B and S2C Fig ) . Fusion of both viruses to L196K-CCR5-expressing A3 . 01 T-cells proceeded at a similar rate compared to WT-CCR5-expressing cells ( S5 Fig ) , consistent with gp120 #25 and #34 binding WT-CCR5 and L196K-CCR5 with similar affinities ( Fig 3D ) . L196K-CCR5 rendered fusion of viruses #25 and #34 less dependent upon the receptor expression level . Indeed , fusion of virus #34 was maximal regardless of the receptor expression level ( Fig 5C , 5D and 5E ) . Virus #25 behaved similarly as virus #34 , except with low-CCR5-expressing cells where fusion was slightly ( 15%-fold ) decreased . The fact that virus #34 enters more efficiently into L196K-CCR5-expressing cells was however unexpected because gp120 #34 binds less efficiently L196K-CCR5 dimers than WT-CCR5 dimers ( Figs 3 and 4 ) . Other R5 viruses displayed improved fusion to L196K-CCR5-expressing cells regardless of how they interact with the receptor dimers ( Fig 5F ) , suggesting that the receptor dimers do not contribute much to HIV-1 entry into T-cells . A similar trend was observed using HEK-CD4 cells as target cells ( S6 Fig ) . Overall , these data suggest that HIV-1 uses predominantly CCR5 monomers to fuse to T-cells and that fusion to L196K-CCR5-expressing cells is improved due to decrease in the proportion of dimers , as compared to WT-CCR5 expressing cells . Macrophages play a key role in HIV-1 infection pathogenesis [54–57] . Yet , many R5-viruses are inefficient at infecting these cells . Highly macrophage- ( M- ) tropic HIV-1 strains are predominant in brain tissue [58–60] , where they have evolved to use the low CD4 densities that feature the local target cells ( macrophages and microglial cells ) [61] . These viruses exhibit high efficiency of CD4 use , as revealed by increased sensitivity to inhibition by sCD4 [43 , 49 , 59 , 62] and the capacity to infect Affinofile cells expressing minimum density of CD4 ( CD4low Affinofile cells ) [59 , 60 , 63] . Infection of brain long-lived cells in some patients can shelter the virus for a long period of time , which is reflected by slow decay of viral load in the cerebrospinal fluid ( CSF ) under cART [61] . Such highly M-tropic viruses are rather rare in the blood and immune tissues , where R5 isolates are adapted to infect T-cells on which CD4 density is higher [63] . R5 T-tropic isolates showing a certain degree of infection of macrophages have nevertheless been described in certain occasions [58 , 60 , 63 , 64] . As part of the present study , we sought to investigate whether the nature of the CCR5 forms that are present on macrophages , which differ from those on T-cells ( S2 Fig and ref . [14] ) , influences HIV-1 entry into macrophages . We wondered whether altering CCR5 dimerization , similarly to what we have just described in T-cells ( Fig 5 ) , could be likely to modulate efficiency of entry into macrophages . To this end , we first assessed the degree to which the virus clones pseudotyped with our Envs infect MDMs ( Fig 6A ) . Infectivity of the viruses was compared to that of the M-tropic virus JR-FL ( arbitrarily set at 100% ) and the non M-tropic virus JR-CSF . In an effort to give the best picture of M-tropism of our Envs , two additional controls were done . First , as sensitivity of MDMs to HIV-1 infection can vary greatly between different donors [63] , experiments were repeated with MDMs from 6 different donors ( Fig 6A ) . Second , recombinant viral populations from plasma and CSF of two infected patients ( Patients #B and #J ) with HIV-associated encephalitis were also included in the experiments . The virus population in the CSF of P#J ( P#J-CSF ) declined only very slowly under cART ( see “Materials and methods” ) , suggesting that these viruses are derived from long-lived cells . In accordance with this , the viruses consistently infected MDMs in our hands , similarly to JR-FL ( Fig 6A ) . In contrast , none of the virus populations from the other patient’s samples ( P#B’s plasma/CSF and P#J’s plasma ) , which were observed to decay rapidly under cART , could do so ( similarly to JR-CSF ) . Thus , we confirm here the link drawn before between cellular tropism of HIV-1 and response of patients to cART [60 , 65] . Regarding their capacity to infect MDMs , the Env-pseudotyped virus clones fell in two groups . Virus #25 , the only representative from the first group among the viruses we tested , behaved similarly to JR-CSF and consistently failed to infect MDMs , while maintaining robust infection of T-cells ( Fig 6A ) . Most of the other viruses however could infect MDMs , but this greatly varied upon the nature of the donor . This is exemplified for virus #34 that infected MDMs from donors 1 , 3 and 4 as efficiently as JR-FL and P#J-CSF , but did so more weakly with donors 2 and 5 . However , infection of MDMs from donor 6 by virus #34 did not differ from that by virus #25 or JR-CSF . On the whole , infectivity of JR-FL and P#J-CSF was less affected by the nature of the donor , as compared to that of the Env-pseudotyped virus clones . The results in Fig 6A further show that the distinct pseudotyped virus clones were differentially influenced by the nature of the MDMs ( e . g . virus #34 infected better MDMs from donors 3 and 4 than virus #10 , while the reverse was observed with donors 2 and 6 ) . Several lines of evidence demonstrated that although the Env-pseudotyped viruses can infect MDMs in some circumstances , and even very efficiently in some cases , they differ from the prototypical M-tropic viruses JR-FL and P#J-CSF in that they don’t show increased affinity for CD4 . Indeed , while JR-FL and even more P#J-CSF had increased sensitivity to sCD4 , as expected for “true” M-tropic strains [43 , 49 , 59 , 62] , this was not the case for the other viruses . The IC50 values for inhibition of the Env-pseudotyped virus clones by sCD4 did not differ from those of the non M-tropic viruses JR-CSF , P#J-plasma and P#B-CSF/plasma . They all lay in a range of few hundreds of nM , thus about one-log higher than those of the M-tropic viruses ( Fig 6B and 6C ) , as previously reported [43 , 59] . Accordingly , the pseudotyped virus clones and JR-CSF were also more sensitive to inhibition by the anti-CD4 mAb Q4120 , as compared to the M-tropic viruses ( Fig 6D and [43] ) . Last , we compared the virus clones to JR-FL and JR-CSF in infection assays of Affinofile cells expressing high levels of CCR5 and increasing amounts of CD4 , as previously reported [62 , 63] . As shown in Fig 6E and 6F , while JR-FL could infect Affinofile cells at the lowest CD4 expression levels , in agreement with recent data [66] , the other viruses required higher CD4 expression levels . Considered altogether , these results demonstrate that the Envs studied here are not adapted to infection of cells expressing low levels of CD4 . As such , they resemble more the R5 T-tropic strains that predominate in the blood of patients than the “true” R5 M-tropic viruses found in brain tissue . Actually , this is in accordance with the blood origin of the primary viruses from which these Envs were isolated . Thus , even if some of the Env-pseudotyped virus clones can infect MDMs in some cases , this cannot be attributed to increased efficiency of CD4 usage . We next reasoned that differences in the mode of CCR5 usage could distinguish between those pseudotyped viruses that can infect MDMs in some cases ( e . g . virus #34 ) and those that cannot do so ( e . g . virus #25 ) . In particular , in light of the results obtained in T-cells , we asked the question of whether impairing CCR5 dimerization could promote entry into MDMs . To test this , we expressed by lentiviral transduction WT-CCR5 or L196K-CCR5 in freshly prepared monocytes , and then differentiated these cells into MDMs for 7 days . The mean receptor expression levels ( detected by the anti-CCR5 mAb 2D7 ) increased by up to 10- and 3-fold in WT-CCR5- and L196K-CCR5-expressing MDMs , respectively , as compared to untransduced MDMs ( Fig 6G ) , and were in the same range as those measured in transduced A3 . 01 T cells ( see the legends of Figs 5B and 6G ) . We then measured fusion of virus #25 , #34 , JR-CSF or JR-FL with untransduced or transduced MDMs . JR-FL and to a reduced extent virus #34 , but only marginally virus #25 and JR-CSF , could fuse with MDMs ( Fig 6H ) . In these cells , however , the impact of the receptors on viral entry differed markedly to that in T-cells . Notably , the extent of fusion of the viruses with WT-CCR5-overexpressing cells was similar to that with untransduced ( NT ) MDMs . This result indicates that HIV-1 entry into MDMs , in contrast to T-cells ( Fig 5C , 5D and 5E ) , hardly depends on the overall CCR5 expression level , regardless of whether or not the viral isolate uses CD4 efficiently . We also observed that L196K-CCR5 substantially diminishes the ability of virus #34 to fuse with MDMs , in contrast to the effect it has in T-cells , indicating that this receptor has a reduced capacity to support HIV entry into MDMs , as compared to T-cells . A similar trend was observed for JR-FL , albeit not significant . This result , together with our data in S2B and S2C Fig showing that CCR5 has gp120 binding capacities that differ between T-cells and MDMs , indicate that the capacity of CCR5 to function as a HIV coreceptor may differ between different cell types and this is likely to contribute to modulate sensitivity of target cells to HIV infection . However , our results suggest that this effect of CCR5 is less marked in the case of viruses that use CD4 efficiently ( JR-FL ) .
Different aspects of the pathogenesis of HIV infection have been interpreted in light of the properties of HIV/receptor interactions . Alteration in the affinity of R5 Envs for CD4 and/or CCR5 has been proposed to influence the virulence of R5 viruses , HIV resistance to entry inhibitors and cell tropism [40 , 43 , 48 , 64 , 67–69] . However , none of these studies has considered that CCR5 exists in structurally different forms , the multiplicity of the modes of gp120/CCR5 interactions and the possibility that distinct HIV Envs differ in the nature of the CCR5 populations to which they bind . We show here that all of these factors are likely to shape phenotypic properties of viruses and their role in infection . Here , distinct gp120s display divergent binding levels to CCR5-expressing cell lines and primary cells ( Fig 1 and S2 Fig ) . This is partly explained by the fact that different gp120s can bind differentially antigenically distinct populations of CCR5 ( S3 Fig ) , which exist in different quantities in cells [11 , 14] . This also owes to distinct capacities of gp120s to bind to CCR5 dimers ( or higher order oligomers ) ( Fig 3 ) , which can themselves also exist in different conformations [52] . Some gp120s ( e . g . gp120 #25 and #34 ) also compete each other for binding to the same receptors ( Fig 2 ) , while showing great differences in their binding levels to CCR5 . This apparent paradox could be explained by a model whereby the gp120s engage the CCR5 dimers with distinct stoichiometries ( Fig 4 ) . Our data indicate that the different CCR5 populations and how they bind the gp120s vary according to the conformation of the receptor ECL2 . The L196K mutation in TM5 to which ECL2 is connected alters accessibility of the loop to mAbs and differentially changes the binding levels of the different gp120s ( Fig 3E and 3F ) . Moreover , minimal change in the composition of the tip of the gp120 V3 loop , which interacts with ECL2 , modifies the binding capacities of gp120s ( Fig 3E ) [32 , 42] . The gp120s used here contain sequence changes in V3 and other CCR5 binding regions ( S7 Fig ) , which similarly could determine which population ( s ) of CCR5 they recognize . Here , we show that HIV-1 gp120s/isolates and anti-CCR5 mAbs do not necessarily recognize the same CCR5 conformations , explaining why anti-CCR5 mAbs can be inefficient as HIV-1 entry inhibitor ( S3 Fig ) . Other CCR5 ligands target particular CCR5 conformations , which in some cases may differ from those used by viruses [27 , 70 , 71] . We recently identified that R5 HIV-1 strains escape inhibition by CCR5 chemokines by exploiting low-chemokine affinity conformations of CCR5 , which are uncoupled from nucleotide-free G proteins [12 , 16] . This is due to the fact that HIV-1 gp120s , in contrast to chemokines , bind CCR5 independently of its coupling to G proteins . Here , we provide further evidence that CCR5 exhibits differential conformational requirements for binding chemokines and gp120 . This is illustrated by the differential effects of the L196K mutation on CCL3 and gp120 binding ( Fig 3 ) . The decrease of CCL3 binding to L196K-CCR5 , which forms less dimers than the wild-type receptor ( Fig 3B and [52] ) , suggests that CCR5 dimerization could be required for high affinity binding of agonist chemokines to the receptor . This suggests that a link could exist between CCR5 dimerization and its coupling to G proteins , although data on other GPCRs favor the idea of a 1:1 interaction stoichiometry between activated receptors and G proteins [72–75] . Previous work already suggested that CCR5 homodimers are involved in high affinity binding of chemokines in a G-protein dependent manner [20] . Alternatively , the proportion of receptors that exist in a high-affinity conformation for CCL3 could be decreased for L196K-CCR5 , regardless of its dimerization state . Whichever the mechanism that is involved in the decrease of CCL3 binding to L196K-CCR5 , we show here that expression of this mutant results in increase of HIV-1 entry into T-cells ( Fig 5 ) . This again evidences that HIV-1 and CCR5 chemokines recognize distinct conformations of the coreceptor . Here , we illustrate that the number of CCR5 molecules to which gp120s is likely to bind may influence viral infectivity ( Fig 5 ) . This parameter could therefore act in concert with HIV-1 entry stoichiometry T , i . e . the number of Envs required for entry , which varies between different HIV-1 strains and also regulates efficiency of viral entry [31 , 76] . As the relative proportions and the nature of the CCR5 forms vary from one cell type to another ( S2 Fig ) [11 , 14] , recognition of a wide variety of the CCR5 forms could also allow a virus to have an extended cell tropism . This could increase its chances of infecting cells that express limiting amounts of the receptor and where , for this reason , CCR5 diversity could be narrower , such as in naïve and central memory T cells ( Tcm ) whose loss in the late stages of infection is thought to contribute to development of AIDS [77] . Two observations suggest that monomeric gp120s and complete viruses can show differences in the nature of the CCR5 forms they target . First , some anti-CCR5 mAbs that weakly displace gp120 binding ( e . g . 2D7 ) are efficient entry inhibitors , and conversely for other mAbs ( S3 Fig ) . Second , the level of gp120 binding measured by means of binding assays is not necessarily correlated with viral entry capacity ( our results related to gp120 #10 and 58 in Fig 1A and ref . [42 , 78] ) . We do not believe that these data could owe to the fact that virus-associated gp120s and monomeric gp120s stabilize distinct conformations of the coreceptor . This would imply that these two forms of the gp120 differ in the conformation of their coreceptor-binding regions , which is not supported by the available structural and functional data . Indeed , in the CD4-bound , open form of the Env trimer [79] , the three gp120 protomers are held separate , and adopt a conformation of their core and coreceptor-binding regions that is very close to those in soluble gp120 monomers [34 , 35 , 80] . In line with this , comparison studies have shown that virus- ( or membrane ) -associated gp120 and soluble , monomeric gp120 induce CCR5 signaling effects that are largely comparable [81–84] . This strongly suggests that complete viruses and monomeric gp120 stabilize the same active conformations of the coreceptor . We believe it more likely that viruses and gp120s bind similar CCR5 forms , but only a subset of them is used for viral entry . This is consistent with the view that CCR5 requires distinct molecular determinants for binding gp120 and mediating fusion [85 , 86] , and with our recent observation that viruses can bind to CCR5 with high affinity without necessarily entering into cells [87] . Our results here suggest that CCR5 homodimers could represent a form of the coreceptor that allows gp120 binding but not entry . Indeed , while results in Fig 3 suggest that gp120s bind CCR5 monomers and oligomers with similar affinity , the infection assays in Fig 5 are consistent with viruses using predominantly CCR5 monomers for entry , in agreement with previous works [88 , 89] . Besides that , dichotomies between the gp120 binding and infection assays could simply relate to the fact that some CCR5 receptors to which sCD4-gp120 can bind do not satisfy critical conditions for viral entry , such as co-localization with CD4 and/or be surrounded by membrane lipids that are appropriate for fusion [90 , 91] . CCR5 differs in its capacity to bind gp120 ( S2B and S2C Fig ) and to mediate viral entry ( Figs 5 and 6 ) between T-cells and MDMs . This could owe to the fact that CCR5 does not adopt the same conformations in T-cells and MDMs [14] . Our results suggest that CCR5 also exhibits different organization at the surface of both cell types . Indeed , MDMs and T-cells express similar quantities of CCR5 ( legend of S2 Fig ) , but MDMs have a lower CD4 density [63] . In this situation , it seems paradoxical that viral entry depends on the CCR5 expression level in T-cells ( Fig 5 ) , but not in MDMs ( Fig 6G and 6H and [92] ) . This could indicate low efficiency of CCR5 usage in MDMs . Indeed , the HIV-1 strain 89 . 6 binds CCR5 very weakly [85] and is similarly not influenced by the CCR5 expression level in Affinofile cells , regardless of the CD4 expression level [93] . This could also reflect that CCR5 in MDMs is clustered in membrane subdomains where its density is already at a saturating level . Actually , HIV-1 entry and replication increase with CCR5 density [94–96] , and this is what we observed in T-cells ( Fig 5 ) . But increase in density of GPCRs in a certain range also increases the proportion of these receptors that exist in dimers/oligomers [53] . Although hypothetical at this time , a higher proportion of CCR5 dimers in MDMs would explain why gp120 # 25 binds less efficiently those cells than T-cells ( S2 Fig ) . This would also explain why L196K-CCR5 decreased HIV-1 entry into MDMs , ( Fig 6H ) , while it increased it into T-cells ( Fig 5 ) . In T-cells , results are compatible with the view that the proportion of L196K-CCR5 that exists as monomers is higher compared to WT-CCR5 . This however might not be the case in MDMs in the context of high receptor density . Under conditions where the receptor density is high , heterodimers between L196K-CCR5 and endogenous CCR5 could also form in MDMs in addition to receptor monomers . These heterodimers could adopt a conformation that is not competent for viral entry . This hypothesis is actually consistent with the prevalent view that GPCRs have distinct structural and functional properties depending on whether they are engaged in homo- or hetero-dimers ( [97 , 98] and for review [5] ) . Most of R5 HIV-1 strains are less efficient at entering MDMs than T-cells [56] . This is argued to result from low CD4 density in MDMs [63] , but low efficiency of CCR5 usage could also contribute to this phenotype . Viruses adapted to infect macrophages are frequent in brain tissue [61] . These viruses were shown to use CD4 very efficiently [43 , 48 , 49 , 60 , 62 , 63] . In some studies , however , M-tropism has been attributed to changes in the binding characteristics to CCR5 [64 , 67] . Viruses with phenotypic properties resembling those of M-tropic viruses from brain are rare in blood and immune tissues [56] . However , we identified here that Envs from biological virus clones of patient’s PBMCs could infect MDMs , although this hugely depended upon the nature of the donor ( Fig 6 ) . Previous works have emphasized that long-term culturing of HIV-1 isolates on PBMCs or T-cells favors acquisition of phenotypic traits of M-tropic viruses ( e . g . increased sensitivity to sCD4 , resistance to anti-CD4 mAbs , and ability to infect cells expressing low levels of CD4 [66 , 99 , 100] ) , presumably due to stabilization of a partially opened conformation of Env [66 , 101] . The Envs studied here , however , do not show such functional characteristics ( Fig 6 ) . They also lack genetic traits of virus adaptation to long-term culture on PBMCs [66] , such as mutation of Leu193 in the V2 loop , a residue that maintains the Env trimer in a closed conformation . Other residue changes that feature culture-adapted viruses [66 , 99 , 100] are absent or present only sporadically in the sequences of our Envs ( S7 Fig ) . Most of the Envs also lack the genetic signatures that have been associated to M-tropism , such as T283N [48] , E153G [102] and N386D [103] . There are some exceptions , such as in the sequences of gp120 #34 and #58 , where the T283N is present . An Asn residue at position 283 has been observed to confer increased affinity for CD4 and improve macrophage tropism [48] . Substitution of Asn by a Thr residue in the sequences of gp120 #34 and #58 however did not change affinity for CD4 [41] , suggesting that the effect of the T283N mutation is context-dependent . The Env-pseudotyped viruses we used here thus do not show increased efficiency of CD4 usage ( Fig 6 ) and as such , share phenotypic characteristics of R5 T-tropic viruses isolated from blood [49 , 59 , 62] . Similarly to our viruses , other T-tropic viruses have been reported to infect macrophages , albeit in most of the cases to a lower level as compared to “true” M-tropic viruses [58 , 60 , 63 , 64] . However , in agreement with previous data [63] , we show here that the degree to which T-tropic viruses support infection of MDMs is a relative concept that actually greatly depends upon the nature of the donor . This suggests that key parameters that control infection of MDMs by R5 T-tropic viruses are differentially expressed from one type of MDMs to another . Our results in Fig 6 show that CCR5 structural plasticity dramatically regulates entry into MDMs of a R5 T-tropic virus ( virus #34 ) , while it has a weaker effect on entry of the M-tropic strain JRFL that uses CD4 efficiently . It has been reported that the CCR5 populations on MDMs differ between different donors [14] , and in light of the results shown here , it is tempting to speculate that this could contribute to the fact that MDMs from different donors are differentially sensitive to infection by R5 T-tropic viruses ( although we cannot exclude that post-entry steps could also be involved [54] ) . Our study also raises the possibility that some R5 T-tropic viruses could contribute to the first steps of the colonization of the central nervous system ( CNS ) in HIV+ patients . Indeed , it has been shown that this process occurs early in infection and is mediated by infected CD4+ T-cells [104] . Baseline replication in macrophages/microglia of R5 T-tropic viruses thanks to their use of particular CCR5 conformations could precede adaptation to usage of low CD4 levels . In this context , the nature of the CCR5 population expressed on macrophages/microglia , if it turned out that it actually regulates the differences in their sensitivity to infection by R5 T-tropic viruses between different individuals , would represent a risk factor to infection of the CNS .
The A3 . 01 human T-cell line ( obtained from Dr H-T He , Centre d’Immunologie INSERM/CNRS de Marseille-Luminy , France ) , HEK 293T cells ( obtained from American Type Culture Collection ( ATCC ) ) stably expressing or not ( parental cells ) CCR5 ( HEK-R5 ) or CD4 ( HEK-CD4 ) , and the HEK 293 cells ( obtained from ATCC ) expressing FLAG/SNAP-tagged WT- or L196K-CCR5 , were described previously [12 , 16 , 42] . U87-R5 cells were generated by transducing the human glioblastoma cell line U87 ( obtained from ATCC ) with the pTRIP ΔU3 lentiviral vector ( a gift from Dr P . Charneau , Institut Pasteur , Paris ) encoding the CCR5 sequence . These cells were cultured in Dulbecco’s Modified Eagle Medium ( DMEM ) supplemented with 10% ( v/v ) foetal bovine serum ( FBS ) , 100 μg/ml streptomycin and 100 units/ml penicillin . The 293-Affinofile cell line ( a gift from Dr B . Lee , Mont Sinai Hospital , New York , NY , USA ) was maintained in DMEM supplemented with 10% ( v/v ) foetal bovine serum , 100 μg/ml streptomycin , 100 units/ml penicillin , and 50 μg/ml blasticidin ( Invitrogen ) . Human CD4+ T-lymphocytes and CD14+ monocytes were purified from Peripheral Blood Mononuclear Cells ( PBMC ) of healthy blood donors from Etablissement Français du Sang ( EFS , The French Official Blood Bank ) in accordance with the EFS ethical guidelines by Ficoll centrifugation ( PAA laboratories ) and subsequent immunomagnetic positive selection using CD4 or CD14 MicroBeads ( Miltenyi Biotec ) . CD4+ T-cells were maintained for 2 days in RPMI 1640 medium containing recombinant interleukin-2 ( IL-2 ) ( 300 IU/ml ) and phytohemagglutinin ( 1 μg/ml , Thermo Fisher Scientific ) , and then for additional 4–6 days in the presence of IL-2 alone . CD14+ monocytes were differentiated into macrophages in M-SFM medium ( Thermo Fisher Scientific ) containing 50 ng/ml M-CSF ( Miltenyi Biotec ) , penicillin ( 100 U/ml ) and streptomycin ( 100 μg/ml ) for 7 days . Recombinant soluble human CD4 ( sCD4 ) was produced and purified as previously described [42] . The anti-CCR5 mAbs CTC5 , 2D7 and 45531 were obtained from BD Biosciences and R&D Systems . The unconjugated and AlexaFluor 647-conjugated forms of the anti-Flag mAb M2 were obtained from Sigma-Aldrich and Cell Signaling Technologies , respectively . The anti-CD4 mAb Q4120 was described in our previous work [42] . The goat anti-mouse AlexaFluor 647-conjugated secondary antibody was from Invitrogen . MAb E51 and Maraviroc were obtained from the AIDS Research and Reference Reagent Program . Blood samples from anonymous healthy donors were obtained from Etablissement Français du Sang ( EFS , the French National Blood Agency ) . Sample use for scientific purpose was carried out in accordance with convention between EFS and Institut Pasteur . All donors have provided written informed consent at the time of blood collection . Some of the envelope glycoproteins studied here ( v . i . ) were isolated from virus clones previously described in references [105 , 106] . These viruses were kindly provided to us by Pr . Dr . H . Schuitemaker ( University of Amsterdam , The Netherlands ) , who isolated them from PBMCs of anonymized participants of the Amsterdam Cohort Studies ( ACS ) on HIV-1 and AIDS . The ACS is a well-reported cohort study ( https://www . amsterdamcohortstudies . org ) that started in 1984 and enrolled HIV-1 infected and HIV-1-uninfected individuals at high risk for HIV infection . The ACS obeys the principles expressed in the Declaration of Helsinki and was approved by the Medical Ethics Committee of the Academic Medical Center in Amsterdam . Participants were volonteers who provided written informed consents for blood usage in research investigations . The patients #B ( P#B ) and #J ( P#J ) , to whom reference is made here , attend the Infectious Diseases Department of Toulouse University-Hospital , France , for care and have provided informed written consent for research investigations on their clinical samples . P#B was diagnosed with HIV-1 in 1993 and further developed clinical , CSF , and brain MRI patterns supporting the diagnosis of HIV encephalitis in 2017 . At that time , HIV-1 viral loads ( VLs ) in the plasma and CSF of P#B attained 2 . 74 and 4 . 31 log10 copies/ml , respectively . R5 tropism was confirmed genotypically and phenotypically for plasma and CSF viruses . The modification of cART regimen with Tenofovir , Emtricitabine , Darunavir/Ritonavir , and Maraviroc led to robust decrease of VLs in both plasma and CSF within the following seven months up to values below the detection threshold . P#J was diagnosed with HIV-1 infection in 2017 . At this time , P#J exhibited a marked depletion in blood CD4 T-lymphocytes ( < 100 cell/mm3 ) and severe clinical manifestations of HIV-associated dementia with brain MRI abnormalities . Measurement of HIV-1 RNA in the plasma and CSF of P#J revealed VLs of 6 . 26 and 4 . 44 log10 copies/ml , respectively . R5 tropism was confirmed genotypically and phenotypically for plasma and CSF viruses . P#J first received cART with Abacavir , Lamivudine , and Dolutegravir . Genotypic drug-resistance profiles in plasma and CSF confirmed full sensitivity to these drugs . Over the first three months of treatment , the VL in the plasma considerably felt down , up to a value of 1 . 85 log10 copies/ml , in contrast to the VL in the CSF that did not change ( 4 . 35 log10 copies/ml ) . cART treatment was optimized for CNS penetration with Abacavir , Lamivudine , Nevirapine , and Maraviroc . After 5 months of cART , the VL in the CSF still remained high ( 4 . 05 log10 copies/ml ) , while that in the plasma continued decreasing ( 1 . 54 log10 copies/ml ) . At that time , HIV drug-resistance testing was repeated and confirmed that the viruses in the CSF are genotypically drug-sensitive . The sequences coding for envelope glycoproteins #1 , 10 , 25 , 34 , 38 , 48 , 50 , 58 and 59 ( displayed in S7 Fig ) were isolated from R5-tropic biological virus clones #15 . 3A7 , 46 . 5B1 , 14 . 5C6 , 75 . 6C4 , 12 . C12 , 43 . 5D3 , 20 . 8C1 , 65 . 9D8 , and 65 . 9E7 , respectively . These clones were isolated from anonymized individuals of the ACS who were previously identified as H1 ( Env #1 , 10 ) , H2 ( Env #38 , 48 ) , H3 ( Env #50 , 58 , 59 ) and H5 ( Env #25 , 34 ) in references [105 , 106] and were recoded here as patients #1 , 458 , 1031 and 341 , respectively . PBMCs were collected early after seroconversion ( SC ) ( Env #1 , 25 , 38 , and 50 were collected 26 , 30 , 20 and 22 months after SC , respectively ) or in the AIDS stage of infection ( Env #10 , 34 , 48 , 58 , and 59 were collected 107 , 128 , 86 , 91 and 91 months after SC , respectively ) . The mean numbers of CD4+ T-cells in the blood of patients were 640 and 90 per μl at the early and late stages of infection , respectively . The envelope glycoprotein from the HIV-1 primary strain Bx08 was previously described [9] . Envs # 1f and 1f ( V3 ) correspond to envelope glycoproteins MVC-Sens and MVC-Sens ( P/S ) described in our previous work [42] . Sequences for the laboratory-adapted HIV-1 strains JR-CSF and JR-FL can be found in the Los Alamos HIV sequence Database ( https://www . hiv . lanl . gov/ ) . Extraction of HIV-1 viral RNA from PBMCs , amplification of env gene sequences , cloning of gp120 sequences in the Semliki Forest Virus-derived expression vector pSFV2 , and production , 35S labelling and purification of soluble , monomeric gp120 fused to the One STrEP-tag ( IBA GmbH ) at their C-tail have recently been described in detail [41] . The sequences encoding the different full-length envelope glycoproteins ( gp160 ) were synthesized and cloned in the pNL-SacII-lacZ/env-Ren proviral vector in collaboration with the Genscript Company . This vector derives from the HIV-1 proviral clone pNL4-3Ren [107] , in which the nef gene in the genome of the HIV-1 strain NL4-3 is replaced by the Renilla luciferase gene at the NotI site in position 8797 , and was constructed similarly as to the pNL-lacz/env-Ren proviral vector previously described [108] . Briefly , pNL-SacII-lacZ/env-Ren was generated by introducing a SacII site at position 6113 in pNL4-3Ren and replacing the env coding sequence between SacII ( 6113 ) and NotI ( 8797 ) by the amino-terminal fragment of the lacZ gene . Then , the fragment in pNL-SacII-lacZ/env-Ren between EcoRI ( 5743 ) and NotI was deleted and replaced by the env genes flanked at their 5’ and 3’ ends by the sequences of NL4-3 between EcoRI/SacII and upstream the NotI site , respectively . The Env-pseudotyped virus clones described here were produced by transfecting HEK 293T cells with the env-containing pNL-SacII-lacZ/env-Ren proviral vectors , as previously described [87] . For preparation of the recombinant virus populations from the plasma and CSF of patients P#J and P#B , the env sequence region encompassing gp120 and ectodomain of gp41 was amplified and then cotransfected with NheI-linearized pNL43-Δenv-Luc2 vector DNA in HEK 293T cells , as described [109] . Forty eight hours post-transfection , cell culture supernatants were harvested , clarified by centrifugation and frozen at—80 °C . The amount of p24 antigen in the supernatants was quantified using a commercially available ELISA kit ( Innotest HIV antigen mAb; Innogenetics , Gent , Belgium ) . All of the viruses used here ( including the virus populations from P#J and P#B ) were confirmed to be R5-tropic in phenotypic assays using CCR5- or CXCR4-expressing U87-CD4 cells as indicator cells . Lentiviral particles expressing SNAP/FLAG tagged WT-CCR5 or L196K-CCR5 were produced in HEK 293TN cells . Cells were cotransfected using lipofectamine 3000 ( Invitrogen ) with the plasmid pTRIP ΔU3-CMV containing either of the two receptor sequences , the encapsidation plasmid p8 . 74 , the vesicular stomatitis virus G protein-expressing plasmid pVSV-G and the HIV-1 Rev protein-expressing plasmid pRev at a 3:2:1:1 ratio ( The plasmids were kindly provided by Dr P . Charneau , Institut Pasteur , Paris ) . Forty-eight hours post-transfection , culture supernatants were collected , cleared by centrifugation at low speed and filtration ( 0 . 45 μm ) and ultracentrifuged at 4°C for 120 min at 83000 x g . The amount of VSV-G-pseudotyped lentiviral particles was determined by measuring the Gag p24 concentration with the Alliance HIV-1 p24 Antigen ELISA kit ( Perkin Elmer ) . For transduction of A3 . 01 T cells , 0 . 5 x 106 cells were incubated for 72 h with 25 or 400 ng Gag p24 of WT-CCR5- or L196K-CCR5-expressing lentiviral particles , respectively . Receptor-expressing cells were then washed , kept in culture for several days and then frozen in liquid nitrogen before use . For expression of receptors in MDMs , lentiviral transductions were carried out as follows . Sorted CD14+ monocytes were plated on 6-well plates ( 2 x 106 / well ) and then transduced with 150 or 450 ng Gag p24 of SNAP-FLAG tagged WT-CCR5- or L196K-CCR5-expressing lentiviral particles in the presence of Vpx-expressing SIVmac 251-derived particles produced using the pSIV3+ plasmid ( a kind gift from Dr O . Schwartz , Institut Pasteur , Paris ) . Transduced monocytes were then allowed to differentiate into macrophages as described above for 7 days before being used in the fusion assays . Most of the protocols for the binding assays used herein have already been detailed in a step-by-step manner in our previous articles to which readers may refer , including a recently published methodological article [41] . Equilibrium binding of 35S-gp120 or 125I-CCL3 to intact cell lines or membrane preparations from these cells was measured and analyzed as described in references [9 , 41 , 42] . Scatchard transformation of data from equilibrium saturation binding experiments of 35S-gp120 to HEK-R5 membranes was performed with GraphPad Prism 6 . Binding of 10 nM 35S-gp120 to 1 . 5 x 106 activated CD4+ T-cells or 1 x 106 MDMs was carried out similarly in the presence of 300 nM sCD4 . Crude membrane preparations from these cells were prepared as described previously [10] , and 20 μg ( T-cells ) or 40 μg ( MDMs ) of membrane proteins were used in the binding assays . The protocol for the displacement experiments of 35S-gp120 #34 binding to HEK-R5 membranes by increasing concentrations of unlabeled gp120 is the same as that described in reference [42] . The displacement experiments of Q4120 binding to HEK-CD4 cells by gp120s have recently been detailed and commented extensively [41] . Finally , surface plasmon resonance analysis of the interaction between gp120 and mAb E51 , in the presence or absence of 200 nM sCD4 , was performed similarly as in our previous work [42] . For assessment of receptor expression levels by flow cytometry , cells ( 2 × 105 ) in conical-bottom 96-well plates were incubated for 1 h at 4°C in 0 . 1 ml final volume of FACS buffer ( phosphate-buffered saline ( PBS ) , 1% BSA , 0 . 1% NaN3 ) containing unconjugated anti-Flag mAb M2 ( usually 2 μg/ml ) or anti-CCR5 mAb 2D7 ( 2 . 5 μg/ml ) . In Fig 3F , the concentrations of unconjugated mAbs ( M2 , 2D7 and 45531 ) were saturating ( 40 μg/ml ) . After two washing steps in ice-cold FACS buffer , cells were incubated at 4°C for 30 min in 0 . 1 ml FACS buffer containing AlexaFluor 647-conjugated goat anti-mouse IgG ( Life Technologies ) at a 1:500 dilution . Cells were then washed twice in the FACS buffer and fixed with 2% paraformaldehyde-containing PBS . Data were acquired out on a FACSCanto flow cytometer ( BD Biosciences ) and analyzed using FlowJo software . In the particular case of immunostaining of MDMs , 1 . 5 × 105 cells in 50 μl of FACS buffer were first incubated for 1 h at 4°C in the presence of 10% human serum AB ( hSAB ) . Cells were then further incubated at 4°C for 20 min in 30 μl of FACS buffer in the presence of FcR blocker ( 2 μl / 106 cells , Myltenyi Biotec . ) . Then , labeling of receptors was carried out and analyzed as described above . Receptor homodimerization in HEK 293 cells was measured after cotransfection of cells with the FRET donor plasmid encoding FLAG/SNAP-tagged WT-CCR5 ( 20 ng ) , L208K-CCR5 ( 20 ng ) or L196K-CCR5 ( 30 ng ) and the FRET acceptor plasmid encoding FLAG/CLIP-tagged WT-CCR5 ( 10–20 ng ) , L208K-CCR5 ( 10–40 ng ) or L196K-CCR5 ( 10–40 ng ) , respectively . These plasmid quantities were such that receptors showed similar expression levels at the cell surface and that the FRET acceptor ( CLIP ) / donor ( SNAP ) concentration ratio is non-saturating . Twenty-four hours post-transfection , the cells were detached , plated on black poly-D-lysine treated 96-well plates ( Greiner ) , and incubated for a further 24 h . Cell surface expression of FLAG-SNAP- or FLAG-CLIP-tagged receptors was then determined by labeling cells ( 2 h , 37°C ) either with 100 nM Snap-Lumi4Tb or 400 nM Clip-Lumi4Tb ( Cisbio Bioassays , Codolet , France ) , exciting them at 320 nm and measuring fluorescence intensities at 620 nm . In separate wells , TR-FRET signals were obtained after colabeling cells with 100 nM Snap-Lumi4Tb and 400 nM Clip-d2 ( Cisbio Bioassays , Codolet , France ) ( 2 h , 37°C ) . After excitation at 320 nm , fluorescence emission intensities were measured at 665 nm for 400 μs after a 50 μs delay on a Mithras LB 940 ( Berthold technologies ) . We calculated the net mTR-FRET ratio ( i . e . FRET efficacy in Fig 3B ) as ( signal at 665 nm/signal at 620 nm ) x 1 , 000 –background with donor plasmid alone . For infection of U87-CD4-R5 cells , cells ( 2 x 105 cells per well ) in 96-well plates were inoculated with 100 ng p24 of the pNL4-3-derived viral clones expressing Renilla luciferase and gp160 from the JR-CSF or JR-FL strains , or the biological virus clones from patients of the ACS . Infected cells were further incubated at 37 °C for 48 h in complete culture medium ( DMEM ) in the presence or in the absence of CCR5 ligands ( mAbs or MVC ) . Viral entry was then determined by measuring luciferase activity ( Renilla Luciferase Assay , Promega , Madison , WI , USA ) in the cell lysates using a Glomax luminometer ( Promega ) . For infection of CD4 T-cells and MDMs , cells ( 2 x 105 cells per well ) in conical- ( T-cells ) or flat-bottom ( MDMs ) 96-well plates were incubated in complete RPMI medium supplemented with 20 ng/ml IL-2 ( CD4 T-cells ) or complete M-SFM medium containing M-CSF ( 10 ng/ml ) ( MDMs ) in the presence or in the absence of entry inhibitors ( Q4120 or MVC ) . For the inhibition assays by sCD4 , viruses were first preincubated with the inhibitor for 45 min at 37 °C , as previously described [59] . Then , the virus-sCD4 mixtures were added to 2 x 105 CD4 T-cells for additional 48 h at 37 °C . Infection experiments of Affinofile cells , as well as induction of CCR5 and CD4 expression by ponasterone A and minocycline , respectively , and determination of receptor expression levels ( expressed as receptor number per cell ) , were carried out as described in our previous work [42] . The amounts of viruses used in the infection experiments of T-cells , MDMs and Affinofile cells were selected as described in the legends of Fig 6 ( panels A and E ) . Infectivity was then determined by measuring Renilla ( pNL4-3-derived viral clones ) or Firefly ( viral populations from P#B and P#J ) luciferase activity ( Promega , Madison , WI , USA ) . BlaM-vpr-containing viral clones were prepared in HEK 293T cells as described [110] . Briefly , 1 . 5 x 107 cells in 162 cm2 culture flasks were cotransfected using the calcium phosphate-DNA coprecipitation method with 60 μg proviral DNA , 20 μg pCMV-BlaM-vpr plasmid and 10 μg pAdvantage vector ( Promega ) . Forty-eight hours post-transfection , culture supernatants containing the viral particles were clarified at low speed and then ultracentrifuged at 72 , 000 g for 90 min at 4°C . The pelleted viruses were then resuspended in DMEM , measured for their content in HIV-1 Gag p24 antigen ( Alliance HIV P24 antigen ELISA Kit from PerkinElmer ) and stored at -80 °C before use . Fifty ng Gag p24 of BlaM-vpr-containing viruses were exposed to 2 x 105 T-cells or 1 . 5 x 105 MDMs , which were detached from culture flasks with Cellstripper ( Corning ) , as previously described [42] . Then , cells were incubated for 2 h with the CCF2/AM dye ( using the CCF2-AM loading kit from Invitrogen ) . Loading of the A3 . 01 T cells with CCF2 was performed in the presence of the AlexaFluor 647-conjugated anti-Flag mAb M2 at a 1:100 final dilution . Enzymatic cleavage of CCF2 by β-lactamase in the target cells was analyzed by flow cytometry ( FACSCanto , BD Biosciences ) . The t- and F-tests presented in the study were realized using the GraphPad Prism software .
|
CCR5 regulates host immune responses against pathogens . It also serves as an anchor for R5-tropic strains of HIV-1 to infect immune cells , hence contributing to development of AIDS . CCR5 exists in different forms ( e . g . conformations , oligomerization states ) , but the mechanisms that govern this diversity and its consequences on the physio-/pathophysio-logical functions of the receptor remain unclear . Because genetically diverse viral isolates populate HIV-1 infected individuals , we asked whether divergent viruses differ in the nature of the CCR5 molecules they use , and if so , whether this accounts for differences in their biological properties . Here we answered in the positive to both questions . We also identified CCR5 oligomerization as a key process regulating the receptor conformational diversity , the extent to which HIV-1 envelope glycoproteins bind to target cells and viral entry efficacy . From a functional standpoint , the nature/quantity of the receptor populations that are used by HIV-1 isolates regulates the type of cells they can infect and their ability to escape inhibition by CCR5 ligands . This study thus represents a step forward toward understanding of the mechanisms that regulate CCR5 diversity and its implications on the virus biological properties while opening new avenues for the development of drugs targeting CCR5 .
|
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"Abstract",
"Introduction",
"Results",
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"Materials",
"and",
"methods"
] |
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] |
2018
|
CCR5 structural plasticity shapes HIV-1 phenotypic properties
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Dravet syndrome ( DS ) is a genetically determined epileptic encephalopathy mainly caused by de novo mutations in the SCN1A gene . Since 2003 , we have performed molecular analyses in a large series of patients with DS , 27% of whom were negative for mutations or rearrangements in SCN1A . In order to identify new genes responsible for the disorder in the SCN1A-negative patients , 41 probands were screened for micro-rearrangements with Illumina high-density SNP microarrays . A hemizygous deletion on chromosome Xq22 . 1 , encompassing the PCDH19 gene , was found in one male patient . To confirm that PCDH19 is responsible for a Dravet-like syndrome , we sequenced its coding region in 73 additional SCN1A-negative patients . Nine different point mutations ( four missense and five truncating mutations ) were identified in 11 unrelated female patients . In addition , we demonstrated that the fibroblasts of our male patient were mosaic for the PCDH19 deletion . Patients with PCDH19 and SCN1A mutations had very similar clinical features including the association of early febrile and afebrile seizures , seizures occurring in clusters , developmental and language delays , behavioural disturbances , and cognitive regression . There were , however , slight but constant differences in the evolution of the patients , including fewer polymorphic seizures ( in particular rare myoclonic jerks and atypical absences ) in those with PCDH19 mutations . These results suggest that PCDH19 plays a major role in epileptic encephalopathies , with a clinical spectrum overlapping that of DS . This disorder mainly affects females . The identification of an affected mosaic male strongly supports the hypothesis that cellular interference is the pathogenic mechanism .
Epileptic encephalopathies are a group of rare disorders in which impairment of cognitive , behavioural and other brain functions is caused by the same underlying disease process . This heterogeneous group of disorders has multiple aetiologies such as symptomatic brain lesions , metabolic causes and diverse genetic syndromes . Much progress has been made in the past few years in the identification of genes responsible for genetic infantile epileptic encephalopathies . Among the genetic syndromes that have been characterized are: Dravet syndrome ( DS ) , also called severe myoclonic epilepsy of infancy ( SMEI , MIM# 607208 ) [1] , CDKL5/STK9 Rett-like epileptic encephalopathy [2] , [3] , ARX-related epileptic encephalopathies [4] , SRPX2-related rolandic epilepsy associated with oral and speech dyspraxia and mental retardation [5] , and very recently , female-limited epilepsy and cognitive impairment ( EFMR ) associated with mutations in PCDH19 , the gene encoding the protocadherin 19 on the X chromosome [6] . Dravet syndrome is characterized by the occurrence of generalized or unilateral clonic or tonic–clonic seizures , usually triggered by fever , in the first year of life of a previously normal infant . Later on , other types of seizures occur , including myoclonus , atypical absences and partial seizures [7] . Development is progressively delayed starting from the second year . Susceptibility to febrile seizures persists over time , and status epilepticus is frequent . Epilepsy generally persists despite appropriate anti-epileptic therapy ( polytherapy including sodium valproate , clobazam or topiramate and stiripentol ) . Children with DS typically have poorly developed language and motor skills , learning disabilities and variable degrees of mental retardation [8] . They are usually sporadic cases; however , sib pairs with SMEI , or patients with a family history of epilepsy , have occasionally been reported [9] . Heterozygous de novo mutations in SCN1A , the gene encoding the voltage-gated neuronal sodium channel alpha 1 subunit ( Nav1 . 1 ) , are a major cause of DS [1] . All types of mutations [10] and rearrangements [11]–[15] in SCN1A have been observed in SMEI patients . However , no point mutations or rearrangements have been found in a fraction of patients , now estimated to 20–25% [15]–[18] , strongly suggesting that DS is a genetically heterogeneous disorder . Our aim was to identify the gene ( s ) involved in SCN1A-negative patients with Dravet syndrome . Most of our patients were isolated , excluding the use of classical genetic approaches . Our hypothesis was that genomic micro-rearrangements , which are increasingly identified as causes of human genetic disorders , might be found in a subset of the SCN1A-negative patients with DS , thus identifying new causal genes . In this study , we have searched for genomic rearrangements in 41 SCN1A-negative patients using high-density SNP microarrays ( Illumina , 370K ) . Genes located in the rearrangements were then considered to be candidate genes and were analysed for point mutations by direct sequencing in the remaining negative patients with DS .
An initial series of 41 probands ( 18 females and 23 males ) , referred for genetic analysis of Dravet syndrome but negative for point mutation and intragenic rearrangement of SCN1A [15] , was screened for genomic rearrangement using Illumina 370CNV microarrays . A hemizygous deletion on chromosome Xq22 . 1 was identified in a male patient ( patient 1 from family 1 ) . This deletion spanned approximately 1 Mb and encompassed a single gene , PCDH19 ( Figure 1A ) . A duplication of the same region was previously reported in one of 776 healthy controls ( 506 unrelated healthy individuals from Northern Germany and 270 HapMap subjects ) [19] , but no deletions in healthy individuals have been recorded in the database of genomic variants . Patient 1 and his mother were then analyzed with high-resolution CGH arrays ( Nimblegen ) . This analysis confirmed that the deletion spans 890 Kb , between genomic positions g . 98731380 and g . 99618794 on chromosome X , and showed that it has occurred de novo since it was not found in the mother of the patient ( Figure 1B ) . PCDH19 encodes protocadherin 19 , a transmembrane protein of the cadherin family of calcium-dependent cell–cell adhesion molecules , which is strongly expressed in the central nervous system . In the postnatal brain , protocadherins might be involved in the modulation of synaptic transmission and the generation of specific synaptic connections [20] . PCDH19 was therefore an attractive candidate gene for epilepsies and mental retardation . To test whether a PCDH19 deficiency might be implicated in some epileptic encephalopathies resembling Dravet syndrome , we sequenced the coding region of this gene in 73 SCN1A-negative probands ( the remaining 40 patients of the initial series plus 33 additional patients , for a total of 45 females and 28 males ) . Ten different variants were identified in 11 unrelated female probands at the heterozygous state ( Figure 2A ) . All but one were located in exon 1: three were nonsense mutations ( c . 142G>T/p . Glu48X , c . 352G>T/p . Glu118X , c . 859G>T/p . Glu287X ) , two were small deletions and insertions creating a frameshift ( c . 506delC/p . Thr169SerfsX43 and c . 1036_1040dup/p . Asn347LysfsX23 ) and the remaining five were missense mutations ( c . 361G>A/p . Asp121Asn , c . 595 G>C/p . Glu199Gln , c . 1019A>G/p . Asn340Ser , c . 1628 T>C/p . Leu543Pro and c . 3319 C>G/p . Arg1107Gly ) . Glu48X was present in two affected sisters of family 2; Glu118X was identified in an isolated patient ( family 3 ) and Glu287X was found independently in a patient with family history of epilepsy and mental retardation ( family 4 ) and in an isolated patient ( family 5 ) . Interestingly , the c . 3319 C>G/p . Arg1107Gly missense variant , located in exon 6 , was associated with the p . Glu287X mutation in the proband of family 5 . In family 6 , cytosine 506 ( c . 506delC ) was deleted in a patient whose parents were unaffected , but whose female cousin also had epilepsy and moderate mental retardation . The 5-bp duplication ( c . 1036_1040dup ) was present in the index case of family 7 . The p . Asp121Asn mutation was identified in the index case of family 8 , who had a sister with epilepsy and psychotic disturbances . Finally , p . Glu199Gln , p . Asn340Ser and p . Leu543Pro variants were identified in the 4 remaining isolated patients ( families 9 to 12 ) ; Asn340Ser was found in two independent patients ( families 10 and 11 ) . These 4 missense variants ( p . Asp121Asn , p . Glu199Gln , p . Asn340Ser and p . Leu543Pro ) all affected amino-acids in the extracellular domain of protocadherin 19 , which are highly conserved in orthologs and in paralogs of PCDH19 in the delta protocadherin family ( Figure 2B ) . Interestingly , p . Arg1107Gly , associated with the de novo Glu287X mutation in the proband of family 5 , affected a residue of the protein that is conserved in mammalian orthologs , but not in other species or in paralogs ( Figure 2B ) . To confirm that the variants are pathogenic , we screened 180 healthy Caucasians . Only Arg1107Gly was found in a healthy female individual and was thus considered to be a rare polymorphism . None of the other variants was found in the control population , confirming that they are causal mutations . The parents and relatives of PCDH19-positive patients were also analysed when possible ( Figure 3 ) . The p . Glu48X mutation , found in two affected sibs in family 2 , was inherited from their asymptomatic father . Likewise , the c . 1036_1040dup5 , p . Asp121Asn and p . Leu543Pro mutations were inherited from the healthy fathers of the index cases in families 6 , 8 and 12 . In family 6 , the 5-bp duplication was inherited from the paternal grandmother who also had epilepsy and cognitive impairment , and transmitted to the half-brother of the father and his affected daughter ( i . e . the index case's cousin , Figure 3 ) . In family 4 , the mother of the proband had mental retardation associated with adult-onset epilepsy , a clinical feature also present in the maternal grandmother and maternal aunt; the proband's father also presented with moderate mental retardation but without epilepsy . The Glu287X mutation in this family was also inherited from the father . In contrast , in families 5 , 7 , 10 and 11 , the mutations ( p . Glu287X , c . 506delC and p . Asn340Ser , respectively ) occurred de novo in the index cases , since they were not found in either parent . Interestingly , Arg1107Gly was inherited from the asymptomatic father in family 5 . In family 4 , only the mother and sisters of the index case were available for genetic analyses . Both sisters , who were monozygous twins , had mild psychomotor and cognitive impairment but never had seizures . Neither the mother nor the sisters had the p . Glu287X mutation . Analysis of the haplotypes in Xq22 . 1 ( PCDH19 locus ) with microsatellite markers confirmed that the three sisters ( the two twins and the affected proband ) received the same X chromosome from their father , with and without the p . Glu287X mutation , which indicates that the mutation also occurred de novo in this family . Finally , in family 9 , the mother did not have the p . Glu199Gln mutation but the father remained unavailable for genetic analyses . Recently , PCDH19 mutations were shown to cause epilepsy and mental retardation limited to females ( EFMR ) , a familial disorder associating childhood-onset epilepsy and a variable degree of cognitive impairment with an unusual mode of inheritance: this X-linked disorder is found in females with heterozygous mutations but not in males with hemizygous mutations [6] . How , then , can we explain the affected male in our series with a deletion of the entire PCDH19 ? Random X-inactivation in mutated females normally leads to tissue mosaicism in which two cell populations , one expressing normal PCDH19 and the other expressing the mutated allele , co-exist . To explain why only females are affected , it might be hypothesized that the co-existence of PCDH19-positive and PCDH19-negative cells would be pathogenic whereas homogeneous cell populations ( PCDH19-positive in normal individuals but PCDH19-negative in mutated males ) would not [6] . A mechanism of this type was previously termed “cellular interference” [21] . Two cell populations would also be found in mosaic males , who , according to this hypothesis , would be affected like mutated females . To test whether our male patient was mosaic for the PCDH19 gene deletion , we compared peripheral blood lymphocytes ( PBL ) and cultured fibroblasts from the patient by FISH with a probe specific to the PCDH19 genomic region . Although no signal corresponding to PCDH19 was detectable in PBL , a normal PCDH19 allele was found in 53% of the fibroblasts ( Figure 4 ) , confirming that the patient was mosaic , in his skin , for the PCDH19 deletion . This result confirms that mutations in PCDH19 can be responsible , in mosaic males , for epileptic encephalopathy phenotypes that are usually limited to females , and strongly supports the hypothesis that cellular interference is the main pathogenic mechanism of the disease . The clinical features of the male patient with the PCDH19 deletion and the female patients with PCDH19 point mutations are summarized in Table 1 . These patients fulfil the main criteria for DS ( see material and methods' section ) , with a mean age of seizures onset of 9 . 5 months ( ranging from 7 . 5 to 12 months ) . Nevertheless , contrary to SCN1A-positive patients ( SCN1A-DS ) , myoclonic jerks , atypical absences , and photosensitivity were unfrequent in PCDH19-positive patients ( PCDH19-DS ) ( 3 , 3 and 1 patients out of 13 , respectively ) . Only 6 patients presented status epilepticus . The mental delay was mild in 6 patients , moderate in 4 and only 3 patients presented with severe delay . Although much delayed , the language was present in all patients , with 12 out of 13 able to formulate short sentences .
In this study , we used SNP microarrays to search for microrearrangements in patients with clinical features suggestive of Dravet syndrome but without mutations in SCN1A in order to identify new causative genes . The identification of a de novo hemizygous deletion of PCDH19 , encoding protocadherin 19 , in a male patient led us to screen the coding region of this gene in the remaining patients . Eleven unrelated probands with point mutations in PCDH19 , all females , were found . While this study was ongoing , PCDH19 was reported to be the causative gene for female-limited epilepsy and cognitive impairment ( EFMR ) , a disorder characterized by seizure onset in infancy or early childhood and cognitive impairment , which is found only in females in multi-generational families [6] . Since all of our patients with point mutations in PCDH19 were females as previously reported , we investigated the possibility that the male patient in whom the gene was deleted might be mosaic for the deletion . FISH analysis confirmed this latter hypothesis . The thirteen patients with PCDH19 mutation or deletion ( 12 probands and one sib , family 2 ) all fulfilled the main criteria for DS and were all negative for mutation or rearrangement in SCN1A after direct sequencing and multiplex ligation-dependent probe amplification ( MLPA ) [15] . The proportion of PCDH19-DS probands in our series of SCN1A-negative patients was 16% ( 12/74 ) , or even 25% ( 11/45 ) if only female patients were included in the calculation . Considering that approximately 25% of all patients with DS are SCN1A-negative [15] , PCDH19 might overall account for 5% of DS patients . PCDH19-DS patients and SCN1A-DS patients have many features in common including: normal psychomotor development before seizures onset , early onset of seizures ( before age one year ) , association of febrile and afebrile seizures , with a high susceptibility of the seizures to fever for all 13 patients , occurrence of hemiclonic or unilateral seizures ( 11/13 ) , and association of generalized tonic-clonic and focal seizures ( 12/13 ) , a high proportion of seizures occurring in clusters ( 12/13 ) , prolonged seizures , a proportion of which lead to status epilepticus , secondary progressive appearance of mental and motor regression and language delay , accompanied , in some cases , with ataxia ( Table 1 ) . However , PCDH19-DS patients slightly differ on average from the classical pattern reported in SCN1A-DS . PCDH19-DS patients were slightly older at onset than SCN1A-DS patients ( 9 . 5 months , with a range from 7 . 5 to 12 months , versus 6 . 3 months , calculated from our series of SCN1A-positive DS patients , p<0 . 0001 ) [15] . Less than half ( 6/13 ) of the PCDH19-DS patients had status epilepticus although this is a highly frequent feature in SCN1A-DS ( 93/113 , p<0 . 007 ) . Photosensitivity , frequently reported in SCN1A-DS , was exceptional in PCDH19-DS and was reported in only one patient but the difference with SCN1A-DS in our series of patients was however not significant . Seizures were , on average , less intractable than in SCN1A-DS , and patients above six years of age ( 9/12 patients ) had less than 4 seizures a year with one patient who was free of seizures at the time of the study . Although all patients were on tri- or poly-therapy , seizures were relatively well-controlled , a situation rarely achieved in SCN1A-DS . Intellectual and language delay were constant but were less severe than the classical outcome of SCN1A-DS [8] ( mostly with important speech and mental delay ) although the difference was not significant . Finally , myoclonic jerks and atypical absences were present in only 2 and 3 patients , respectively , whereas they are frequent features in SCN1A-DS ( myoclonic jerks: 55/110 , p<0 . 018; atypical absences: 92/108 , p<0 . 0001 ) . Patients with SMEI but without myoclonia have been previously referred as SMEB ( borderline severe myoclonic epilepsy in infancy ) [22] , but SMEI and SMEB are currently grouped together under the term DS . In addition , the same types of mutation , and even the same mutations , are found in patients with DS and patients with other infantile epileptic encephalopathies ( such as cryptogenic generalized or focal epilepsies ) , which has extended the clinical spectrum of SCN1A and the definition of DS [15] . Therefore , in individuals , these divergent clinical characteristics are not sufficient to distinguish between patients with SCN1A or PCDH19 mutations , and the two clinical spectrums largely overlap . They can be useful , however , to prioritize molecular diagnosis although they must be first confirmed on larger series . Mutations in PCDH19 were recently reported to cause EFMR , which also associates mental retardation and epilepsy exclusively in females . EFMR was differentiated from DS by the authors on both clinical and genetic grounds [6] , [23] . The clinical features of EFMR , unlike those of DS , are highly variable , even in members of the same family: onset of seizures is between 6 and 36 months , the patients present with a combination of febrile and afebrile seizures of various types and a variable degree of psychomotor delay and cognitive impairment , ranging from mild to severe mental retardation [23] . Dibbens et al . reported PCDH19 mutations in six large families and one small family with two affected sib pairs [6] . All the patients were familial cases that were , for the most part , already adults at the time of examination , and appeared socially integrated in that most of them were married and had children . In the present study , on the contrary , the patients were essentially young , had a severe epileptic encephalopathy , and 8 of the 12 were isolated cases . In 6 patients out of 11 in whom inheritance could be assessed , the mutation occurred de novo . In the 5 remaining patients , the mutation was inherited from fathers who were healthy , had no cognitive impairment , and never had febrile seizures or epilepsy ( families 2 , 6 , 8 and 12 ) , or had mild mental retardation but no epilepsy ( family 4 ) . The global clinical pictures of PCDH19-DS and EFMR appear therefore to differ . The difference in the phenotypes might be due to the modes of recruitment ( familial versus sporadic cases ) . It might also be hypothesized , that patients with PCDH19-DS have a better final outcome than in those with SCN1A-DS despite the severity of their disease in childhood and that the two disorders are different clinical expressions of the same disease . Both hypotheses are not mutually exclusive . Interestingly , the variability in the severity of epilepsy and cognitive impairment in EFMR is reminiscent of what is observed in GEFS+ families ( generalized epilepsy with febrile seizures plus , # MIM# 604233 ) , an autosomal dominant condition that also associates febrile seizures with epilepsy of variable types and severity , and which is associated in ∼10–15% of the families with missense mutations in SCN1A [10] . Although patients with GEFS+ are usually responsive to treatment and generally have a benign outcome , some family members may be more severely affected , and even present with DS . The clinical spectrum of PCDH19 mutations could be as broad as the spectrum of GEFS+ . Random X inactivation could contribute to this variability by generating variable proportions of mutated to normal cells in the brains of the mutated females . Although the mutations in EFMR families and in PCDH19-DS patients are distinct , the spectra of mutations are comparable , and include nonsense mutations , small deletions/insertions introducing a frameshift as well as missense mutations affecting highly-conserved amino-acids in the protein ( Figure 5 ) , which would probably cause loss-of-function of the mutated allele . Messenger RNAs with mutations introducing premature termination codons ( PTC ) have indeed been shown to be degraded via the nonsense-mediated mRNA decay ( NMD ) surveillance system of the cell in fibroblasts from EFMR patients [6] . The identification of a whole gene deletion in the mosaic male patient with PCDH19-DS also supports the loss-of-function as the main consequence of the mutations . However , all the point mutations identified so far are clustered in the large exon 1 of the gene corresponding to the extra-cellular cadherin domain of the protocadherin 19 protein , as previously reported by Dibbens and collaborators [6] . Further studies are needed to determine whether PTC mutations can be found in other exons; this would be expected if the loss-of-function assumption is correct . EFMR and PCDH19-DS are paradoxical X-linked disorders in which mutated females are severely affected whereas males carrying the mutation are phenotypically unaffected: they have normal cognitive function and no seizures although a subtle psychiatric carrier status was evoked [6] , [23] . All affected patients with point mutations identified in this study were also females . In families 2 , 4 , 6 , 8 and 12 , the mutation was inherited from the father . Five males ( families 2 , 6 and 8 ) were asymptomatic carriers of PCDH19 mutations , they were healthy , had no cognitive impairment or epilepsy , and none had histories of febrile seizures . In family 4 , however , the father who transmitted the mutation to his daughter had moderate mental retardation but no epilepsy . The link between the mutation and his cognitive impairment remains , however , uncertain . The only definitely affected male was , therefore , the patient who was mosaic for the PCDH19 deletion . There was no molecular evidence of mosaicism in the blood of the father in family 4 . Several mechanisms have been suggested to account for the unusual mode of inheritance observed in EFMR . 1 ) A dominant negative effect of the mutant protein in females ( as for mutations in STK9/CDKL5 and MECP2 ) is unlikely , since it is usually associated with lethality in males . 2 ) Compensatory factors may exist in males; in particular , a protocadherin gene on the Y chromosome ( PCDH11Y ) is specifically expressed in males and could play a role in a sex-dependent compensation; a paralogous gene is located on the X chromosome ( PCDH11X ) , but the proteins encoded by the two genes are not identical [6] . In addition , the protocadherin family contains more than 80 genes scattered throughout the human genome [24] , supporting the hypothesis of molecular compensation . 3 ) Another explanation for the unusual mode of inheritance associated with PCDH19 mutations is cellular interference , a mechanism reminiscent of metabolic interference [6] , [21] , [25] . It postulates that random inactivation of one X chromosome in mutated females generates tissue mosaicism ( i . e . co-existence of PCDH19-positive or PCDH19-negative cells ) , which would be pathogenic by altering cell-cell interactions; normal individuals and mutated males , who are homogeneous for PCDH19-positive or PCDH19-negative cells respectively , would not develop the disease ( Figure 6 ) . The identification of an affected male who was mosaic for the PCDH19 deletion in his fibroblasts , and therefore had PCDH19-positive and PCDH19-negative cells in this tissue , strongly supports the hypothesis of cellular interference as the main pathogenic mechanism associated with PCDH19 mutations . The co-existence of normal and mutated cells and the proportion of each population in the brain of this patient cannot , however , be extrapolated from fibroblasts or lymphocytes . To definitely establish that cellular interference is the pathogenic mechanism , it is necessary i ) to demonstrate that neuronal cells are mosaic , but also that ii ) females who are homozygous for PCDH19 mutations or deletions are also unaffected , like hemizygous males . Although pathogenesis in cells that express the mutated allele after inactivation corresponds to a loss-of-function , cellular interference would result in a gain-of-function at the tissue level , because of abnormal interactions between mutated and normal cells . This hypothesis supposes that the loss of protocadherin 19 is compensated for , but by a mechanism that is relatively independent of gender . The same X-linked pattern of inheritance and has been observed for craniofrontonasal syndrome ( CFNS ) , a disorder in which females have multiple skeletal malformations . The gene responsible for CFNS is EFNB1 , located in Xq12 and encoding Ephrin B1 , a transmembrane protein that is a ligand for Eph receptors [21] . The Ephrin B1/Eph interaction plays a role in cell migration and pattern formation during developmental morphogenesis [26] . Cellular interference , also proposed as the pathogenic mechanism for CFNS [21] , had previously been demonstrated in female mice heterozygous for Ephrin B [27] . Although homozygous female and hemizygous male mice showed comparable perinatal lethality due to major skeletal abnormalities , heterozygous females were even more affected , and they alone had polydactyly . Ephrin B1-EphB receptor signaling was shown to regulate skeletal development by controlling cell movement . Mosaic expression of Ephrin B1 , caused by random X inactivation in heterozygous females , results in ectopic interactions between the Ephrin B1 ligand and EphB receptors , sufficient to induce the skeletal defects [27] . Protocadherin 19 is an 1148 amino-acids transmembrane protein belonging to the protocadherin delta2 subclass of the cadherin superfamily , which is highly expressed in neural tissues and at different developmental stages [6] , [28] , [29] . The precise functions of the protein remain so far unknown . However , Delta protocadherins were reported to mediate cell-cell adhesion in vitro and cell sorting in vivo , and could regulate the establishment of neuronal connections during brain development [24] , [30] . Ephrin B1 and protocadherin 19 could therefore share major characteristics . Several isoforms of protocadherin 19 have been reported to result from alternative splicing of exon 2 and the existence of two acceptor sites for intron 4 which adds a residue at the beginning of exon 5 . The isoform ( s ) implicated in the physiopathology of EFMR and PCDH19-DS are still not known . Functional studies as well as the development of mouse models are now needed to confirm and unravel the molecular mechanisms of cellular interference in these diseases . In conclusion , these results extend the clinical spectrum associated with PCDH19 mutations: we demonstrated that mutations in this gene are not limited to familial female patients , but can also account for isolated cases . Our results suggest that isolated mosaic male patients are also susceptible to the disease . Finally , mutations in PCDH19 can cause an early and severe epileptic encephalopathy mimicking DS , a major problem for differential diagnosis . The high frequency of patients with PCDH19 found in this study justifies the molecular testing of this gene in SCN1A-negative patients , especially females , diagnosed as having Dravet syndrome . This study also validates the use of SNP microarrays to identify novel genes in isolated patients with severe genetic pathologies . This strategy will hopefully identify new genomic regions or genes that would account for the ∼15–20% of DS patients that do not have SCN1A and PCDH19 mutations .
A total of in 74 probands ( 45 females and 29 males ) , referred by specialized neuropediatric centres as having Dravet syndrome but who were negative for point mutations or rearrangements in SCN1A , were included in the study [15] . Forty-one of these patients were initially selected for the microarray analysis and 33 were later on included for sequencing of PCDH19 . The referring physicians filled out detailed clinical questionnaires for every patient . Clinical histories were also obtained when possible to assess the evolution of the disease . All clinical reports and questionnaires were re-examined by the same neuropediatrician ( RN ) . Intellectual assessment was based on psychological evaluation when available . Psychomotor skills and cognitive delay were clinically evaluated in all patients . The clinical diagnosis of DS included: normal cognitive and motor development prior to seizures onset , onset of the seizures before the age of one year , seizures mainly triggered by fever , long-lasting seizures ( >15 min , that might evolve to status epilepticus ) , later occurrence of other types of seizures ( febrile and afebrile ) and cognitive regression . The presence of myoclonic jerks and/or ataxia was considered to be a highly characteristic , although inconstant , feature of the disease that could reinforce a diagnosis; however , their absence did not exclude the clinical diagnosis of Dravet syndrome , since they were not previously observed in all patients with DS [7] , [31] . Informed written consent was obtained from the patients' parents before blood sampling . This study was approved by the ethical committee ( CCPPRB of Pitié-Salpêtrière Hospital , Paris , n°69-03 , 25/9/2003 ) . Patients were screened using Illumina 370CNV-Duo genotyping BeadChip arrays ( 370 K ) . The Infinium II Genotyping reaction steps were performed according to the manufacturer's specifications ( Illumina , San Diego , CA ) on the P3S platform ( Pitié-Salpêtrière Hospital ) . Briefly , 750 ng of genomic DNA were isothermally amplified at 37°C overnight . The amplified products were fragmented by a controlled enzymatic process then precipitated with isopropanol . The dried precipitated pellet was resuspended , hybridized to 370CNV-Duo beadchips in a capillary flow-through chamber and incubated overnight at 48°C . The amplified , fragmented DNA samples anneal to locus-specific 50-mers during the hybridization step . Each bead type corresponds to one allele per SNP locus . After hybridization , allelic specificity was conferred by enzymatic single-base extension and fluorescent staining . Arrays were washed and dried for 1 h before imaging using a BeadArray Reader ( Illumina ) . Image data analysis and automated genotype calling was performed using Beadstudio 3 . 1 ( Illumina ) . All genomic positions were based on the UCSC and Ensembl Genome Browsers . Each copy number variant ( CNV ) identified in patients was searched in the database of genomic variants ( http://projects . tcag . ca/variation/ ) , which repertories the structural variation in the Human genome , to determine whether this CNV is normally present in a control population . Genomic DNA from the patients was analysed by microarray-based comparative genomic hybridization with the HG18 WG Tiling 385 K CGH array v2 . 0 ( Roche NimbleGen , Madison , WI ) , according to the NimbleGen hybridization Kit Protocol . Briefly , DNA samples from patients and controls were labelled by random priming: the DNA ( 1 µg ) was denatured in the presence of 5′Cy3- or Cy5-labeled random nanomers ( Trilink Biotehcnologies , San Diego , CA ) and incubated with 100 units of exo-klenow fragment ( NEB , Beverly , MA ) and dNTP mix [6 mM each in TE buffer ( 10 mMTris/1 mM EDTA , pH 7 . 4 , Invitrogen ) ] for 2 h at 37°C . Reactions were terminated by addition of 0 . 5 mM EDTA ( pH 8 . 0 ) , precipitated with isopropanol and resuspended in water . The Cy-labelled test sample ( Cy3 ) and the reference sample ( Cy5 ) were combined in 13 µL of Nimblegen Hybridization solution ( Roche Nimblegen ) . After denaturation , hybridization was carried out on a MAUI Hybridization System ( BioMicro Systems , Salt Lake City , NE ) for 18 h at 42°C . The array was washed with the NimbleGen Wash System ( Roche NimbleGen ) , dried by centrifugation and scanned with the genePix 4000B scanner ( Axon Instrument , Union City , CA ) . Fluorescence intensity ( raw data ) was obtained from the scanned images of the oligonucleotide tiling arrays with NIMBLESCAN 2 . 0 extraction software ( Nimblegen Systems ) . For each spot on the array , log2 ratios of the Cy3-labeled test sample versus Cy5 reference sample were calculated . Regions were considered to be duplicated or deleted when result exceeded the +/−0 . 25 . Eleven specific primer pairs were designed to amplify the 6 exons and adjacent intron-exon boundaries ( ∼100 bp from each side of the exons ) of the PCDH19 gene ( transcript reference EF676096 ) . Primer sequences are available on request . Forward and reverse sequence reactions were performed with the Big Dye Terminator Cycle Sequencing Ready Reaction Kit ( PE Applied Biosystems ) using the same primers . G50-purified sequence products were run on an ABI 3730 automated sequencer ( PE Applied Biosystems ) and data were analyzed with the Seqscape 2 . 5 software ( Applied Biosystems ) . Mutations identified in the patients were looked for directly in the DNA of available parents by sequencing the corresponding amplicon . If neither parent had the mutation , the parents were tested with microsatellite markers at the Xq22 . 1 locus to ensure that the mutation occurred de novo . In addition , 180 European controls ( 90 males and 90 females ) were included to test new variants in the PCDH19 gene . FISH experiments were performed on peripheral blood lymphocytes ( blood samples ) and fibroblasts ( skin biopsies ) . Fibroblasts were grown in Dulbecco's modified Eagle's medium containing 4 . 5 mg/ml glucose and 110 µg/ml pyruvate ( DMEM ) supplemented with 10% fetal calf serum ( FCS ) , 0 . 03% glutamine , 1000 U/ml penicillin/streptomycin in a 5% CO2 atmosphere for 2 weeks before FISH . Lymphocytes were grown in PB-Max medium ( Invitrogen ) for 3 days . Metaphase chromosome spreads were obtained by standard hypotonic treatment and methanol/acetate ( 3/1 ) fixation . The slides were washed with the cytology FISH accessory kit ( Dako ) . A FISH DNA probe , specific for the Xq22 . 1 region covering PCDH19 , was labeled with rhodamine by nick-translation after amplification of the RP11-99E24 BAC ( Invitrogen ) and cohybridized with a commercial subtelomeric control probe ( Cytocell ) , specific for the pseudo-autosomal region 1 ( chromosomes X/Y ) labeled with fluorescein isothiocyanate ( FITC ) . The slides were then washed and counterstained with 4 , 6-diamino-2-phenylindole ( DAPI ) for chromosome identification . Metaphase cells were examined under a motorized reflected BX61 Olympus fluorescence microscope with filters for separate detection of DAPI , FITC and rhodamine . One hundred metaphase cells were counted to determine the degree of mosaicism in fibroblasts and lymphocytes . Metaphase chromosomes from a karyotypically normal female were used as a control . Frequencies were compared with the Chi-Square test or the Fisher exact test when appropriate . Means were compared using Mann-Whitney Rank Sum Test . Statistical analysis was performed using SigmaStat 3 . 5 software .
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Severe epilepsies associated with cognitive impairment in children are multifarious and most affected patients are sporadic cases . Thus , there is a challenge to identify which of these epilepsies are genetically determined , since their sporadic status excludes the use of classical genetic approaches . We have used microarrays , which are new technological tools to investigate the whole genome of an individual , to search for small genomic abnormalities and identify novel genes in 41 patients with a clinically well-characterized severe infantile epileptic disorder called Dravet syndrome . We have identified PCDH19 , a new gene on chromosome X , which was recently found in a familial epileptic syndrome known as female-limited epilepsy and cognitive impairment . This gene was mutated in 12 out of 74 patients with clinical features compatible with Dravet syndrome . Eleven of these patients were females . The single male with a PCDH19 deficiency was mosaic in his skin; i . e . , some of his cells express PCDH19 and others do not . This finding suggests that a new pathogenic mechanism—cellular interference—is associated with an unusual X-linked mode of inheritance in which females are more frequently affected than males .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"and",
"genomics/gene",
"discovery",
"cell",
"biology/neuronal",
"signaling",
"mechanisms",
"genetics",
"and",
"genomics/genetics",
"of",
"disease",
"neurological",
"disorders/epilepsy",
"cell",
"biology/cell",
"adhesion",
"genetics",
"and",
"genomics/medical",
"genetics"
] |
2009
|
Sporadic Infantile Epileptic Encephalopathy Caused by Mutations in PCDH19 Resembles Dravet Syndrome but Mainly Affects Females
|
Dengue virus is a mosquito-borne flavivirus that has a large impact in global health . It is considered as one of the medically important arboviruses , and developing a preventive or therapeutic solution remains a top priority in the medical and scientific community . Drug discovery programs for potential dengue antivirals have increased dramatically over the last decade , largely in part to the introduction of high-throughput assays . In this study , we have developed an image-based dengue high-throughput/high-content assay ( HT/HCA ) using an innovative computer vision approach to screen a kinase-focused library for anti-dengue compounds . Using this dengue HT/HCA , we identified a group of compounds with a 4- ( 1-aminoethyl ) -N-methylthiazol-2-amine as a common core structure that inhibits dengue viral infection in a human liver-derived cell line ( Huh-7 . 5 cells ) . Compounds CND1201 , CND1203 and CND1243 exhibited strong antiviral activities against all four dengue serotypes . Plaque reduction and time-of-addition assays suggests that these compounds interfere with the late stage of viral infection cycle . These findings demonstrate that our image-based dengue HT/HCA is a reliable tool that can be used to screen various chemical libraries for potential dengue antiviral candidates .
Dengue virus ( DENV ) is an important mosquito-borne pathogen responsible for causing dengue fever ( DF ) and the more severe , life-threatening dengue hemorrhagic fever/shock syndrome ( DHF/DSS ) [1] . DENV is a small , enveloped virus belonging to the genus Flavivirus , family Flaviviridae . [2] . Dengue virions are approximately 50 nm in diameter [3] , containing a single-stranded positive RNA of ∼11 kilobase with a genomic organization: 5′-C-preM-E-NS1-NS2A/B-NS3-NS4A/B-NS5-3′ , and flanked by the 5′ and 3′ UTRs ( untranslated regions ) [4] , [5] . There are 4 serotypes of dengue ( DENV1 , DENV2 , DENV3 , DENV4 ) , with two or more serotypes commonly found to co-circulate in many dengue endemic areas [6] , [7] . The presence of more than 1 dengue serotype in a geographical area contribute to the persistence of epidemics , as immunity acquired against one dengue serotype does not confer long-term protective immunity against heterologous serotypes [8] . Conversely , individuals that have acquired humoral immunity against one dengue serotype may be pre-disposed to DHF/DSS when subsequently infected with a heterologous serotype through antibody-dependent enhancement [9] , [10] . Since its re-emergence in 1953 , DENV has spread rapidly across 5 continents and more than 100 countries , mostly in tropical and subtropical regions . Present estimates by the World Health Organization place nearly 2 . 5 billion people at risk of dengue , with approximately 50 million cases of dengue infection and 20 thousand mortalities occurring annually [11] . Due to the global burden of dengue , numerous studies have been done to elucidate the nature of dengue infection and the underlying mechanisms of DF and DHF/DSS . Although ADE is the most widely accepted theory for the occurrence of DHF/DSS , several studies have suggested higher viremia titers , virus serotype and host genetic background as determinants of DHF/DSS [12] , [13] . Extensive genetic , cellular and immunological studies have investigated the role of host innate immunity in the events leading to DHF/DSS [14]–[16] . However , a clear understanding on the immunopathology of dengue infections remains elusive . With the number of DHF/DSS cases rising every year , the demand for a preventive , prophylactic , or therapeutic measure against DENV is growing rapidly . Significant progress has been achieved in the development of a dengue vaccine , including one vaccine candidate ( ChimeriVax ) that has passed through Phase II clinical trials [17] . However , its long-term efficacy and safety has not been established and mass production of this vaccine candidate to meet the growing demand remains a daunting task . A new approach that is gradually gaining interest is the development of dengue antivirals . For the last 6 years , several drug candidates for HCV and other RNA viruses have been pursued for repositioning as potential drug candidates for dengue [18] . Though at present , none of these compounds have gone beyond pre-clinical trials . Recent advancements in high-throughput screening ( HTS ) technologies have contributed to increasing efficiency in the drug discovery process . These include in silico HTS , in vitro enzymatic assays , cell-based reporter assays , image-based whole infection assays , among others [19] . In the work reported here , we describe the development of an image-based high-throughput/high-content assay ( HT/HCA ) screening method for anti-dengue compounds using an infectious virus system and an innovative approach in image analysis . Using this dengue HT/HCA system , we screened a BioFocus kinase inhibitor library of 4 , 000 small molecules against DENV1-4 to identify compounds that possess antiviral activity against all 4 serotypes during infection of a human host cell . We counter-screened the primary hits against DENV infection in Aedes albopictus clone C6/36 , hepatitis C virus ( Family Flaviviridae , genus Hepacivirus ) infection , and chikungunya virus ( Family Togaviridae , genus Alphavirus ) infection to partially characterize the compounds as having a host-specific or virus-specific target . The dengue hit compounds were clustered based on their chemical structures and , together with the activity profile , used to identify scaffolds whose antiviral activity against the 4 serotypes vary depending on the chemical substituents . These scaffolds identified from our dengue HT/HCA screening could be used as potential starting points for the development of dengue antivirals .
The mosquito cell line C6/36 Aedes albopictus clone ( CRL-1660 ) , mouse hybridoma cells D1-4G2-4-15 ( HB-112 ) , and human hepatocyte Huh-7 . 5 ( PTA-8561 , U . S . Patent Number 7455969 ) were obtained from the American Type Culture Collection . HuH-7 ( JCRB0403 ) was kindly provided by Dr . Katja Fink . Three South American isolates of dengue viruses: Den1 BR/90 ( GenBank AF226685 . 2 ) , BR DEN2 01-01 ( GenBank JX073928 ) , BR DEN3 290-02 ( GenBank EF629369 . 1 ) , and the World Health Organization laboratory strain DEN4 TVP-360 were generously provided by Dr . Claudia N . Duarte dos Santos . Hepatitis C virus ( HCV ) genotype 2a ( JFH-1 ) expressing the NS5a-GFP fusion protein was kindly provided by Dr . Marc Windisch and the chikungunya virus ( CHIKV-118-GFP ) was a generous gift from Dr . Olivier Schwartz . C6/36 was maintained at 28°C in Leibovitz's L-15 media ( Gibco/Invitrogen , USA ) supplemented with 5% Fetal Bovine Serum ( FBS , Gibco/Invitrogen , USA ) , 0 . 26% Tryptose Phosphate Broth ( TPB , Sigma-Aldrich , USA ) and 25 µg/mL Gentamicin Sulfate ( Gibco/Invitrogen , USA ) and passaged every 3–4 days . Huh-7 . 5 was maintained under humidified conditions at 37°C , 5% CO2 in Dulbecco's minimum essential medium/Hank's F-12 ( DMEM/F12 , 1∶1 ) ( Gibco/Invitrogen , USA ) supplemented with 10% FBS and 100 U/mL Penicillin/100 µg/mL Streptomycin ( antibiotic solution , Gibco/Invitrogen , USA ) and passaged every 3–4 days . HuH-7 was cultured under humidified conditions at 37°C , 5% CO2 in RPMI 1640 containing 25 mM HEPES ( WelGene , South Korea ) supplemented with 10% FBS and antibiotic solution . Low passaged dengue viruses were propagated for 7 days in C6/36 maintained at 28°C in Virus Medium ( VM: Leibovitz's L-15 medium supplemented with 1% FBS , 0 . 26% TPB , 25 µg/mL Gentamicin ) according to previously described methods [20] and titrated by focus formation assay ( FFA ) using C6/36 as previously described [21] . Dengue virus titers were expressed as focus forming units per mL ( ffu/mL ) . Flavivirus group-specific αE monoclonal antibody 4G2 [22] , used as detecting antibody , was prepared from culture supernatant of D1-4G2-4-15 maintained under humidified conditions at 37°C , 5% CO2 in RPMI 1640 containing 25 mM HEPES ( WelGENE , South Korea ) and supplemented with 10% FBS , 1 mM sodium pyruvate ( Sigma-Aldrich , USA ) , antibiotic solution and 250 ng/mL Amphotericin B ( Sigma-Aldrich , USA ) . 4G2 was concentrated by ammonium sulfate precipitation following previously described methods [23] and purified by protein G affinity chromatography ( GE Amersham , Sweden ) according to manufacturer's instruction . Total antibody protein was determined by spectrophotometric analysis using the formula of Warburg and Christian [24] . A small target-focused chemical library , comprising of 4 , 000 synthesized compounds based on ligand binding of known kinase binding sites , was sourced from BioFocus ( Galapagos , Belgium ) . Reference compounds were purchased from TOCRIS Bioscience ( Bristol , UK ) : AZ 10417808 , BIBU 1361 , LE 135 and MPP and Sigma-Aldrich ( USA ) : Chloroquine , ribavirin and recombinant human Interferon-αA ( IFN-α2A ) . All compounds from the BioFocus kinase inhibitor library and reference compounds were prepared in 100% dimethyl sulfoxide ( DMSO , Sigma-Aldrich , USA ) , with the exception of IFNαA that was prepared in Dulbecco's Phosphate-buffered saline ( DPBS , WelGENE , South Korea ) containing 5% FBS . For dispensing of liquid media containing the host cell and viruses , the Thermo Scientific WellMate ( Fischer Brand , USA ) was used . Dispensing of antibody solutions and other liquid reagents for IFA , including the washing steps , was done using the 96/384-head BioTek EL406 automated liquid washer/dispenser ( BioTek , USA ) . For miniaturization of the image-based dengue HT/HCA , the following conditions were optimized: a ) host cell seeding density , b ) multiplicity of infection ( M . O . I . ) and c ) incubation period of infection . For the optimum host cell density , Huh-7 . 5 cells were prepared at various cell densities and seeded in a 384-well plate , μ-clear black ( Greiner Bio-one , Germany ) . The cells were cultured between 2–4 days at 37°C , 5% CO2 . For dengue virus infection , the optimum cell seeding density of Huh-7 . 5 was inoculated with DENV1 , DENV2 , DENV3 or DENV4 at various M . O . I . ( 0 . 1–5 ) and cultured between 2–4 days at 37°C , 5% CO2 . An immunofluorescence assay ( IFA ) used to detect dengue infection was optimized for the dengue HT/HCA . Briefly , cells were fixed with 4% ( w/v ) paraformaldehyde ( PFA ) for 20 min at room temperature ( Rm T ) . PFA-fixed cells were treated with 0 . 25% ( v/v ) Triton-X for 20 min at Rm T . DENV-infected cells were detected by probing with 4G2 mAb prepared in blocking buffer: DPBS containing 5% FBS for 30 min at 37°C , followed by AlexaFluor-conjugated goat anti-mouse IgG ( H+L ) ( Invitrogen Molecular Probes , USA ) prepared in blocking buffer for 30 min at 37°C . Cell nuclei counterstained with 5 µg/mL 4′ , 6-diamidino-2-phenylindole ( DAPI , Sigma-Aldrich , USA ) . Two washing cycles of DPBS was done after each step of the IFA . After the final washing , digital images were acquired using a high-throughput confocal fluorescence imaging system ( Evotec Technologies High-Throughput Cell Analyzer Opera , Perkin Elmer , USA ) . The digital images were taken from 3 different fields of each well at 20× magnification . Acquired images were analyzed using our in-house developed image-mining platform ( IM ) . This platform is designed to do high-content screening and directly access the database of images that were sequentially analyzed with specially designed algorithms developed as a customized plug-in to the IM platform . The results of all the analyses were stored in a centralized database . The IM plug-in for dengue HT/HCA works by independently analyzing two separate channels acquired with the Evotec Technologies High-Throughput Cell Analyzer Opera using different algorithms and converging these results to yield the final readout . One channel ( DAPI-channel ) captures the signal emitted by DAPI-stained nuclei at 450 nm , while the other channel ( A488-channel ) captures the signal emitted by the AlexaFluor 488 dye bound to the dengue E protein-antibody complexes confined in the cytoplasm of dengue-infected cells at 540 nm . To define the percentage of dengue-infected cells and , conversely the non-infected cells , a modified watershed method was applied . Compared to the original watershed algorithm [25] that uses the morphological gradient image , a weighted gradient image is used for the topographic surface , whose weights are defined as the ridge values computed from the eigen values of the original image . The percentage of non-infected cells , which is also defined as the percent inhibition or percent activity , is derived by using the formula: [1− ( A488-positive cells/total cells ) ]×100% . The dengue HT/HCA was validated by 1 ) by infecting Huh-7 . 5 cells in 384-well plates spotted with 0 . 5% DMSO with DENV or MOCK plated and 2 ) observing the dose-response curves of reference compounds previously reported to have anti-dengue activity . In both validation experiments , Huh-7 . 5 was mixed with DENV1 , DENV2 , DENV3 or DENV4 at a M . O . I . of 0 . 5 or VM only for MOCK-infection and dispensed in designated wells using the Thermo Scientific WellMate automated liquid dispenser . For the first validation experiment , the statistical reliability of the dengue HT/HCA was determined by calculating the Z'-factor for the percent infection or percent inhibition . Briefly , the Z'-factor of a defined parameter is calculated using the formula: 1−[ ( 3σp+3σn ) / ( |μp−μn| ) ] , where the μp , μn , σp and σn are the means ( μ ) and standard deviations ( σ ) of the positive ( p ) and negative ( n ) controls [26] . For the second validation experiment , the reference compounds were prepared in 2-fold serial dilutions and dispensed in duplicate wells in the 384-well plate , followed by the Huh-7 . 5 and DENV mixture ( M . O . I . 0 . 5 ) . Percent infection , percent inhibition , and percent cell number were determined using the customized IM platform plug-in . Scatter-plot distribution was generated using TIBCO Spotfire 4 . 5 . 0 ( TIBCO Software Inc . , Somerville , MA ) . The ten-point dose-response curves ( 10-pt DRCs ) were plotted using the non-linear regression formula: log ( inhibitor ) vs . response – variable slope ( 4 parameters ) , available in GraphPad Prism 5 . 04 ( GraphPad Software Inc . , San Diego , CA ) . The top and bottom values of the reference compounds were unconstrained when the curve fittings of the 10-pt . DRCs were generated . The BioFocus kinase inhibitor library were screened against DENV1 , DEN2 , DENV3 and DENV4 at 10 µM in 0 . 5% ( v/v ) DMSO . MOCK-infected Huh-7 . 5 and IFN-α2A ( 500 U/mL ) were used as positive controls , and the 0 . 5% DMSO vehicle was used as negative control . Four sets of 15 384-well plates ( 13 plates designated for test compounds and 2 plates designated for DMSO vehicle control ) were used for the primary screening of the BioFocus kinase inhibitor library against each dengue serotype . Each of the 4 , 000 compounds in the library was tested in single wells . For data normalization and quality control of the screening , each test compound plate contained 16 replicates of the positive and negative controls . After dispensing the test compounds IFN-α2A and DMSO vehicle in the 384-well plates , Huh-7 . 5 was mixed with DENV1 , DENV2 , DENV3 , or DENV4 to achieve a M . O . I . of 0 . 5 and dispensed at 5×103 cells/well using the Thermo Scientific WellMate automated liquid dispenser . For the MOCK-infected Huh-7 . 5 , the cells were mixed with VM and dispensed under the same conditions as previously stated . Virus infection in the presence of the compounds proceeded at 37°C , 5% CO2 for 96 hrs . Compound activity based on percent inhibition and cell toxicity was assessed by IFA and IM analysis as described above . Scatter-plot distribution of the entire screening was generated using TIBCO Spotfire 4 . 5 . 0 ( TIBCO Software Inc . , Somerville , MA ) . The calculated activity was normalized to a percent inhibition ( PI ) based on the MOCK-infected cells ( 100% activity , or zero infection ) and dengue-infected cells ( 0% activity , or maximum measured infection percentage ) controls according to the formula:where: PImeasured - percent inhibition of test compound μPIDENV-infect - average percent inhibition readout of dengue-infected control μPIMOCK-infect - average percent inhibition readout of non-infected control Similarly , the percent cell number was normalized based on the measured cell number in MOCK-infected ( 100% cell number ) controls according to the formula: % cell number = ( Cmeasured/μCMOCK-infect ) ×100% , where Cmeasured is the measured cell number in the test well and μCMOCK-infect is the average cell number in the MOCK-infected controls . The statistical validity of the dengue high-throughput screening was determined by calculating for the Z'-factor using the 0 . 5% DMSO-treatment and MOCK-infected Huh-7 . 5 as negative and positive controls , respectively . In addition , other parameters , including DRC of a reference compound , were used to evaluate the assay performance . For the primary screening a Z'-factor ≥0 . 5 and a coefficient of variation ( CV ) among the controls ≤10% was used to validate the results of the assay . The hit ( i . e . a compound that demonstrates inhibition of infection ) selection criteria for the primary screening was set at ≥80% inhibition of dengue viral infection in at least 1 dengue serotype and with the corresponding percent cell number at ≥50% . The hits identified from the primary screening were tested at 10 µM for inhibition of dengue virus infection in C6/36 . Briefly , cells were inoculated with DENV1∼4 at an M . O . I . of 0 . 5 and seeded in 384-well plates spotted with the reference and primary hit compounds and incubated for 96 hrs at 28°C . Detection of dengue-infected cells by IFA and image acquisition using Evotec Technologies High-Throughput Cell Analyzer Opera was carried out following the method described above . Compound activity was determined by measuring percent inhibition and percent cell toxicity using the IM platform as previously described . The hits identified from the primary screening were tested at 10 µM against HCV genotype 2a ( JFH-1 ) infection in Huh-7 . 5 using an in vitro HCV cell culture system ( HCVcc ) . Cells were seeded in 384-well plates and cultured under humidified conditions at 37°C for 24 hrs . Reference and primary hit compounds were added , followed by inoculation with HCV at an M . O . I . of 1 and incubated for another 72 hrs at 37°C . HCV-infected cells were identified by detection of NS5A-GFP expression using ImageXpress Ultra ( Molecular Devices , USA ) and analysis using the IM platform as previously described . Compounds resulting in ≥50% inhibition of HCV genotype 2a infection and percent cell number ≥50% were considered as positive hits for anti-HCV activity . The hits identified from the primary screening were also tested at 10 µM against CHIKV-118-GFP infection in HuH-7 cells and evaluated by resazurin reduction assay ( RRA ) . Resazurin ( 7-Hydroxy-3H-phenoxazin-3-one 10-oxide ) is reduced to the red fluorescent resorufin by redox enzymes produced by viable cells , and is a good indication of metabolic capacity , and by extension cell viability . The amount of converted resorufin was measured as relative fluorescence readout ( RFU ) at excitation/emission of 531/572 nm using a fluorescence spectrophotometer ( Victor3 V Spectrophotometer , Perkin Elmer , USA ) . Briefly , cells were inoculated with CHIKV-118-GFP at an M . O . I . of 0 . 5 and seeded in 384-well plates containing reference and primary hit compounds and incubated under humidified conditions for 72 hrs at 37°C . Resazurin solution was added to a final concentration of 10 µM and further incubated for another 12 hrs prior to measurement of RFU . The percent activity of the compounds , reflected by the percent cell viability , was quantified by normalizing against the RFUs of MOCK-infected cells and CHIKV-118-GFP-infected cells . Compounds resulting in normalized RFU ≥70% were considered as positive hits for anti-CHIKV activity . To confirm the compound activity against dengue viruses , the selected hits from the primary screening were tested in a 10-pt . DRC ( 2-fold serial dilution from 50 µM ) using the same assay described for the dengue HT/HCA . Each concentration of the hit compounds was tested in duplicate wells . Data generated from image analysis of the 10-pt . DRC was plotted and analyzed using the non-linear regression formula: log ( inhibitor ) vs . response – variable response ( 4 parameters ) in GraphPad Prism 5 . 04 . The EC50 value , defined as the effective concentration resulting in a 50% inhibition of DENV infection , was used to evaluate compound activity . Compound toxicity was determined by testing the hit compounds in a 10-pt . DRC against Huh-7 . 5 in the absence of viral infection and measuring the cell viability using resazurin reduction assay as described above . The CC50 value , defined as the compound concentration resulting in a 50% reduction in cell viability ( based on normalized RFU values ) compared with the MOCK-infection , was used to evaluate cell toxicity . Confirmed hits were selected based on their Selectivity Index ( SI ) , a dimensionless value that indicates the magnitude between cytotoxic concentration and effective concentration , and is calculated as: SI = CC50/EC50 . Cluster analysis was done using a molecule-clustering module from Pipeline Pilot ( Accelrys Software Inc . , San Diego , CA , USA ) . The active scaffolds of compounds confirmed to have anti-dengue activity through dose response curves were selected for structural analysis . Structural relationship among the hit compounds was analyzed using the Tanimoto coefficient structural similarity [27] .
Several phases were involved in developing the image-based dengue high-throughput/high-content assay ( HT/HCA ) . A schematic workflow diagram of the assay development and assay method is shown in Figure S1 . The first phase involved miniaturization of the assay to the 384-well plate format , including host cell seeding density and viral infection conditions . Selection criteria for the appropriate cell seeding density was included having a sufficiently high number cells but with enough spatial distribution for proper identification and accurate segmentation by the IM platform plug-in . After testing various seeding densities of Huh-7 . 5 , the seeding density of 5×103 cells per well was selected ( data not shown ) . DENV infection in Huh-7 . 5 was visualized by immunofluorescence assay ( IFA ) detection of the dengue E protein using the 4G2 mAb and confocal imaging using the Evotec Technologies High-Throughput Cell Analyzer Opera . For the DENV infection , a M . O . I . of 0 . 5 and incubation time of 96 hrs was used since it allows for multiple rounds of virus replication and facilitates the screening of active compounds that target different stages of the dengue virus life cycle ( Figure S2 ) . A flowchart of the image analyses is shown in Figure 1 . Defining the cell nuclei was done as follows ( Figure 1A–D ) : after applying a Gaussian low pass filter [28] with relatively high sigma value ( to the nucleus size ) on the DAPI channel ( Figure 1A ) , the local maxima ( Figure 1B ) were subsequently located . A k-means clustering method [29] was then utilized to separate the background and foreground to obtain the nuclei mask image ( Figure 1C ) and the local maxima located in the background were removed , leaving the remaining maxima as those representing the number of cells in the image . Starting from the local maxima constrained by the nuclei mask , along with the slightly blurred nucleus image as a distance map , the region for single nuclei were defined ( Figure 1D ) with the watershed method [25] . Identifying the dengue-infected cells and the percentage of non-infected cells were done as follows ( Figure 1D–I ) : from the image obtained from the A488-channel ( Figure 1E ) , a weight map was calculated based on the edge features of this channel ( Figure 1F ) . After applying an open-by-reconstruction operator and Gaussian low-pass filter to alleviate the noise , a foreground mask was attained ( Figure 1G ) . Starting from the separated nuclei borders ( Figure 1D ) constrained by the foreground mask , and along with the weight map , the region of the signals were defined and marked with four different colors ( Figure 1H ) , delineating the borders of the cells . Finally , the dengue-infected cells are identified as those having an A488 signal within the defined cell borders above a pre-defined threshold level , and are delineated by blue line segments ( Figure 1I ) . The first assay validation evaluated the Z'-factors for DENV1-4 infection of Huh-7 . 5 in the 384-well plate format . Figure 2A shows a representation of the validation process done for the DENV2 HT/HCA . Cells , virus , and a reference control were dispensed in 384-well following a designed template pattern ( upper left panel ) . After the viral infection period and IFA , IM analyses of the acquired images revealed the infection percentage , cell number based on nuclei detection and other pre-defined parameters . An IM analysis showing the relative percentage of DENV2 infection is represented by a generated heat map ( lower left panel ) . The Z'-factor was calculated using the average and standard deviations of the percent infection of the positive and negative controls ( right panel ) . MOCK-infected Huh-7 . 5 was designated as positive control while the DENV-infected Huh-7 . 5 was used as the infection control . All wells contained 0 . 5% DMSO vehicle to simulate the culture conditions used in the screening . The calculated Z'-factors for the DENV1 , DENV2 , DENV3 , and DENV4 HT/HCA in 384-well plates showed a range between 0 . 50 and 0 . 75 ( Figure S3 ) . According to Zhang et al . [26] a Z'-factor ≥0 . 5 indicates a statistically reliable separation between positive and negative controls . The second assay validation tested a panel of reference compounds previously reported to have antiviral properties against different strains of DENV2 . This panel includes: AZ10417808 , BIBU1316 , MPP , LE 135 [30] , Ribavirin [31] , Chloroquine [32] and IFN-α2A [33] . MOCK-infection and 0 . 5% DMSO were used as positive and negative controls , respectively . The compounds' antiviral activities and cell toxicities against BR DEN2 01-01 infection of Huh-7 . 5 were determined by DRC . Figures 2B shows the generated heat map for percent DENV2-infected cells and percent cell viability . Figure 2C shows the DRC of the reference panel , with the percent infection normalized against DENV2-infected Huh-7 . 5 and percent cell viability normalized against MOCK-infected Huh-7 . 5 . It was observed that all compounds in the reference panel showed inhibition of DENV2 infection in a dose-dependent manner . At very low concentration of the reference compounds , the percent cell viability of DENV2-infected cells did not exceed 75% compared with the MOCK-infected cells , as a consequence of DENV2-associated cytopathic effect . The resulting EC50 of the reference compounds against the DENV2 infection of Huh-7 . 5 using our dengue HT/HCA varied from those previously reported . Furthermore , most of the compounds in our reference panel exhibited significant cell toxicities at EC50 compared with the MOCK-infected and DENV2-infected controls . Conversely , IFN-α2A concentration ≥EC50 resulted in higher cell numbers compared with MOCK-infected Huh-7 . 5 . Discrepancies between the EC50 of the reference compounds obtained in this study with the previous reports may be attributed to factors such as intrinsic differences between the DENV2 strains and the type of host cell used . Nonetheless , the results of the assay validation demonstrate the statistical reliability of our developed dengue HT/HCA . None of the compounds in the reference compound panel exhibited the ideal EC50 and CC50 values for use in the dengue HT/HCA . While IFN-α2A has shown strong antiviral properties against dengue infection , having a multi-target mode of action restricts its application as a reference drug . Based on these observations , MOCK-infection and IFN-α2A were used as positive controls for the screening of the compound library , but only MOCK-infection was used for calculating Z'-factors and validating the reliability of the entire screening process . The compounds screened with our dengue HT/HCA is a subset of 4 , 000 small molecules belonging to the BioFocus kinase inhibitor library of chemical compounds designed to interact with one of the seven representative subsets of kinases according to protein conformations and ligand binding modes [34] . The library was screened at 10 µM against DENV1 , DENV2 , DENV3 and DENV4 , and primary hits were selected based on the criteria: ≥80% activity and ≥50% cell number ( Figure 3 ) . The 50% cell number threshold was chosen to allow a wider range of compounds that are slightly cytotoxic at 10 µM , but may still be active at lower concentrations , to be selected . Primary hits were selected for activity against at least 1 dengue serotype . Out of the 4 , 000 small molecules screened , 157 compounds qualified for further confirmation and counter-screening , giving a hit rate of 3 . 9% . The primary hits were selected according to activity ( ≥80% inhibition ) , irrespective of their cytotoxicity levels . Among the 157 primary hits , 40 compounds ( 25 . 5% ) showed inhibition of all 4 serotypes , 19 ( 12 . 1% ) against 3 serotypes , 30 ( 19 . 1% ) against 2 serotypes , and 68 ( 43 . 3% ) against 1 serotype . The inhibitory properties of the dengue primary hits were further investigated by testing these compounds at 10 µM against DENV infection of C6/36 , HCV genotype 2a infection of Huh-7 . 5 , and CHIKV-118-GFP infection of HuH-7 . The activity profile of these dengue primary hits is summarized in Figure S4 . Thirty-nine of the dengue primary hits ( 24 . 8% ) exhibited ≥50% inhibition against at least 1 DENV serotype in the C6/36 host , suggesting that the targets of these compounds are required for successful DENV infection in both human and insect host cells . It is important to note that even though the other 118 dengue primary hits ( 75 . 1% ) did not inhibit DENV infection in C6/36 at the same concentration , the putative role of their targets in DENV infection in the insect cells have not been ruled out . Conversely , 103 dengue primary hits ( 65 . 6% ) showed ≥50% inhibition of HCV genotype 2a infection of Huh-7 . 5 at 10 µM , with only 19 hits exhibiting <50% cell number in the host cell . In contrast to the high number of overlapping hits between DENV and HCV genotype 2a , only 9 ( 5 . 7% ) of the dengue primary hits exhibited detectable activity against CHIKV-118-GFP in the resazurin reduction assay . These hits had low antiviral activity , and were excluded after conducting DRC analysis ( data not shown ) . The activities of the 157 primary hits were confirmed by 10 pt . DRC against DENV1 , DENV2 , DENV3 and DENV4 infection in Huh-7 . 5 . Cluster analysis of the top 53 compounds exhibiting the lowest EC50 values were performed using a molecule-clustering module from Pipeline Pilot yielded 4 enriched clusters plus singletons . Core structures of the two scaffolds were heterocyclic ring of imidazopyridine and the other two scaffolds were thiazole-based compounds . One of the thiazole scaffold clusters , consisting of 11 compounds , had 4- ( 1-aminoethyl ) -N-methylthiazol-2-amine as a common core structure . The profile of these compounds ( EC50 , CC50 and Selectivity Index ) against the four dengue serotypes and their chemical structures are shown in Table 1 and Figure 4 , respectively . The compounds showing a wide spectrum of anti-dengue activity against all four serotypes have only pyridine or pyrimidine ring by amine linkage to the core scaffold and addition of substituents on the ring narrows the spectrum of activity , especially against DENV4 . In addition , 9 out of 10 compounds in this cluster have an extra carbon next to aminoethyl linkage at 4th position of thiazole , followed by a phenyl group and trifluoro- , methoxy- , amine or chloride substituents on para position of the phenyl group .
From the time of its re-emergence 60 years ago , dengue has spread across the globe , placing nearly 40% of the world's population at risk of infection . Coincidentally , the geographical distribution of the four serotypes has also expanded , with all serotypes reported to co-circulate in most of the dengue-endemic countries [7] . This has serious implications in the rise of DHF/DSS cases , as it is presently understood that antibody-dependent enhancement combined with elevated cytokine responses resulting from subsequent infection with a heterologous serotype are involved in disease severity [35] . The most advanced dengue vaccine candidates try to address this issue by constructing chimeric YF/DENV virus ( ChimeriVax-DEN ) , incorporating the prM and E genes of each DENV serotype in the yellow fever ( YF ) 17D backbone , and used these to prepare tetravalent cocktails [36] . However , while the tetravalent ChimeriVax-DEN vaccine has shown good immunological responses in clinical trials [17] , its long-term safety and efficacy has not been fully established . In contrast to the dengue vaccine approach , therapeutic drug approach circumvents the immunopathological complication of dengue , and directly addresses the acute viral infection . The work reported here describes the development of a high-throughput/high-content assay screening for potential anti-dengue drugs using image-based quantitation of inhibition of dengue virus infection in vitro as a measure of antiviral activity . This dengue HT/HCA was used to screen 4 , 000 small molecules from the BioFocus kinase inhibitor library against DENV1-4 , and revealed a number of compounds that inhibit more than 80% infection of all 4 serotypes of dengue in vitro . More than 60% of these compounds were also found to inhibit more than 80% infection of HCV genotype 2a infection . Interestingly , most of the compounds belonging to the 4- ( 1-aminoethyl ) -N-methylthiazol-2-amine cluster exhibited measurable antiviral activities against dengue viruses in Huh-7 . 5 , but did not demonstrate strong inhibition of HCV genotype 2a infection in the same host cells . Recently , high throughput assays ( HTA ) have been used to find several drug candidates with anti-dengue properties [37] . Structure-based dengue virtual screening ( i . e . in silico high-throughput screening or HTS ) is a target-based HTA that analyzes the binding potential of chemical compounds against a target dengue viral protein . Using combinatorial libraries and docking programs that predict the chemical interactions , binding potentials of the compounds with known crystal structures and associated ligands of the target protein are computed . This approach has been used extensively in discovering potential inhibitors of DENV E protein binding and fusion [38] , [39] NS5 2′O-Methyltransferase [40] , NS3 protease [41] , [42] and its complex , NS2B/NS3protease ( NS2B/NS3pro ) [43] . Another target-based HTS approach using enzymatic assay has identified BP2109 as a potential inhibitor of NS2B/NS3pro complex [44] . One drawback of in silico-based HTS is that predicted chemical interactions do not take into account other biological factors . This often results in selection of compounds with high binding properties in silico , but weak activities once tested in vitro . Similarly , target-based enzymatic assays are performed in a highly controlled environment that facilitates optimum enzymatic function of the target , which can be dramatically different from its biological setting . Thus , compounds found to be highly active against the target using this approach may not exhibit the same effect when tested using a cell-based assay , since the assays do not factor in cellular uptake , availability of the target , and other environmental conditions [45] . The in silico and target-based HTA approaches are designed to find active compounds against a specific target . While this helps to simplify the process of identifying the probable mechanism of action , it is inherent in the assays to exclude compounds that may be active against other targets involved in viral infection . Hence , their application to the comprehensive screening of active compounds against viral infection is limited . In contrast to target-based HTA , cell-based HTA is more robust as it covers a wider aspect of the viral infection process . This type of HTA uses either infectious viruses to follow one or multiple rounds of viral infection , or viral replicons to observe the events surrounding viral replication . Cell-based dengue HTA takes advantage of the various intracellular and intercellular events that occur during viral infection . Hence , antiviral properties can be attributed to either compound activity against viral or cellular targets . Cell-based flavivirus immunodetection ( CFI ) , which measures viral protein expression after infection by ELISA , and luciferase reporter viral replicon assay were used to identify inhibitors of viral RNA synthesis namely , the adenosine nucleoside inhibitor NITD008 [14] , NS4B inhibitor NITD-618 [46] and NITD-982 , an inhibitor of host dihydroorotate dehydrogenase ( DHODH ) [47] . A modified type of the viral replicon assay using dengue-1 virus-like particles ( DENV1-VLP ) assembled by packaging the dengue viral replicon tagged with a Renilla luciferase 2A reporter gene ( Rluc2A ) in DENV1 structural proteins generated using the Semliki Forest Virus ( SFV ) expression system was reported to be useful in identifying inhibitors of dengue viral entry , translation and replication [48] . In addition to the expression of viral proteins during infection or replication , other indications of viral infection can be used to assess compound activity such as cell death and reduced metabolic activity . A dengue cytopathic effect ( CPE ) -based HTA that uses luminescence assay to determine cellular viability by measuring cellular ATP was previously reported [49] . These cell-based HTA are described as “single-readout” assays , since a single value is generated during the assay corresponding to the effect of a particular treatment . Image-based high-content assay ( HCA ) is also a form of cell-based assay . Unlike CFI and replicon-based luciferase reporter assays however , it requires more sophisticated equipment like a high-throughput confocal microscope for acquiring images and special software for analyzing image data . When adapting image-based HCA for high-throughput screening , it is more labor intensive and requires more stringent criteria for data analysis . Nonetheless , image-based HCA has one clear advantage over single-readout assays – the amount of information that can be generated from images of a single treatment is not limited to a single value . Aside from the degree of viral infection and cell viability , other interesting information can be extracted from images such as morphological changes in host cell , protein localization , among others [50] . Like other cell-based HTA , image-based assay can be used to screen compounds with diverse modes of activity . This was demonstrated in an image-based HCA screening of 5 , 362 compounds with diverse chemical structures against DENV2 , revealing 73 active compounds , most of which have previously characterized cellular interactions [30] . Dasatinib , a c-Src kinase inhibitor that disrupts the assembly of dengue virions in virus-induced membranous replication complexes , was also identified after screening a kinase inhibitor library using image-based HCA [51] . The image-based dengue HT/HCA developed in this study was used to screen 4 , 000 compounds belonging to the BioFocus kinase inhibitor library . Primary screening against all four dengue serotypes required 16 , 000 experiment points ( 4 , 000 compounds×4 dengue serotypes ) , excluding the positive and negative controls . The entire primary screening took 11 days in total: 3 days for expansion of Huh-7 . 5 from a single T175 tissue culture ( TC ) flask to 8 T175 TC flasks , 4 days for host cell plating and virus infection in the 384-well plates containing the compounds , 2 days for image acquisition , 1 day for image analysis using the IM platform , and 1 day for data analysis . Two previous image-based screening campaigns for dengue antivirals were conducted with lab-adapted DENV2 ( New Guinea C ) whole virus [30] , [51] . One of these campaigns [51] further investigated the hit compounds by testing the antiviral activities against other lab-adapted dengue serotypes . Our image-based dengue HT/HCA screening campaign differs from the previous image-based HTA in three aspects: First , we used a novel target-focused chemical library ( BioFocus kinase inhibitor library ) whose collection of small molecules has not been thoroughly screened and characterized . Second , the compounds were screened against low passage strains of field isolated dengue viruses ( with the exception of DENV4 tvp360 ) , which allows the identification of compounds that may be active towards prevalent strains . Third , the entire 4 , 000 compound subset of the BioFocus kinase inhibitor library was screened against all four dengue serotypes . By screening all the compounds of the library against the four dengue serotypes , we can identify novel compounds that may be active against all four serotypes or specific toward any of the serotypes . Such findings can have biological implications on the differences between the viral infection process of the 4 serotypes at the cellular and molecular level . This offers an advantage over the primary screening using the DENV2 serotype and subsequent confirmation of activity with the other serotypes since the latter is already biased towards compounds active against DENV2 , resulting in the “loss” of potential hit compounds that do not inhibit this particular serotype . Screening of the BioFocus kinase inhibitor library using our image-based dengue HT/HCA resulted in the identification of 4 major clusters exhibiting inhibitory properties against dengue virus infection in vitro . Among them , one cluster consisting of 11 compounds having a 2-aminothiazole as a core scaffold , showed antiviral activities of varying degrees against the infection of DENV1 , DENV2 , DENV3 , DENV4 in the human hepatoma cell line Huh-7 . 5 . The inability of these compounds to inhibit dengue infection in C6/36 initially suggests that the target is most likely a factor involving dengue virus infection in human cells . However , this discrepancy may also be attributed to other factors , such as difference in membrane permeability between the two different host cells or molecule uptake of the compounds into the host cell . Such differences in the physiology between the human and insect cells may affect the efficacy of these compounds in inhibiting dengue viral infection , but has not been thoroughly investigated in this study . Interestingly , none of the hit compounds from the 2-aminothiazole cluster significantly inhibited the infection of HCV and CHIKV in hepatoma cells , suggesting that the inhibitory property is more specifically directed towards dengue virus infection . One major drawback when using cell-based assays in high-throughput screening is the effect of toxicity to the host cells , and by extension , viral infection . Compound toxicity can have a profound effect in the viral infection process , and may lead to inaccurate assessment of the antiviral activity . Since the HTS is conducted using only a single concentration of the compounds , it is impossible to avoid encountering those that exhibit moderate to high level of toxicity . Hence , a confirmatory assay that tests a range of concentration is necessary to verify if these hit compounds indeed have antiviral activities . In addition , secondary assays are used to confirm compound activity and predict the mechanism of action . For cell-based assays that utilize image-based technology , determining compound toxicity with high certainty is more difficult . In the absence of biological markers that detect mitochondrial activity , cell apoptosis , cell starvation , the only indicator of compound toxicity is the relative cell number compared with non-treated controls . This can be misleading if the toxicity does not result in abolition of the cells or degradation of the cell nuclei since the cell number will not reflect the actual number of viable cells . Thus , it is essential to confirm compound toxicity by measuring production of ATP or relative oxygen species as an indicator of cell viability [52] . Time-of-addition assay , a strategy to determine the stage of inhibition during the viral infection cycle , has been used to characterize the mode of action of some inhibitors of dengue viral entry ( NITD Compound 6 ) , viral replication ( NITD-982 ) and early translation ( NITD-2636 ) [39] , [47] , [53] . The inhibitor is added at different time points during viral infection and monitored for expression of the viral proteins , replication of the genome or production of infectious progeny virions . Among the 2-aminothiazole hit compounds identified in this study , CND1203 exhibited a strong antiviral activity against all 4 dengue serotypes , and blocked the formation of dengue virus plaques in Huh-7 . 5 at 25 µM in the plaque reduction assay ( Figure 5 ) . Compound CND1203 was used for the time-of-addition assay , adding 25 µM at different time points ( −2 hpi , 0 hpi , 0 . 5 hpi , 1 hpi , 2 hpi , 4 hpi ) of the dengue virus infection ( M . O . I . 5 ) in Huh-7 . 5 . In contrast to the strong inhibition of compound CND1203 against dengue viruses in the plaque reduction assay , none of the treatments inhibited dengue infection in the time-of-addition assay , suggesting that the compound does not interfere with viral entry ( data not shown ) . Aminothiazole-based compounds have previously been implicated in the inhibition of HCV replication by binding to an allosteric site on the viral polymerase [54] . However , the structure of these active anti-HCV compounds differ from the 2-aminothiazole hit compounds reported in this study in terms of the substitutions on the scaffold . As a consequence , it is unlikely that our hit compounds interact with the viral polymerase . Although compound CND1203 did not inhibit DENV entry , its ability to inhibit the spread of DENV infection and formation of virus plaques in Huh-7 . 5 clearly suggest that the mode of action is at the post-entry stages . Two kinase inhibitors were previously reported to block virus assembly of dengue virions: Dasatinib , a thiazolyaminopyrimidine that inhibit c-Src protein kinase , did not interfere with dengue RNA replication , but disrupted the proper assembly of dengue virions within virus-induced cell membranous replication complex [51] . SFV785 , a trifluorinated N-methylanaline derivative that selectively inhibits NTRK1 and MAPKAPK5 kinase activity , altered the distribution of structural envelope protein from the reticulate network to enlarged discrete vesicles , consequently affecting the co-localization with the DENV replication complex and disrupting the assembly of progeny virions [55] . Interestingly , the 2-aminothiazole hit compounds identified in this study shares the aminothiazole moiety of Dasatinib , as well as the rings on each of the molecule . Based on the structural similarity with Dasatinib , c-Src kinase may be a candidate target of the 2-aminothiazole hit compounds , which would imply a post-genomic replication mode of action . Further investigation is necessary to support this hypothesis . The persistence of dengue outbreaks around the world , and the lack of an available dengue vaccine reinforce the need to find and develop therapeutic drugs to address this major health concern . The use of HT/HCA screening technologies can expedite the drug discovery of potential dengue antivirals by facilitating the screening of large chemical libraries . The work reported here features an innovative image-based HT/HCA system that can be used as a reliable tool in screening for antiviral compounds against all four DENV serotypes . Furthermore , the compounds identified in the present study can serve as a potential starting point for the development of dengue antivirals .
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Dengue , a re-emergent human disease that places nearly half of the world's population at risk , threatens to further expand in geographical distribution . The lack of an available effective dengue vaccine has encouraged the search for antiviral drugs as an alternative approach . In recent years , drug discovery through high-throughput screening has become a trend in the search for dengue antivirals . In this study , we developed an image-based dengue high-throughput/high-content assay using prevalent viral strains of three dengue serotypes ( DENV1 , DENV2 and DENV3 ) isolated from dengue outbreaks in South America and a laboratory-adapted strain of DENV4 . We demonstrated the usefulness of our image-based dengue HT/HCA in identifying potential dengue antivirals by screening a small subset of chemical compounds for inhibition of dengue virus infection in a human-derived host cell line ( Huh-7 . 5 ) , and partially characterized their activities against dengue infection in a mosquito host cell line ( C6/36 ) , a distantly-related virus ( hepatitis C virus ) , and an unrelated virus that is transmitted by the same mosquito vector ( chikungunya virus ) .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"biotechnology",
"virology",
"biology",
"microbiology",
"drug",
"discovery"
] |
2013
|
High Content Screening of a Kinase-Focused Library Reveals Compounds Broadly-Active against Dengue Viruses
|
Reaction networks are systems in which the populations of a finite number of species evolve through predefined interactions . Such networks are found as modeling tools in many biological disciplines such as biochemistry , ecology , epidemiology , immunology , systems biology and synthetic biology . It is now well-established that , for small population sizes , stochastic models for biochemical reaction networks are necessary to capture randomness in the interactions . The tools for analyzing such models , however , still lag far behind their deterministic counterparts . In this paper , we bridge this gap by developing a constructive framework for examining the long-term behavior and stability properties of the reaction dynamics in a stochastic setting . In particular , we address the problems of determining ergodicity of the reaction dynamics , which is analogous to having a globally attracting fixed point for deterministic dynamics . We also examine when the statistical moments of the underlying process remain bounded with time and when they converge to their steady state values . The framework we develop relies on a blend of ideas from probability theory , linear algebra and optimization theory . We demonstrate that the stability properties of a wide class of biological networks can be assessed from our sufficient theoretical conditions that can be recast as efficient and scalable linear programs , well-known for their tractability . It is notably shown that the computational complexity is often linear in the number of species . We illustrate the validity , the efficiency and the wide applicability of our results on several reaction networks arising in biochemistry , systems biology , epidemiology and ecology . The biological implications of the results as well as an example of a non-ergodic biological network are also discussed .
Let us now formally describe reaction networks . Motivated by the literature on chemical kinetics , we refer to the network participants as molecules which may belong to one of species . There are reactions in the network and for any , the stoichiometric vector denotes the change in the number of molecules in each of the species due to the -th reaction . Consider the deterministic model for the reaction network described above . In this setting , the state of the system is described by a vector of concentrations of the species which we denote by . The concentration of a species is simply its molecular count divided by the system volume . Let be the flux associated with the -th reaction ( see [8] ) . To ensure positivity of the system , we require that whenever and . If the initial state is , then the evolution of concentrations is given by which satisfies the Reaction Rate Equations ( RRE ) of the form ( 1 ) We are interested in the long-term behavior and stability of our reaction dynamics . More precisely , we would like to check if the following conditions are satisfied . DC1 For any , there is a compact set such that for all . DC2 There exists a compact set such that for any , we have for large values of . DC3 There is a such that for any we have as . The first condition , DC1 , says that for any , the entire trajectory stays within some compact set . We would expect this to be true for most realistic systems . Hence a violation of this property may suggest a flaw in the deterministic model . The second condition , DC2 , says that there is an attractor set for the dynamics , where all the trajectories eventually lie , irrespective of their starting point . The last condition , DC3 , says that there is a globally attracting fixed point for the deterministic model . Using techniques from the theory of dynamical systems [30] , [46] , one can verify these conditions , without the need of simulating the deterministic model . There is also a general theory to check condition DC3 for reaction networks satisfying mass-action kinetics ( see [47]–[50] ) . Broadly speaking , these three conditions present different ways of saying that the reaction dynamics is “well-behaved” . Our goal in this paper is to develop a theoretical and computational framework for verifying conditions similar to DC1 , DC2 and DC3 for stochastic models of reaction networks . Consider the stochastic model corresponding to the reaction network described above . In this setting , the firing of reactions are discrete events and the state of the system refers to the vector of molecular counts of the species . When the state is , the -th reaction fires after a random time which is exponentially distributed with rate . The functions are known as the propensity functions in the literature . To ensure positivity of the system , we require that if , then , where is the set of non-negative integers . The dynamics can be represented by the Markov process where is the initial state . Note that if , then is the number of molecules of at time . It is important to select a suitable state space for the Markov process representing the reaction dynamics . We choose to be a non-empty subset of satisfying the following properties: Observe that part ( A ) ensures that if then for all and hence can be taken to be the state space of all the Markov processes describing the stochastic reaction network with an initial state in . Part ( B ) implies that the reaction dynamics cannot be contained in a proper subset of . The role of this assumption will become clear in the next section , when we discuss the issue of state space irreducibility . Note that in certain cases , such as the pure-birth network , a suitable state space satisfying the above criteria cannot be found . There also exist cases where the above criteria restricts the choice of state space . For example , for the pure-death network , the only possible choice for state space is . Finally we remark that if the reactions in a network satisfy a conservation relation then the state space must be chosen with an initial condition in mind . For example , for the network , the sum of molecular counts of and is preserved by the reactions . Hence if we wish to study the stochastic dynamics with the initial sum as , then the correct choice for state space is . Let denote the space of probability distributions over , endowed with the weak topology which is metrized by the Prohorov metric ( see [51] ) . For any let denote the following probability ( 2 ) Defining , for any , we can view as an element in . In fact , is the distribution at time of the Markov process . The dynamics of is given by the Chemical Master Equation ( CME ) which has the following form: ( 3 ) where if and for all . Theoretically , one can find for any and , by solving this system . However this system consists of as many equations as the number of elements in . Hence an explicit solution is only possible when is finite , which only happens in very restrictive cases where all the reactions preserve some conservation relation . Typically , is infinite and solving this system analytically or even numerically is nearly impossible , except in some restrictive cases ( see [16] ) . From now on , we assume that is infinite . The above discussion shows that at the level of distributions , we can view the stochastic dynamics as the deterministic dynamics , which satisfies the CME . However , the major difficulty in analyzing this deterministic dynamics is that it occurs over an infinite dimensional space . Nevertheless we can recast the conditions DC1 , DC2 and DC3 in the stochastic setting as below . SC1 For any , there is a compact set such that for all . SC2 There exists a compact set such that for any we have for large values of . SC3 There is a such that for any we have as . Each of the above conditions give an important insight about the long-term behavior and stability of the stochastic dynamics . The first condition , SC1 , says that for every we can find a finite set such that each puts at least of its mass in . In other words , the probability that the state of the underlying Markov process at any time is inside is greater than . We would expect this to be true for most realistic models . If condition SC2 holds then the evolution of distributions have a compact attractor set in , where all the trajectories eventually lie irrespective of their starting point . This suggests that in the long run , the family of processes , spend most of their time on the same set of states . The last condition SC3 says that the evolution of distributions have a globally attracting fixed point . If this holds , then the Markov process representing the reaction dynamics is ergodic with as the unique stationary distribution . For understanding the long-term behavior of a stochastic process , ergodicity is a desirable property to have . In the long-run , the proportion of time spent by any trajectory of an ergodic process , in any subset of the state space is equal to the stationary probability of that subset ( see ( 12 ) ) . In other words , information about the stationary distribution can be obtained by observing just one trajectory for a sufficiently long time . Such a result can have important applications . For example , consider a culture with a large number of identical cells with each cell having the same reaction network . If we can show that this intracellular network is ergodic , then by observing the long-term reaction dynamics in a single cell , using for example . time-lapse microscopy , we can obtain statistical information about all the cells at stationarity . Conversely , ergodicity allows us to obtain the stationary distribution of a single-cell by observing the distribution over the population , using for example flow cytometry . In this paper we develop a general framework for checking conditions SC1 , SC2 and SC3 . However , the scope of our paper is broader than that . As mentioned in the introduction , we obtain easily computable bounds for the statistical moments of the underlying Markov process and investigate when these moments converge with time . We also present conditions for the distribution of the process to be light-tailed .
In this section we discuss the main results of our paper . In particular , we explain how conditions SC1 , SC2 and SC3 can be verified without having to simulate the trajectories of the Markov process representing the reaction dynamics . Intuitively , these conditions can only hold if the Markov process has a low probability of hitting states that have a very large size . In our case , the states are vectors in and so we can measure their size by using any norm on . The central theme of this paper is to demonstrate that for many networks , long-term behavior can be easily analyzed by choosing the right norm for measuring the state sizes . This right norm has the form ( 4 ) where is a positive vector in satisfying the following condition . In this section , we formally present the main results of our paper . Their proofs are given in the Supplementary Material S1 .
Let us then consider a unimolecular reaction network which involves species that interact through reaction channels of the form: ( 15 ) where , , and . The reaction rates , and are positive real numbers . In accordance with ( 3 ) , the reactions are indexed from to , and corresponding propensities and stoichiometries are denoted by and , respectively . Similar results are now presented for stochastic bimolecular reaction networks which , in addition to the unimolecular reactions ( 15 ) , also involve bimolecular reactions of the form: ( 18 ) where , , , and . The reaction rates and are positive real numbers . In this section we illustrate that stochastic and deterministic models of the same reaction network may exhibit very different qualitative behaviors . Therefore assessing ergodicity or the convergence of moments of a stochastic model from the stability properties of the corresponding deterministic model is , in general , incorrect . To support this claim , we consider two reaction networks . The goal of this section is to compute a compact set that is attractive for the first-order moment of using the optimization problems ( 17 ) or ( 20 ) . Due to the moment closure problem [54] , analytical expressions for the steady-state values of the moments of bimolecular reaction networks are not available , and hence this is an important class of networks to analyze . Consider the following bimolecular reaction network ( 29 ) representing a dimerization process , i . e . dimerizes to . It is easily seen that this network is irreducible since any point in the state-space can be reached from any other point in a finite number of reactions having nonzero propensities . Choosing in , e . g . , yields that and , hence the network is exponentially ergodic , and all the moments are bounded and converging . On solving the optimization problem ( 20 ) with numerical values , , and , we get that which coincides with the theoretical value . One can regard to be an attractive compact set in which the first-order moments of eventually lie . To validate this calculation , Monte-Carlo simulations were performed which yield ( 30 ) showing the correctness of the attractive compact set . To further illustrate this result , several trajectories of and for different initial conditions are plotted in Figure 3 . We now discuss how the computation of an attractive compact set for the first-order moments can be used to assess whether a closure method leads to a result that is consistent with the stochastic dynamics . The idea is to check whether the closed system converges towards a value which lies within the compact set . Let us consider the reaction network ( 29 ) and close the first-order moments equations by neglecting the second order cumulant , i . e . neglecting the variance . By doing so , we get the model ( 31 ) where and are the approximate first-order moments of the system . The unique positive equilibrium point for this model is given by ( 32 ) where . With the same parameter values as before , we find that and and therefore for , showing that the state of the closed system converges to the boundary of the compact set . Note that SSA also predicts that the trajectories of the first-order moments converge to the boundary of this set . However the actual equilibrium values for the first-order moments of the stochastic dynamics are and , which differ from the ones obtained with the closure method . This discrepancy is expected since the variance has been neglected . This example shows how attractive compact sets for the moments can be used as a test for the moment-closure methods by checking whether the closed system predicts trajectories that converge inside those compact sets . However , note that in the current state , these compact sets can only be used to obtain a lower bound on the closure-error whenever the trajectories of the closed dynamics converge to a point outside the compact set . In such a case , the lower bound on the closure-error is simply given by the distance between the equilibrium point of the closed-system ( 33 ) where is the attractive ( convex ) compact set and is the equilibrium point of the closed dynamics . Let us consider the feedback loop network of Figure 4 represented by the reaction network ( 34 ) where is mRNA and is the corresponding protein . The dimer acts back on the gene expression through an arbitrary bounded nonnegative function . We have the following result: Result 16 For any positive values of the rate parameters and any bounded nonnegative function , the feedback loop with dimerization ( 34 ) is ergodic and all the moments are bounded and globally converging . Let us consider the stochastic switch of [63] described by the unimolecular stochastic reaction network ( 35 ) Above and represent mRNAs and proteins of gene , respectively . The functions and are arbitrary bounded nonnegative functions . We have the following result: Result 17 For any positive values of the rate parameters and any bounded nonnegative functions and , the stochastic switch ( 35 ) is ergodic and all the moments are bounded and globally converging . We consider here the stochastic repressilator of Figure 5 ( see also [42] ) involving genes . The reaction network corresponding to this -gene repressilator is given by ( 36 ) where , . Above , and are the mRNA and protein corresponding to gene . We have the following result: Result 18 For any positive values of the rate parameters and , the stochastic -gene repressilator ( 36 ) is ergodic and all the moments are bounded and globally converging . We consider here the following SIR-model , similar to the one in [64] , defined as ( 37 ) where birth and death reactions represent people entering and leaving the process , respectively . The only bimolecular reaction is the contamination reaction which turns one susceptible person into an infectious one . The two last reactions represent how infectious people are recovering and how recovered people become susceptible again . We then have the following result: Result 19 For any positive values of the rate parameters , the SIR-model ( 37 ) is ergodic and all the moments are bounded and globally converging . Let us consider the circadian oscillator of [65] , depicted in Figure 6 , which is a network involving 9 species and 18 reactions . Applying the developed theory on this model , we obtain the following result: Result 20 For any positive values of the rate parameters , the circadian clock model of [65] is ergodic and all the moments are bounded and globally converging . Using , for instance , the values of [65] and solving for the optimization problem ( 20 ) using linprog and Yalmip [66] , we find that and . Typical trajectories for the proteins A , R and C are depicted in Figure 7 where we can observe the expected oscillatory behavior . When averaging the populations of the proteins A , R and C over a population of 2000 cells , we obtain the sample-average trajectories depicted in Figure 8 . Convergence to stationary values is easily seen . Moreover , from the ergodicity property , we can even state that these fixed points for the sample-averages are globally attracting and that they coincide with the asymptotic time-average ( dashed lines ) . The steady-state average values for the proteins A , R and C are given by 222 . 1797 , 534 . 8853 and 549 . 7195 , respectively . Let us consider one of the oscillatory p53 models of [67] , which is described by the reactions ( 38 ) where is the number of p53 molecules , the number of precursor of Mdm2 molecules and the number of molecules of Mdm2 . The function implements a nonlinear feedback on the degradation rate of p53 . We have the following result: Result 21 For any positive values of the rate parameters , the oscillatory p53 model ( 38 ) is ergodic and all the moments are bounded and globally converging . We consider here the stochastic reaction network ( 39 ) which is an open analogue of the deterministic Lotka-Volterra system of [68] . The first set of reactions represent immigration , the second one reproduction , the third one competition due to overpopulation and the last one deaths/migrations . We obtain then the following result , which is a stochastic analogue of the results in [69] obtained in the deterministic setting: Theorem 22 Let us define and assume that one of the following conditions hold: Then , the stochastic reaction network ( 39 ) is ergodic and all the moments up to order are bounded and globally converging . In order to illustrate that the method can be applied to systems with more general mass-action kinetics , we consider the stochastic version of the well-known Schlögl model [70]: ( 40 ) where is the main molecule in the network . The above model is derived in the supplementary material S1 where we have assumed that the other molecular populations do not vary over time . Note that in the present form the model has an infinite state-space and involves a single trimolecular reaction . We then have the following result . Theorem 23 For any positive values of the rate parameters and any positive values for and , the Markov process describing the Schlögl model ( 40 ) is exponentially ergodic . Note , however , that we cannot say anything on the stability of the moments ( besides the fact that the first order-moment converges ) since the condition DD2 does not hold here due to the presence of a cubic term . Note that extending the condition DD2 to handle more general cases , such as this one , might be possible .
The central theme of this paper is to verify the ergodicity and moment boundedness of reaction networks in the stochastic setting . Note that even though we mainly consider mass-action kinetics in this paper , the framework also applies to more general kinetics described , for instance , by Hill functions ( see the examples on the repressilator and the stochastic switch ) and more general mass-action kinetics . These results have several interesting and important biological implications . For example , the ergodicity of a network shows that population-level information could be obtained by observing a single trajectory for a long time . Such an insight can be used to leverage different experimental techniques for a given application . For example , consider a clonal cell population with each cell having a gene-expression network that is ergodic . Then the stationary distribution ( at the population level ) of the species involved in this network can be ascertained by observing a single cell over time . In other words , to obtain stationary distributions one can either collect samples over time from a single cell ( e . g . using time-lapse microscopy ) or one can take a snapshot of the entire cell population at some fixed time ( e . g . using flow-cytometry ) . Due to ergodicity , both these approaches will yield the same information . Hence , far from being a technical condition , ergodicity can have far reaching experimental implications . As a property of a network , ergodicity also sheds important light on the long range behaviors that can be exhibited by that network . One may expect that most endogenous biochemical networks to be ergodic in order to achieve robustness with respect to variability in initial conditions and kinetic parameters , thus ensuring proper biological functions in spite of environmental disturbances . As also mentioned in the introduction , ergodicity is a non-trivial property which needs to be carefully established and cannot be generically assumed . To illustrate this , let us consider a simplified version of the model of carcinogenesis considered in [71] which is given by ( 41 ) where , . When , the trajectories of the species grow unbounded , as shown in Figure 9 , emphasizing then non-ergodicity of the model for this choice of parameters . The ideas we use for analysis can also be applied for rationally designing circuits in synthetic biology , where it is important that the network be ( structurally ) ergodic in order to ensure that the dynamics has the desired behavior irrespective of the initial conditions . Such a design is crucial because the initial conditions are usually unknown or difficult to control at certain times , e . g . after cell division or after the transfection of plasmids in the cell . Our results on boundedness and convergence of statistical moments enable verification of the suitability of a stochastic model and to characterize the properties of its steady-state distributions , even if such a distribution is not explicitly computable . One application of this is to provide justifications and insights for using moment closure techniques which have been extensively used to study stochastic chemical reaction networks . Some of these techniques [72] , [73] are based on manipulations of the moment generating function of the underlying stochastic process . The existence of this moment generating function is implicitly assumed in such techniques but it may not always hold , thereby jeopardizing the validity of the technique . In this article , we show that under certain conditions , the distribution of the stochastic process is uniformly light-tailed , which proves that the moment generating function exists for all time . Certain moment closure techniques ( see [74] , [75] ) prescribe ways to approximate higher order moments as a function of lower order moments . Such an approximation is , however , only reasonable if the higher order moments are bounded over time . This can be easily assessed with our approach and one can even quantify the error by explicitly computing the moment bounds as described in this article . Finally , the techniques developed here will prove invaluable for designing synthetic biological control systems and circuits whose objective is to steer the moments of the network of interest to a specific steady-state value . Until now , no theory has provided guidance for such a design . The specifics are outside the scope of this article and will be pursued elsewhere .
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In many biological disciplines , computational modeling of interaction networks is the key for understanding biological phenomena . Such networks are traditionally studied using deterministic models . However , it has been recently recognized that when the populations are small in size , the inherent random effects become significant and to incorporate them , a stochastic modeling paradigm is necessary . Hence , stochastic models of reaction networks have been broadly adopted and extensively used . Such models , for instance , form a cornerstone for studying heterogeneity in clonal cell populations . In biological applications , one is often interested in knowing the long-term behavior and stability properties of reaction networks even with incomplete knowledge of the model parameters . However for stochastic models , no analytical tools are known for this purpose , forcing many researchers to use a simulation-based approach , which is highly unsatisfactory . To address this issue , we develop a theoretical and computational framework for determining the long-term behavior and stability properties for stochastic reaction networks . Our approach is based on a mixture of ideas from probability theory , linear algebra and optimization theory . We illustrate the broad applicability of our results by considering examples from various biological areas . The biological implications of our results are discussed as well .
|
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"Abstract",
"Introduction",
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"Methods",
"Discussion"
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2014
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A Scalable Computational Framework for Establishing Long-Term Behavior of Stochastic Reaction Networks
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Phosphoglycerate kinase 1 ( PGK1 ) catalyzes the reversible transfer of a phosphoryl group from 1 , 3-bisphosphoglycerate ( 1 , 3-BPG ) to ADP , producing 3-phosphoglycerate ( 3-PG ) and ATP . PGK1 plays a key role in coordinating glycolytic energy production with one-carbon metabolism , serine biosynthesis , and cellular redox regulation . Here , we report that PGK1 is acetylated at lysine 220 ( K220 ) , which inhibits PGK1 activity by disrupting the binding with its substrate , ADP . We have identified KAT9 and HDAC3 as the potential acetyltransferase and deacetylase , respectively , for PGK1 . Insulin promotes K220 deacetylation to stimulate PGK1 activity . We show that the PI3K/AKT/mTOR pathway regulates HDAC3 S424 phosphorylation , which promotes HDAC3-PGK1 interaction and PGK1 K220 deacetylation . Our study uncovers a previously unknown mechanism for the insulin and mTOR pathway in regulation of glycolytic ATP production and cellular redox potential via HDAC3-mediated PGK1 deacetylation .
Phosphoglycerate kinase ( EC 2 . 7 . 2 . 3; PGK ) catalyzes the reversible phosphotransfer reaction from 1 , 3-bisphosphoglycerate ( 1 , 3-BPG ) to ADP to form 3-phosphoglycerate ( 3-PG ) and ATP . The PGK-catalyzed reaction is the first ATP-yielding step of glycolysis and is essential for energy generation by the glycolytic pathway of aerobes and the fermentation of anaerobes in most living cells [1] . Besides ATP , the other product of PGK-catalyzed reaction is 3-PG , which can not only serve as a glycolytic intermediate but also be oxidized by phosphoglycerate dehydrogenase ( PHGDH ) to form 3-phosphohydroxypyruvate and thus enter one-carbon metabolism [2] . It is known that one-carbon metabolism involving the folate and methionine cycle integrates carbon units from amino acids , including serine and glycine , and creates various outputs , such as the maintenance of redox status by affecting glutathione biosynthesis and NADPH production , the synthesis of lipids , nucleotides , and substrate for methylation reactions [3–5] . By controlling ATP and 3-PG levels , PGK therefore plays an important role in coordinating energy production with biosynthesis and redox balance . Regulation of PGK has been studied extensively , with research mostly focused on the transcriptional level . In yeast cells , PGK is one of the most highly expressed genes and accounts for approximately 5% of the total mRNA and protein [6] . PGK gene expression can be regulated by diverse carbon sources , with glucose induction and pyruvate suppression having been observed in yeast cells [7–9] . PGK gene expression is also up-regulated by oxidative stress [10] . Overexpression of PGK can suppress the apoptotic phenotypes induced by high ROS and restore normal aging of yeast cells [11] . In humans , PGK has two isoforms ( PGK1 and PGK2 ) , which share 87% identity in amino acid sequence and are structurally and functionally similar , but have different expression patterns [12–14] . PGK1 ( Gene ID: 5230 ) is broadly expressed in most cell types , while PGK2 ( Gene ID: 5232 ) is uniquely expressed in meiotic and postmeiotic spermatogenic cells [13] . PGK1 gene as the target of the hypoxia-inducible transcriptional factor HIF-1α has been reported to be selectively up-regulated by oxidants in cultured human colon carcinoma cells [15] and hepatoblastoma cells [16 , 17] . In contrast to the extensive investigation on the transcriptional regulation of PGK1 , little is known about its post-translational regulation . Protein acetylation has recently been discovered as an evolutionarily conserved post-translational modification in the regulation of a wide range of cellular processes , particularly in nuclear transcription and cytoplasmic metabolism [18–20] . Together with several recent acetylome proteomic studies [21–23] , more than 4 , 500 acetylated proteins , including human PGK1 , have been identified by the mass spectrometric analyses . In this study , we investigate the regulatory mechanism and functional consequence of PGK1 acetylation .
Previous proteomic studies have identified that PGK1 is acetylated on multiple lysine residues [18–20] . Western blotting with a pan anti-acetyl lysine antibody demonstrated that PGK1 was indeed acetylated , and its acetylation was elevated by 3 . 2-fold in HEK293T cells after treatment with nicotinamide ( NAM ) , an inhibitor of the SIRT family deacetylases [24 , 25] , and trichostatin A ( TSA ) , an inhibitor of histone deacetylase ( HDAC ) I and II ( Fig 1A ) [26 , 27] . By performing enzyme activity assay in vitro , we found that the specific enzyme activity of PGK1 was decreased by as much as 63% after NAM and TSA treatment ( Fig 1A ) , suggesting that acetylation negatively regulates PGK1 activity . Notably , the effect of TSA on increasing PGK1 acetylation and decreasing PGK1 activity was more potent than that of NAM ( Figs 1A , S1A and S1B ) , implying that a HDAC I and/or II is the major deacetylase for PGK1 . Given that PGK1 is a highly conserved protein [28] , we speculated that important regulatory sites in PGK1 targeted by acetylation might also be conserved . Sequence alignments from diverse species revealed that of twenty putative acetylated lysines identified by the different mass spectrometric analyses , fifteen lysine residues ( K11 , K30 , K48 , K86 , K91 , K97 , K131 , K146 , K156 , K192 , K199 , K264 , K267 , K291 , K323 ) are not conserved , while five ( K75 , K139 , K141 , K220 , K406 ) are invariably conserved ( S2 Fig ) . To determine which lysine residue ( s ) plays a major role in the regulation of PGK1 , we mutated each of the five conserved putative acetylated lysine residues in PGK1 to arginine ( R ) or glutamine ( Q ) and assayed their enzyme activity individually . The K to R mutation is often used as a deacetylation mimetic , whereas the K to Q mutation may act as a surrogate of acetylation [29] . We found that substitution at K220 , but not the other four lysine residues ( K75 , K139 , K141 , and K406 ) , by arginine substantially reduced PGK1 enzyme activity by 82% ( Fig 1B ) , indicating K220 has an important role in controlling PGK1 activity . Moreover , both the K220R and K220Q mutants exhibited a negligible response in changing acetylation level and enzyme activity upon NAM and TSA treatment ( Figs 1C and S3 ) , re-affirming that K220 is a major acetylation site in PGK1 . PGK1 exists as a monomer comprising two nearly equal-sized N- and C-terminal domains . This extended two-domain structure is associated with large-scale “hinge-bending” conformational changes , bringing the two substrates into close proximity , with 3-PG or 1 , 3-BPG binding to the N-terminal domain and the nucleotide substrate ADP binding to the C-terminal domain of the enzyme [30 , 31] . PGK1 in complex with 3-PG , ADP , and tetrafluoroaluminate ( ALF ) results in a fully active and closed conformation [14] . Molecular modeling predicts that K220 acetylation disturbs PGK1’s binding with ADP ( Fig 1D ) , suggesting that K220 acetylation may inhibit PGK1 enzyme activity by blocking the substrate ADP binding . To test this hypothesis , we employed an expression system genetically encoding Nε-acetyllysine to prepare recombinant PGK1 protein that was completely acetylated on K220 in Escherichia coli [32 , 33] . Briefly , the K220 codon in PGK1 was mutated to an amber stop codon . An amber suppressor tRNA and a tRNA synthetase that could conjugate the acetyllysine to the amber tRNA suppressor were also expressed in the bacteria . Therefore , in the presence of acetyllysine added in the culture medium , the amber stop codon at the position of K220 was replaced by an acetyllysine when PGK1 was expressed in the genetically engineered E . coli strain . This expression system produced PGK1 proteins with 100% acetylation at the lysine residue 220 . Moreover , we generated and verified an antibody that specifically recognizes the K220 acetylated PGK1 [α-acPGK1 ( K220 ) ] ( S4A , S4B and S4C Fig ) . The K220 acetylation of the recombinant PGK1 was confirmed by immunoblotting with the site-specific α-acPGK1 ( K220 ) antibody ( Fig 1E ) . Consistent with the structural prediction , the recombinant PGK1K220ac protein was catalytically inactive when compared to recombinant wild-type PGK1 . Importantly , isothermal titration calorimetry ( ITC ) analysis demonstrated that the recombinant PGK1K220ac protein was defective in binding with ADP ( Fig 1F ) . These results provide direct and unequivocal biochemical evidence to support a model that K220 acetylation in PGK1 inhibits its enzymatic activity by blocking the substrate ADP binding . The acetylation state of a given protein is controlled by the action of lysine acetyltransferases ( KATs ) and deacetylases ( KDACs ) , enzymes that catalyze the addition and removal , respectively , of an acetyl group from a lysine residue . To search for potential KAT ( s ) which are responsible for PGK1 K220 acetylation , we generated a siRNA library with three siRNAs targeting each of the 19 human KAT genes [34] . The knockdown efficiency of each siRNA against corresponding KAT genes was determined by quantitative RT-PCR ( S5 Fig and S1 Table ) . We found that knockdown of ATF2 ( Gene ID: 1386 ) , KAT5 ( Gene ID: 10524 ) , KAT6A ( Gene ID: 7994 ) , KAT9 ( Gene ID: 55140 ) , KAT12 ( Gene ID: 9329 ) , or KAT13B ( Gene ID: 8202 ) led to an increase in cellular PGK1 enzyme activity ( Fig 2A ) , indicating that these KAT enzymes may play a direct or indirect role in the regulation of PGK1 activity . Among these six candidate KAT genes , knockdown of KAT9 ( also known as ELP3 ) , but not the other five KATs , significantly decreased the K220 acetylation level of endogenous PGK1 without changing its protein expression in HEK293T cells ( Fig 2B and 2C ) . In a converse experiment , co-overexpression of HA-KAT9 with Flag-PGK1 increased the PGK1 K220 acetylation level by 2 . 2-fold and decreased PGK1 activity by 36% ( Fig 2D ) . Collectively , these results indicate that KAT9 is a potential acetyltransferase of PGK1 . Our earlier observation that TSA is more potent than NAM to increase PGK1 acetylation and inhibit PGK1 activity ( Fig 1A ) led us to search for the HDAC enzyme ( s ) that mediates PGK1 deacetylation . We found by binding assay that PGK1 interacted with HDAC3 ( NP_003874 . 2 ) , but not the other six HDACs , when co-expressed in HEK293T cells ( Fig 3A ) . The endogenous protein interaction between HDAC3 and PGK1 was readily detected in HEK293T cells ( Fig 3B ) . Furthermore , based on the protein amount and the immunoprecipitation efficiency , we found that ~16% of endogenous HDAC3 interacted with endogenous PGK1 ( Fig 3B ) . Co-overexpression of HA-HDAC3 with Flag-PGK1 decreased the acetylation level of PGK1 by 75% , and increased PGK1 activity by ~1 . 6-fold ( Fig 3C ) . In contrast , co-overexpression of HA-HDAC3 with the K220R/Q mutants of PGK1 did not change PGK1 acetylation or enzyme activity ( Fig 3C ) . When PGK1 was co-overexpressed with a catalytic inactive mutant HDAC3Y298H [35] , neither PGK1 acetylation nor enzyme activity was altered ( Fig 3D ) . Conversely , depletion of HDAC3 increased the K220 acetylation level of endogenous PGK1 by >2-fold and decreased PGK1 activity by >50% in HEK293T cells ( Fig 3E ) . More importantly , we performed in vitro pull-down assay using purified recombinant proteins of His-PGK1 and GST-HDAC3 , and found that HDAC3 directly binds with PGK1 in vitro ( Fig 3F ) . Furthermore , in vitro deacetylation assay using Flag-tagged PGK1 and GST-HDAC3 confirmed that PGK1 is a direct substrate of HDAC3 and that HDAC3-mediated K220 deacetylation increases PGK1 activity ( Fig 3G ) . Previous studies have shown that PGK1 gene expression is regulated by nutrient availability in yeast [7–9] , indicating that PGK1 may respond to energy status to maintain cellular energy homeostasis . We found that PGK1 activity was dose-dependently stimulated by insulin , an important metabolism and energy regulator , and this activation was associated with a concomitant reduction of PGK1 K220 acetylation ( Fig 4A ) . The effect of insulin on PGK1 K220 acetylation and activity was suppressed by TSA treatment ( Fig 4A ) . Moreover , the K220R mutant PGK1 displayed a negligible change in K220 acetylation and enzyme activity upon insulin stimulation ( Fig 4B ) , indicating that K220 is a vital site for PGK1 deacetylation and enzymatic activation by insulin . Next , we determined the K220 acetylation level of endogenous PGK1 in HEK293T cells . By using the recombinant PGK1K220ac protein purified from E . coli as the standard , we found that ~37% of endogenous PGK1 was acetylated at K220 , and that insulin treatment decreased PGK1 acetylation to 11% ( Fig 4C ) . The fact that a significant fraction of endogenous PGK1 undergoes deacetylation upon insulin treatment suggests that K220 acetylation plays a physiologically relevant role in PGK1 regulation . Furthermore , we assessed PGK1 K220 acetylation in mouse tissues after insulin injection . As expected , intraperitoneal injection of insulin ( 5 U/kg ) resulted in a transient drop in blood glucose levels ( Fig 4D ) . Interestingly , the K220 acetylation level of endogenous Pgk1 in mouse livers was significantly ( p < 0 . 01 ) decreased and bottomed at 60 min after insulin injection , followed by a period of recovery in parallel with the blood glucose levels ( Fig 4D and 4E ) . Similarly , Pgk1 K220 acetylation was dynamically changed in mouse kidneys after insulin injection ( S6 Fig ) . Together , these findings suggest that K220 acetylation plays a signaling role in regulating PGK1 function both in cultured cells and in mouse tissues upon insulin treatment . We next set out to investigate how insulin signal regulates PGK1 K220 acetylation and activity . We found that insulin signal did not change KAT9 gene expression in HEK293T cells ( S7A Fig ) . The PGK1-KAT9 protein association was readily detected in cells co-overexpressing Flag-PGK1 and HA-KAT9 , leading to an increase in PGK1 K220 acetylation ( S7B Fig ) . However , neither the KAT9-PGK1 protein interaction nor PGK1 K220 acetylation was affected by insulin treatment in cells co-overexpressing PGK1 and KAT9 ( S7B Fig ) . These findings suggest that KAT9 may not contribute to insulin-regulated PGK1 acetylation . On the other hand , we observed that the effect of insulin on reducing the K220 acetylation level of endogenous PGK1 was diminished in HEK293T cells when HDAC3 was depleted by siRNA ( Fig 5A ) . Insulin treatment did not change HDAC3 gene expression in HEK293T cells ( S8 Fig ) , but could enhance endogenous PGK1-HDAC3 association and decrease PGK1 K220 acetylation in a dose-dependent manner ( Fig 5B ) . In agreement , insulin injection reduced Pgk1 K220 acetylation and increased Pgk1-Hdac3 interaction in mouse livers ( Fig 5C ) . Together , these results suggest that insulin promotes PGK1 K220 deacetylation likely through enhancing PGK1-HDAC3 association . Recent studies have identified HDAC3 as a phosphorylated protein , with Ser424 being one of the key phosphorylation sites [36 , 37] . Protein kinase CK2 has been reported to be responsible for HDAC3 S424 phosphorylation [38] . Mutation of serine ( S ) 424 to alanine ( A ) ( mimics dephosphorylation ) inhibits HDAC3 enzyme activity without affecting its expression or subcellular localization [38] . In accord , we found that the HDAC3S424A mutant displayed a ~50% reduction in enzyme activity ( S9 Fig ) . Strikingly , we observed that HDAC3S424A mutant exhibited impaired association with endogenous PGK1 in HEK293T cells ( S9 Fig ) , suggesting that Ser424 phosphorylation is critical for not only the deacetylase activity of HDAC3 but also its interaction with PGK1 . To provide further evidence to support this notion , we transfected HEK293T cells singularly with plasmid expressing Flag-PGK1 or with plasmids co-expressing Flag-PGK1 and HA-HDAC3 . Lysates from transfected cells were first immunoprecipitated with Flag antibody and then the remaining supernatant was precipitated with HA antibody . We found that HA-HDAC3 was co-precipitated with Flag-PGK1 ( Fig 5D ) . Importantly , HA-HDAC3 in the PGK1 immune complex was highly phosphorylated on Ser424 , whereas the free HDAC3 was weakly phosphorylated . These results suggest that Ser424 phosphorylation of HDAC3 may enhance its interaction with PGK1 . Previous studies have shown that HDAC3 phosphorylation is regulated by phosphoinositide-3-kinase/AKT ( PI3K/AKT ) pathway [39] . In addition , HDAC3 has also been identified as a downstream target of mTOR [36 , 37] . This led us to hypothesize that the PI3K/AKT/mTOR signaling pathway may regulate HDAC3 S424 phosphorylation and PGK1 K220 acetylation in response to insulin . To test this hypothesis , we treated HEK293T cells co-overexpressing Flag-PGK1 and HA-HDAC3 with either LY294002 or Wortmannin , two specific PI3K inhibitors . As expected , either PI3K inhibitor profoundly attenuated AKT S473 phosphorylation ( S10A and S10B Fig ) . Notably , both LY294002 and Wortmannin dose-dependently increased K220 acetylation of ectopically expressed Flag-PGK1 and decreased the interaction between ectopically expressed PGK1 and HDAC3 ( S10A and S10B Fig ) . In accord , both LY294002 and Wortmannin dose-dependently increased K220 acetylation of endogenous PGK1 and decreased endogenous PGK1-HDAC3 association in HEK293T cells ( Fig 6A and 6B ) . In addition , MK-2206 2HCI , a specific AKT inhibitor , produced similar effects on changing PGK1 K220 acetylation and PGK1-HDAC3 association as the PI3K inhibitor LY294002 or Wortmannin ( Fig 6C ) . Furthermore , we observed that the mTOR inhibitor Rapamycin profoundly attenuated S6K T389 phosphorylation as expected , reduced HDAC3 Ser424 phosphorylation , impaired endogenous PGK1-HDAC3 interaction , and increased PGK1 K220 acetylation ( Fig 6D ) . Collectively , these findings demonstrate that the PI3K/AKT/mTOR pathway can regulate PGK1 K220 acetylation , possibly through controlling HDAC3 S424 phosphorylation and HDAC3–PGK1 protein interaction . The PI3K/AKT/mTOR pathway is regulated by a wide variety of cellular signals , including insulin [40] . We then studied the role of the PI3K/AKT/mTOR pathway in mediating insulin signal to control PGK1 K220 acetylation . As shown , insulin increased HDAC3 Ser424 phosphorylation and enhanced the interaction between ectopically expressed proteins of HDAC3 and PGK1 ( Fig 6E ) . The dephosphorylation-mimicking HDAC3S424A mutant exhibited weak binding to PGK1 even upon insulin treatment ( Fig 6E ) , supporting the notion that HDAC3 S424 is a key site for regulating HDAC3-PGK1 association upon insulin stimulation . Rapamycin treatment decreased HDAC3 Ser424 phosphorylation , impaired protein interaction between endogenous HDAC3 and PGK1 , and increased PGK1 K220 acetylation ( Fig 6F ) . Moreover , rapamycin abolished the effect of insulin on changing HDAC3 Ser424 phosphorylation , HDAC3-PGK1 association , and PGK1 K220 acetylation ( Fig 6F ) . Besides insulin , other growth factors , such as EGF , may also enhance the PI3K/mTOR pathway [41] . We found that EGF stimulation increased ERK1/2 phosphorylation , but not S6K T389 phosphorylation , in HEK293T cells , suggesting that EGF cannot potently activate mTOR in our experiment . As a result , EGF treatment did not change HDAC3 S424 phosphorylation and PGK1 K220 acetylation ( S11 Fig ) . Collectively , these data suggest that the mTOR pathway regulates PGK1 K220 acetylation through controlling HDAC3 S424 phosphorylation . To address the physiological significance of PGK1 regulation by K220 acetylation , we generated stable HEK293T cells in which endogenous PGK1 was depleted , and wild-type or K220 mutant PGK1 was re-introduced at a comparable level ( knockdown and put-back , S12 Fig ) . We found that ATP production did not differ between wild-type and PGK1K220Q mutant put-back cells ( Fig 7A ) . We then treated these stable cells with rotenone , a chemical which inhibits the complex I of the respiratory chain and thus mitochondrial ATP production [42 , 43] . A short-term treatment with rotenone greatly decreased cellular ATP production without inducing substantial cell death ( S13 Fig ) . Notably , PGK1K220Q mutant put-back cells displayed a remarkable reduction ( by 76%; p < 0 . 01 ) in glycolytic ATP production compared to wild-type rescued cells ( Fig 7A ) . Liquid chromatography-mass spectrometry ( LC-MS ) analysis demonstrated that the level of 3-PG , which is the product of PGK1 catalysis , was dramatically reduced , by 89% ( p < 0 . 05 ) , in PGK1K220Q mutant put-back cells compared to wild-type rescued cells ( Fig 7B ) . 3-PG can be oxidized to 3-phosphohydroxypyruvate for de novo ( that is originated from glucose ) serine biosynthesis [44] . In accord with reduced 3-PG , the serine level was significantly decreased ( by 64%; p < 0 . 01 ) in PGK1K220Q mutant put-back cells ( Fig 7B ) . Moreover , extracellular acidification rate ( ECAR ) analysis revealed that several parameters reflecting the glycolytic function , such as glycolysis , glycolytic capacity , and glycolytic reserve , were lower in PGK1K220 mutant put-back cells than wild-type rescued cells ( Figs 7C and S14 ) . Glucose consumption was significantly decreased ( ~60% less; p < 0 . 01 ) , while glycogen storage was significantly increased ( by ~2 . 2-fold; p < 0 . 01 ) in the PGK1K220Q mutant put-back cells when compared to wild-type rescued cells ( S15A and S15B Fig ) . These results suggest that K220 acetylation plays an important role in regulating PGK1 activity and/or function to modulate glycolytic ATP production and glucose metabolism . We also observed that stable knockdown of PGK1 led to a ~65% reduction in the NADPH/NADP+ ratio in HEK293T cells ( Fig 7D ) , suggesting that PGK1 is an important contributor to NADPH pools besides its well-known role in glycolytic ATP production . Moreover , higher ROS production was detected in PGK1 knockdown cells subjected to menadione , a quinone compound that induces the production of superoxide radicals ( Fig 7E ) . ROS has been extensively implicated in signaling cascades , which function as important cell survival mechanisms in response to oxidative stress [45] . We found that PGK1 knockdown cells exhibited higher levels of cleaved PARP , an indicator of apoptosis ( S16A Fig ) , as well as higher levels of p38 MAPK phosphorylation , a stress-responsive kinase ( S16B Fig ) . Importantly , re-expression of wild-type PGK1 , but not the acetylated mimetic K220Q mutant , restored the NADPH/NADP+ ratio and suppressed ROS production in PGK1 knockdown cells subjected to menadione ( Fig 7D and 7E ) . Putting-back wild-type PGK1 , but not the K220Q mutant , reduced the levels of cleaved PARP and p38 MAPK phosphorylation in PGK1 knockdown cells when subjected to menadione ( S16A and S16B Fig ) . As a result , PGK1 knockdown cells exhibited a higher incidence of cell death in response to menadione , and re-expression of wild-type PGK1 , but not the K220Q mutant , could rescue cells from menadione-induced cell death ( Figs 7F and S17 ) . These results suggest that K220 acetylation plays an important role in regulating PGK1 activity/function to modulate NADPH redox and cellular oxidative response . To further illustrate the role of K220 acetylation in controlling glucose metabolism and redox potential in the cell , we generated PGK1 knockdown and put-back stable HEK293T cells , in which endogenous PGK1 was depleted and the K220Q mutant was re-introduced at a higher protein level in order to reach an equivalent PGK1 enzyme activity between the cells re-expressing wild-type and K220Q mutant PGK1 ( Fig 7G ) . By monitoring the reduction of NADH , we found that the activity of wild-type PGK1 was significantly ( p < 0 . 05 ) stimulated by insulin treatment ( Fig 7G ) , leading to a remarkable increase of 3-PG ( by 3 . 2-fold; p < 0 . 01 ) ( Fig 7H ) . In contrast , cells with PGK1K220Q mutant put-back cells displayed a negligible change in PGK1 enzyme activity and 3-PG upon insulin stimulation ( Fig 7G and 7H ) . These findings strongly support the notion that K220 acetylation plays an important role in regulating PGK1 activity/function in cells upon physiological stimulus , such as insulin .
The current study uncovers a biochemical mechanism for how acetylation controls the activity/function of PGK1 . We have identified KAT9 and HDAC3 as the potential acetyltransferase and major deacetylase of PGK1 , respectively . To the best of our knowledge , we show for the first time that acetylation of PGK1 plays an important role in modulating glycolytic energy production and cellular oxidative response . We have identified K220 as an important regulatory acetylation site within the PGK1 protein . Human PGK1 exists as a monomer containing two nearly equal-sized domains corresponding to the N- and C-termini of the protein [30] . 1 , 3-BPG binds to the N-terminal domain whereas ADP binds to the C-terminal domain of PGK1 [46] . Though the binding of either substrate 1 , 3-BPG or ADP triggers a conformational change , only through the binding of both substrates does domain closure occur , bringing the two substrates into the proper vicinity for phosphotransfer [47 , 48] . Of note , K220 locates in the C-terminus of PGK1 and interacts with the nucleotide substrate ADP [48 , 49] . Our data show that acetylation mimetic K220Q substitution significantly reduces PGK1 catalysis . This is best illustrated by the observation that the recombinant PGK1K220ac protein is defective in ADP binding , implying that neutralization of the positive charge of K220 by acetylation may disrupt ADP binding and thus inhibit PGK1 catalysis . It has to be noted that both acetylation mimetic K220Q substitution and deacetylation mimetic K220R substitution can significantly reduce PGK1 activity . This raises another possibility , that is , the steric hindrance that occurs when K220 is mutated to Q or R , and this will generate different interaction force and/or steric hindrance , thereby abolishing ADP binding and inhibiting PGK1 catalysis . Our study has provided novel insights into the role of K220 acetylation in controlling PGK1 activity/function in response to insulin . To the best of our knowledge , this is the first evidence linking the regulation of PGK1 to mTOR downstream of PI3K/AKT and insulin . In addition , we show a molecular crosstalk between mTOR-mediated HDAC3 S424 phosphorylation and PGK1 K220 acetylation . We have identified HDAC3 as the major deacetylase of PGK1 . HDAC3 can form multi-protein complexes with the co-repressors SMRT and N-CoR and deacetylates histones , thereby regulating the transcription of a plethora of genes [50 , 51] . In addition , many non-histone substrates of HDAC3 have been identified , including the NF-ƘB protein RelA [52] , sex-determining region Y ( SRY , a master regulator of testis organogenesis ) [53] , and several transcription factors such as p53 [54] , myocyte enhancer factor-2 ( Mef2 ) [55] , and glial cell missing ( GCMa ) [56] . Very recently , it was reported that HDAC3 deacetylates methionine adenosyltransferase IIα ( MAT IIα ) in the methionine cycle [57] , indicating that HDAC3 can deacetylate a metabolic enzyme and may play an important role in regulating metabolic pathway ( s ) . Supporting this notion , we show in this study that another metabolic enzyme , PGK1 , is a direct substrate of HDAC3 and that HDAC3-mediated PGK1 deacetylation plays a signaling role in regulating PGK1 activity/function upon insulin stimulation . Moreover , we demonstrate that the PI3K/AKT/mTOR pathway regulates PGK1 K220 acetylation , in part , via affecting HDAC3 Ser424 phosphorylation , suggesting a potential mechanism for HDAC3 Ser424 phosphorylation regulating PGK1 K220 acetylation . Upon insulin stimulation , the PI3K/AKT/mTOR pathway induces HDAC3 Ser424 phosphorylation , which increases the deacetylase activity of HDAC3 and/or enhances the protein association between HDAC3 and PGK1 , leading to PGK1 K220 deacetylation and enzyme activation ( Fig 7I ) . mTOR is a central cell growth controller and is potently activated by insulin [58] . In addition , mTOR is also regulated by nutrient availability and cellular energy status to control cellular metabolism . Our data provides a direct link of mTOR activation to glycolysis , which is achieved by mTOR-mediated HDAC3 phosphorylation and PGK1 deacetylation , thus leading to PGK1 activation . These results also provide an example illustrating how cells integrate different pathways such as extracellular growth signaling and intracellular metabolic flux by a crosstalk involving different type of protein modifications such as phosphorylation and acetylation . In addition , we have also identified KAT9 as a potential acetyltransferase of PGK1 . KAT9/ELP3 , which encodes the catalytic subunit of the histone acetyltransferase elongator complex , has previously been identified as an α-tubulin acetyltransferase in mouse neurons [59] . Previously , we reported that KAT9 is the potential acetyltransferase of glucose-6-phosphate dehydrogenase ( G6PD ) , which is a key enzyme in the pentose phosphate pathway and plays an essential role in the oxidative stress response by producing NADPH [60] . In this study , we show that KAT9 as the potential acetyltransferase of PGK1 interacts with and increases PGK1 acetylation , thereby inhibiting PGK1 activity . However , KAT9 may not contribute to cells sensing insulin signal to regulate PGK1 acetylation and function , as neither KAT9 expression nor KAT9-PGK1 interaction is changed upon insulin treatment . Our study also links the regulation of PGK1 activity by acetylation to cellular response to oxidative stress . We show that replacement of endogenous of PGK1 with an acetylation-mimetic K220Q mutant results in a significant decrease in NADPH production and higher susceptibility of cells to oxidative stress . The underlying mechanism for PGK1 controlling NADPH production remains unclear . We propose that a general inhibition of glycolysis may at least in part explain the reduction in NADPH production . In addition , we also show that both 3-PG and serine levels are significantly reduced in cells with PGK1 knockdown and put-back of the acetylation mimetic K220Q mutant as compared to wild-type rescued cells ( Fig 7B ) . 3-PG is essential for de novo serine biosynthesis [44] . Serine can be converted to glycine by serine hydroxymethyl transferase , a reaction that yields one carbon units , which enter the tetrahydrofolate cycle and are critical for NADPH production [61 , 62] . Whether PGK1 K220 acetylation inhibits PGK1 catalytic activity , leading to reduced production of 3-PG , disturbed serine biosynthetic flux , and subsequently reduced NADPH production , still needs further investigation . Recent work has suggested a critical role of serine metabolism in cancer pathogenesis [63–65] . Supporting this notion , PHGDH gene expression is up-regulated in diverse cancer cells , such as esophageal adenocarcinoma , triple-negative breast cancer , and melanoma [66] . Moreover , cancer cells with PHGDH amplification were found to exhibit increased metabolic flux into serine biosynthesis , which is known to play a dual role in redox balance and providing nucleotide units to support cancer cell proliferation [5 , 67] . As noted above , K220 acetylation inhibits PGK1 activity/function and reduces the 3-PG and serine levels , implying that modulation of PGK1 activity/function by acetylation may serve as a promising anti-cancer strategy through regulating serine metabolism . Moreover , the clinical implication of PGK1 dysfunction has been highlighted by chronic haemolysis with progressive neurological impairment in PGK1-deficient patients [68–70] . Two metabolic alterations , a decreased steady-state level of ATP and an increased 2 , 3-BPG , in red blood cells with human PGK1 deficiency have been proposed to cause hemolytic anemia [71] . It is known that hemolytic anemia also contributes to the inability of erythrocyte cells to produce NADPH and withstand harmful oxidants [72] . Likely , PGK1 dysfunction-associated chronic haemolysis and neurological impairment are in part caused by oxidative stress due to increased levels of oxidative damage and decrease levels of antioxidants , such as reductant NADPH . In this study , we have provided evidence showing that HDAC3-dependent acetylation regulates the function of PGK1 in both glycolytic ATP production and NADPH redox . Therefore , future therapeutic intervention ( s ) to modulate PGK1 activity via HDAC3-mediated deacetylation may serve as a potential target for treating related diseases , such as hemolytic anemia and brain disorders associated with PGK1 dysregulation .
All the animal experiments were carried out in accordance with the National Institutes of Health guidelines for the Care and Use of Laboratory Animals and the regulations of Fudan University for animal experimentation . MaleBALB/c mice ( 6–8 wk old , 20–25 g body weight ) were purchased from the Fudan Animal Center . Animals were given unrestricted access to a standard diet and tap water . Blood was collected from tail vein at different time points post intraperitoneal injection of insulin ( 5 U/kg body weight ) , and blood glucose levels were determined by using a glucose detection kit ( Roche ) . Before being humanely killed , mice were anesthetized with sodium pentobarbital ( 25 mg pentobarbital/kg body weight , ip ) . After that , mouse livers and kidneys were harvested and then homogenized using the Tissuelyser-24 ( Shanghai JingXin ) in 0 . 5% NP-40 buffer containing protease inhibitor cocktail , and lysed on ice for 30 min . To determine the acetylation level of endogenous Pgk1 , tissue lysates were incubated with the Pgk1 antibody ( Santa Cruz ) for 1 hr , followed by incubating with Protein-A beads ( Upstate ) for another 2 hr at 4°C . The Rabbit anti-pan-acetyllysine antibody was generated as previously described [73] . To generate acetyl-lysine 220 specific antibody of PGK1 , synthetic peptide VADKIQLINNMLDK was coupled to KLH as antigen to immunize rabbit ( Shanghai Genomic Inc ) . For more detail information about the other antibodies used in this study , please refer to the S1 Text . Flag-tagged PGK1 protein was overexpressed in HEK293T cells , immumoprecipitated and eluted by 250 mg/ml Flag peptides ( Gilson Biochemical ) dissolved in PBS ( pH 7 . 5 ) . The PGK1 activity assay was carried out as previously described [74] . The reactions were started by adding enzyme into the buffer containing 80 mM pH 7 . 6 triethanolamine , 8 . 0 mM MgCl2 , 0 . 25 mM NADH , 2 . 4 mM ATP , 12 mM 3-phosphoglycerate and 50 μg/ml glyceraldehyde-3-phosphate dehydrogenase in a total volume of 0 . 3 ml , and assayed at 25°C . By monitoring the reduction of NADH fluorescence ( Ex350 nm , Em470 nm ) , the specific PGK1 activity was measured using HITACHI F-4 , 600 fluorescence spectrophotometer . The K220 site-specific acetylated PGK1 was expressed in E . coli as previously described [32 , 33] . In short , the ORF of PGK1 was cloned into pTEV-8 vector with amber codon being incorporated at lysine 220 ( AAG to TAG by site-directed mutagenesis ) . The E . coli strain BL21 ( DE3 ) was transformed with three plasmids , pAcKRS-3 , pCDF PylT-1 , and pTEV-8-PGK1 . Cells were grown overnight in LB containing spectinomycin ( 50 μg/ml ) , kanamycin ( 50 μg/ml ) , and ampicillin ( 150 μg/ml ) at 37°C till OD600 reached 0 . 6–0 . 8 . The culture was added with 20 mM NAM and 10 mM N-acetyllysine ( Sigma-Aldrich ) . Protein expression was induced at 37°C by addition of 0 . 5 mM of isopropyl-1-thio-D-galactopyranoside ( IPTG ) for 3 hr . Afterward , the cells were harvested , the K220-acetylated PGK1 protein was purified and then stored at −80°C till further analysis . ITC assay was performed by using a MicroCal VP-ITC type microcalorimeter ( MicroCal Inc . ) . Briefly , temperature equilibration was allowed for 1–2 hr till to 20°C prior to the experiment . PGK1 protein and ADP ( Sigma ) were dialyzed against 40 mM Tris-HCl ( pH 7 . 4 ) , 50 mM KCl , and 10 mM MgCl2 . All solutions were thoroughly degassed before being used by centrifugation at 13 , 000 rpm for 20 min . The experiment was conducted by consecutively injecting 50 μM ADP solution into the calorimetric cell containing 50 μM purified PGK1 . The titration enthalpy data was corrected for the small heat changes in control titrations of ADP solution into the dialysis buffer . To generate stable PGK1 knockdown cell pools in HEK293T cells , shRNA targeting PGK1 was constructed , and retrovirus was produced using a two-plasmid packaging system as previously described [75] . The shRNA targeting sequence for PGK1 is 5′-GCTTCTGGGAACAAGGTTAAA-3′ . The pMKO . 1-puro shRNA construct was co-transfected with vectors expressing the gag and vsvg genes into HEK293T cells . Retroviral supernatant was harvested 36 hr after transfection , and mixed with 8 μg/mL polybrene to increase the infection efficiency . HEK293T cells were infected with the retrovirus and selected in 1 μg/ml puromycin for 1 wk . To generate PGK1 knockdown and put-back stable cell pools in HEK293T cells , two silent nucleotide substitutions were introduced into Flag-tagged human wild-type or K220 mutant PGK1 in the sequence corresponding to the shRNA targeted region . Both shRNA resistant PGK1 were cloned into the retroviral pQCXIH-hygro vector and co-transfected with vectors expressing the gag and vsvg genes in HEK293T cells to produce retrovirus . Retroviral supernatant was harvested 36 hr after transfection , and mixed with 8 μg/mL polybrene to increase the infection efficiency . HEK293T cells with PGK1 knockdown were infected with the retrovirus and selected in 1 μg/ml puromycin and 2 μg/ml hygromycin for 4 wk . The intracellular NADPH/NADP+ was measured by enzymatic cycling methods as previously described [76 , 77] . Briefly , cells were counted and seeded in 10 cm dishes at a density of 1 . 5 × 106 . After 24 hr , cells were lysed in 400 μl of extraction buffer ( 20 mM NAM , 20 mM NaHCO3 , 100 mM Na2CO3 ) and centrifuged at 1 , 200 g for 15 min . 150 μl of the supernatant was incubated in a heating block for 30 min at 60°C for NADPH extraction . And then , 20 μl of the cell extract with 160 μl of NADP-cycling buffer ( 100 mM Tris-HCl , pH8 . 0; 0 . 5 mM thiazolylblue; 2 mM phenazine ethosulfate; 5 mM EDTA ) containing 1 . 3 U of G6PD was added to a 96-well plate . After incubation for 1 min at 30°C in darkness , 20 μl of 10 mM G6P was added to well , and measured the change of absorbance at 570 nm every 30 s for 10 min at 30°C by using SpectraMax M5 Microplate Reader ( Molecular Devices ) . Subtracting NADPH ( heated sample ) from the total of NADP+ and NADPH ( unheated sample ) was the level of NADP+ . Statistical analyses were performed with a two-tailed unpaired Student's t test . Almost all data shown represent the results obtained from triplicated independent experiments with standard deviation of the mean ( mean ± S . D . ) . The values of p < 0 . 05 were considered statistically significant . The numerical data and statistical analysis used in all figures are included in S1 Data .
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Phosphoglycerate kinase ( PGK1 ) catalyzes the reversible phosphotransfer reaction from 1 , 3-bisphosphoglycerate ( 1 , 3-BPG ) to ADP to form 3-phosphoglycerate ( 3-PG ) and ATP . By controlling ATP and 3-PG levels , PGK1 plays an important role in coordinating energy production with biosynthesis and redox balance . In contrast to the extensive investigation of the transcriptional regulation of PGK1 , little is known about its post-translational regulation . Here , we report that PGK1 is acetylated at lysine 220 ( K220 ) and this acetylation inhibits PGK1 activity by disrupting the binding with its substrate , ADP . We have identified KAT9 and HDAC3 as the acetyltransferase and deacetylase , respectively , for PGK1 . Moreover , we show there is molecular crosstalk between mTOR-mediated HDAC3 S424 phosphorylation and PGK1 K220 acetylation . Our study uncovers a previously unknown mechanism for the insulin and mTOR pathway in regulating glycolytic ATP production and cellular redox potential via HDAC3-mediated PGK1 deacetylation .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods",
"and",
"Materials"
] |
[] |
2015
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Insulin and mTOR Pathway Regulate HDAC3-Mediated Deacetylation and Activation of PGK1
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Characterizing the evolutionary relationships and population structure of parasites can provide important insights into the epidemiology of human disease . We examined 142 isolates of Trypanosoma brucei from all over sub-Saharan Africa using three distinct classes of genetic markers ( kinetoplast CO1 sequence , nuclear SRA gene sequence , eight nuclear microsatellites ) to clarify the evolutionary history of Trypanosoma brucei rhodesiense ( Tbr ) and T . b . gambiense ( Tbg ) , the causative agents of human African trypanosomosis ( sleeping sickness ) in sub-Saharan Africa , and to examine the relationship between Tbr and the non-human infective parasite T . b . brucei ( Tbb ) in eastern and southern Africa . A Bayesian phylogeny and haplotype network based on CO1 sequences confirmed the taxonomic distinctness of Tbg group 1 . Limited diversity combined with a wide geographical distribution suggested that this parasite has recently and rapidly colonized hosts across its current range . The more virulent Tbg group 2 exhibited diverse origins and was more closely allied with Tbb based on COI sequence and microsatellite genotypes . Four of five COI haplotypes obtained from Tbr were shared with isolates of Tbb , suggesting a close relationship between these taxa . Bayesian clustering of microsatellite genotypes confirmed this relationship and indicated that Tbr and Tbb isolates were often more closely related to each other than they were to other members of the same subspecies . Among isolates of Tbr for which data were available , we detected just two variants of the SRA gene responsible for human infectivity . These variants exhibited distinct geographical ranges , except in Tanzania , where both types co-occurred . Here , isolates possessing distinct SRA types were associated with identical COI haplotypes , but divergent microsatellite signatures . Our data provide strong evidence that Tbr is only a phenotypic variant of Tbb; while relevant from a medical perspective , Tbr is not a reproductively isolated taxon . The wide distribution of the SRA gene across diverse trypanosome genetic backgrounds suggests that a large amount of genetic diversity is potentially available with which human-infective trypanosomes may respond to selective forces such as those exerted by drugs .
Trypanosoma brucei is a unicellular flagellated parasite restricted to sub-Saharan Africa by the distribution of its tsetse vector ( Glossina spp . ) [1] . It has caused periodically devastating epidemics of human sleeping sickness . In the last decade , the annual number of new cases has decreased [2] , [3]; currently , the World Health Organization estimates that among the millions of people at risk across 36 countries , sleeping sickness causes approximately 50 , 000 deaths each year [4] , [5] . However , geographically restricted outbreaks can still cause severe economic and social disruption [6] , [7] and past disease cycles suggest that new epidemics could occur at any time [8] . In addition , appropriate drugs to treat the disease are still lacking [9] . Taxonomically , T . brucei is divided into three subspecies , largely based on their geographical origin , infectivity to humans and severity of disease . T . b . gambiense ( Tbg ) is restricted to West and Central Africa , where it causes a chronic form of sleeping sickness in humans . The Gambian form of sleeping sickness , caused by Tbg , was traditionally viewed as primarily a human infection , but it has become clear that a broad range of wild and domestic animal reservoirs also harbor the parasite [10] , [11] , [12] . A second human-infective subspecies , T . b . rhodesiense ( Tbr ) , is found in eastern and southern Africa and causes an acute form of sleeping sickness . Tbr is a zoonotic disease for which non-human vertebrates are the primary reservoir . The third subspecies , T . b . brucei ( Tbb ) , is distributed across sub-Saharan Africa , and is restricted to non-human vertebrates , in which it can cause nagana , a chronic wasting disease [13] . Over the last three decades , population genetic research has provided important insights into the biology of T . brucei and the epidemiology of sleeping sickness [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] . But the fine scale ecological and evolutionary processes underlying disease dynamics and the distinction of the different parasite forms are still not very well understood . From a taxonomic standpoint , this previous work has clearly established that Tbg is genetically distinct from Tbr and Tbb [14] , [15] , [24] , [25] . However , there is still a debate whether Tbg is evolutionarily older than Tbb/Tbr . As Tbg is less virulent than Tbr , there is a widespread belief that Tbg is evolutionarily older than Tbb/Tbr , based on the assumption that parasites generally evolve towards becoming more benign as they adapt to their host , an assumption that not supported by evidence [26] , [27] . In addition , the evolutionary relationship between Tbr and Tbb remains enigmatic; they are morphologically indistinguishable , sympatric in large parts of eastern Africa , and differentiated solely by their capacity to infect humans . Epidemics involving Tbr tend to occur in more or less discrete foci and may involve multiple Tbr lineages [28] , [29] , , but sometimes , a single lineage of Tbr may clonally expand to high frequency . Consequently , when population genetic structure is characterized over a small geographical range or over a small time frame , Tbr and Tbb may appear deceptively isolated from each other [31] , [32] . On the other hand , Tbr may evolve through frequent genetic exchange with sympatric Tbb , leading to a mosaic of different Tbr genotypes distributed throughout endemic regions of eastern Africa [30] , [33] . Laboratory studies have demonstrated that T . brucei is capable of sexual reproduction [34] and that crosses between Tbb and Tbr can produce viable progeny [35] . The extent to which this occurs in nature is unknown , but concurrent infections with multiple T . brucei genotypes are common [36] , providing ample opportunity for genetic exchange . The finding that isolates of Tbr from Uganda were more closely related to sympatric Tbb than to Tbr from Zambia supports the hypothesis that human-infective parasite may have had multiple origins in Africa [37] . Human infectivity in Tbr has been attributed to the serum resistance associated ( SRA ) gene [38] , [39] . The SRA gene has been PCR-amplified exclusively from human-infective trypanosome stocks [40] and transfection of Tbb with the SRA gene is sufficient to confer resistance to human serum [39] . Therefore , in eastern Africa , the SRA gene has emerged as a useful marker for identifying human-infective trypanosomes in their animal reservoir [33] , [40] , [41] . Given the potential for recombination between Tbr and Tbb [35] , the SRA gene could potentially occur on all genetic backgrounds ( i . e . turning Tbb into Tbr through recombination ) . This would imply that the standing genetic variation and associated phenotypic variation present in all T . brucei parasites in eastern and southern Africa , could eventually occur in a human-infective form . The questions remain if T . brucei lineages exist that are incapable of hosting the SRA gene and if Tbb and Tbr are simply host-range variants . Insights into these questions will be critical for more clearly defining the pool of parasites responsible for human disease , for understanding the emergence of new disease foci , and for eventually understanding how human-infectivity interacts with the evolution of other important traits such as animal host range [42] , parasite fitness [43] , virulence [44] , [45] , [46] and drug-resistance [47] . Our goal in this study was to clarify the evolutionary history of T . brucei and to more finely resolve the relationships between Tbr and Tbb from eastern and southern Africa , explicitly accounting for the SRA status of isolates . To accomplish this , we estimated phylogenetic relationships of all three subspecies using kinetoplast ( mitochondrial ) DNA sequence and integrated this with estimates of population structure based on nuclear microsatellite variation . We then examined the extent to which the distribution of the two existing lineages of the SRA gene among Tbr isolates matched the phylogenetic and population genetic patterns .
We obtained 142 isolates of T . b . brucei , T . b . rhodesiense and T . b . gambiense live , lysed , or as extracted DNA from the Swiss Tropical Institute Basel ( STIB , Reto Brun ) , University of Bristol ( Wendy Gibson ) , CIRAD-IRD/LRCT , Montpellier ( Pascal Grébaut ) , IRD , Montpellier ( Anne Clarisse Lekane ) , and Yale University ( Serap Aksoy ) ( see supplementary material , Table S1 ) . All isolates had been expanded in mice or in axenic culture in the past . Consequently , the diversity of parasite genotypes occurring in the wild may have been reduced by artificial selection pressures while cultures were maintained in an atypical environment [48] . All isolates were isolated in previous studies in adherence with national and institutional guidelines . Trypanosome isolates from patients were collected in previous studies as part of diagnostic procedures according to local ethical guidelines and were treated anonymously . Of those isolates with known host species , 16 ( 11% ) were originally isolated from tsetse flies , 73 ( 52% ) were from humans , and 52 ( 37% ) from other vertebrate hosts . The geographical origin of these isolates , which spans sub-Saharan Africa , is indicated in Figure 1 . Isolates of T . b . gambiense had been previously assigned five different taxonomic labels: Tbg , Tbg group 1 , Tbg group 2 , Tbg “non group 1” and “Tb non-gambiense group 1” . Tbg and Tbg group 1 were considered to be synonymous here and are referred to collectively as Tbg group 1 . This group , which comprises classical Tbg , is distinguished from the more virulent and genetically distinct taxon Tbg group 2 , which was originally found in Ivory Coast [11] , [49] . Isolates originally classified as “Tbg non group 1” or “Tb non-gambiense group 1” ( ob152–ob155 ) , for which human infectivity has not been established , were treated as Tbb . Depending on the quality of the material , DNA was extracted using either a DNA extraction kit ( Qiagen ) or using phenol/chloroform extraction . Partial cytochrome c oxidase subunit I ( CO1 ) was amplified from the kinetoplast ( kDNA ) genome of a subset of samples using primers Max1 ( 5′-ccctacaacagcaccaagt ) and Max2 ( 5′-ttcacatgggttgattatgg ) designed to the CO1 open reading frame contained in the maxicircle sequence of T . b . brucei 427 ( GenBank accession no . M94286 ) and sequenced on an ABI3730 Genetic Analyzer ( Applied Biosystems Inc . ) . Sequences were aligned by eye with Sequencher 4 . 2 ( Gene Codes Corporation , Ann Arbor , MI ) . All isolates were also typed at eight dinucleotide microsatellite loci ( TB1/8 , TB2/19 , TB5/2 , TB6/7 , TB8/11 , TB9/6 , TB10/5 , TB11/13 ) using conditions described previously [50] . These loci , which are located on eight different chromosomes are not physically linked [51] . Isolates exhibiting three or more alleles at any locus were considered to harbor multiple infections [36] , [50] and were excluded from this analysis . We tested samples identified as Tbb and Tbr for the presence of the SRA gene . We performed PCR detection using the primers and protocols developed by Gibson et al . [33] ( primers SRA A/E ) and Radwanska et al . [52] ( primers SRA F/R ) . Products from primers SRA A/E were sequenced on an ABI3730 Genetic Analyzer . As a control to help ensure that failure to amplify SRA using either of these primer sets was not attributable to poor DNA quality , we also tested the same samples for amplification of a single-copy microsatellite ( Tb 9/6 , [50] ) . For some isolates we incorporated the results of prior typing efforts [33] , [52] . We limited this analysis to Tbb and Tbr since SRA has not been detected in Tbg groups 1 and 2 [33] , [52] , [53] . A phylogenetic tree of kinetoplast sequences was estimated using the Bayesian approach implemented in MrBayes [54] . Plotting of the appropriate maximum likelihood ( ML ) distance as determined by Modeltest [55] against the uncorrected p-distance for all sample pairs revealed saturation of the third codon position between ingroup and outgroup . Therefore third codon positions and combined first and second codon positions were treated as two separate partitions . The hierarchical likelihood ratio test implemented in MrModeltest [56] identified the Hasegawa , Kishino and Yano [57] model with gamma ( HKY85+G ) as the most appropriate nucleotide substitution model for the data in both partitions . Phylogenetic relationships were also estimated using maximum parsimony as implemented in PAUP* [58] . Bootstrap support was estimated using 1000 replicates . Trees were rooted with available sequences of T . cruzi ( GenBank accession no . DQ343646 ) , T . vivax , and T . congolense as outgroups ( the two latter sequences were produced by the Pathogens Sequencing Group at the Sanger Sequencing Centre and can be obtained from GeneDB . org ) . We assessed geographical and taxonomic patterns in haplotype distribution using a haplotype network constructed using the statistical parsimony approach implemented in the program TCS 1 . 21 [59] . Sub-networks were created using the 99% confidence limit settings . Subsequently , sub-networks were connected to each other by relaxing the confidence limit . Divergence between subnetworks was calculated in the program DnaSP [60] . We used the individual-based Bayesian clustering approach implemented in the program STRUCTURE [61] to explore the hierarchical genetic relationships among all parasite isolates . For sexually recombining organisms , STRUCTURE estimates the proportion of each individual's genome that is derived from one of K pre-specified populations . In the case of an often clonal organism such as T . brucei , inferred “populations” are likely to reflect the major clades of the coalescent tree and these clusters can help to describe the structure of genetic variation ( J . Pritchard , pers . comm . ) . To identify the most likely K , we conducted 3 independent runs for each K from 1 to 16 , assuming an admixture model and correlated allele frequencies . We used a burn-in of 50 , 000 and replication values of 250 , 000 . We used two methods to determine the most likely number of clusters given the data . In the first , the likelihood values of each K ( i . e . L ( K ) ) were converted into posterior probabilities as suggested by Pritchard et al . [61] to assess which number of subpopulations is most probable given the data . In the second , the greatest value of delta K , the second order of change in L ( K ) divided by the standard deviation of L ( K ) was taken as indication for the optimal K as suggested by Evanno [62] . We examined whether clusters of genetically similar individuals within the Tbb/Tbr group were more similar in geographical origin than expected by chance , given our sampling . For this analysis , individuals were assigned to the single cluster in which they exhibited the highest membership probability . We calculated a statistic that measured the sum of all differences between country of origin ( same = 0 , different = 1 ) for all pairwise comparisons among individuals within clusters . We then randomly re-assigned individuals to clusters 1000 times and calculated the same statistic for each permutation . Significance was determined by comparing the observed value to the distribution generated by random permutation . We also performed a similar analysis using date of sampling , but here the statistic was the sum of differences between years of sampling ( number of years difference between two isolation events ) for all pairwise comparisons among isolates within clusters . Permutations were performed in SAS v 9 . 1 ( SAS Institute , Cary , NC ) . We further evaluated the genetic differentiation between subspecies of T . brucei using principal components analysis ( PCA ) . This method , which makes no assumptions regarding Hardy-Weinberg or linkage equilibrium , reduces the dimensionality of microsatellite data to two axes , allowing for easy visualization of relative differentiation . PCA was performed in R [63] using the package adegenet [64] . Within subspecies of T . brucei , we estimated the differentiation between temporally and geographically cohesive subgroups using DEST , an estimate of Jost's D [65] calculated with the program smogd [66] . DEST , which varies on a scale from 0 ( no differentiation ) to 1 ( complete differentiation ) , provides a less biased estimate of differentiation than FST and related statistics , particularly when estimated using highly polymorphic microsatellite loci [67] .
Sequencing of CO1 yielded 812 base pairs with no gaps or stop codons . We recovered a total of 19 distinct haplotypes from the 87 T . brucei isolates sequenced ( Table S2 ) . These haplotypes exhibited sequence divergence ranging from 0 . 1% ( 1 nucleotide substitution ) to 4 . 2% ( 34 substitutions ) . With the exception of the placement of Hap13 , topologies recovered from Bayesian analysis and from maximum parsimony analysis were almost identical; therefore , we present only the results of the former . The 50% majority rule tree resulting from the Bayesian analysis of kinetoplast haplotypes ( Figure 2 ) revealed one well-differentiated high-level clade ( Clade A , Hap1 to Hap 12 ) . Clade A was composed of haplotypes recovered from each of the three subspecies of T . brucei , all of which were more closely related to each other than to haplotypes Hap 13 to Hap 19 , which formed clades B and C . The latter haplotypes derived from one isolate of Tbr , as well as several isolates of Tbb that had been previously assigned to the “Sindo” ( Hap13 ) or “Kiboko” groups ( Hap14 to Hap19 ) by kDNA typing [68] or isoenzyme analysis [15] ( Table S1 ) . Within Clade A , haplotypes were further structured , with Clade A2 exhibiting strong Bayesian and bootstrap support . Subclade A2 was composed of all three subspecies of T . brucei and contained all haplotypes recovered from Tbg . Tbg group 1 was represented by only two closely related haplotypes ( Hap8 and Hap9 ) , which differed by just one nucleotide . Hap8 was recovered from 34 out of 35 Tbg group 1 isolates . Isolates classified as Tbg group 2 were represented by three different haplotypes ( Hap6 , Hap10 and Hap12 ) , each of which was also found in Tbb and one of which ( Hap6 ) was also recovered from Tbr . A close relationship between Tbr and Tbb was supported by the fact that four out of the five haplotypes recovered from Tbr were also recovered from Tbb , and these haplotypes were distributed across the phylogeny . The structure observed in the Bayesian phylogeny was reiterated in a haplotype network ( Figure 3A ) . Haplotype network construction resulted in three separate subnetworks , reflecting the relatively large divergence ( ∼3% ) observed between Clades A , B and C . Clade A was composed of isolates found across all of Africa while Clade C appeared to be restricted to eastern and southern Africa ( Figure 3B ) . Isolates of Tbb or Tbr from Kenya , Tanzania and Zambia were represented in both Clades A and C . The most commonly recovered haplotype of Tbg ( Hap8 ) was found across most of central and western Africa and from every country in which Tbg was sampled ( Figure 3B ) . We used microsatellites to genotype 27 isolates of Tbr , 55 isolates of Tbb and 58 isolates of Tbg collected across Africa ( Figure 1B and Table S1 ) . The L ( K ) values derived from STRUCTURE analysis indicated that the probability of our data was maximized by K = 11 partitions . Alternatively , ignoring the strong signal derived from the obvious division between parasites identified as Tbg group 1 and all other parasites , Evanno's criterion ( delta K ) indicated that our data were most consistent with K = 5 partitions . To capture the hierarchical relationships among genotypes , Figure 4 shows the clustering results for K = 5 and 11 , as well as K = 3 , corresponding to the number of classically-defined subspecies presumed to be present in our sample . Nesting of clusters ( from K = 11 to K = 3 ) reflects the hierarchical relationships among parasite genotypes . Among isolates of Tbb and Tbr , only one cluster ( Cluster 2 , Figure 4 ) exhibited strong cohesion across various levels of K . This cluster was composed exclusively of Tbb from Kenya and Tanzania , and contained all individuals of Tbb that had been previously identified as the “Kiboko B” group by isoenzyme and kDNA analysis ( Table S1 ) . These isolates also possessed a discrete group of closely related kDNA haplotypes ( Hap14–Hap16 ) that were not shared by any isolates outside of this cluster . The relative differentiation of this group compared to other Tbb/Tbr and to Tbg is visualized in Figure 5 , in which the first two axes accounted for 40% of the overall genetic variance . None of the isolates belonging to Cluster 2 tested positive for the SRA gene; however , clustering of isolates at K = 3 and K = 5 , as well as the PCA analysis , identified two isolates of Tbr ( ob065 , ob066 ) that were closely related to isolates in Cluster 2 . Outside of Cluster 2 , Tbb and Tbr exhibited strong genetic similarity as reflected in broadly overlapping 95% ellipses in PCA analysis ( Figure 5 ) . At finer scales , clustering of genotypes indicated that in many cases , Tbb and Tbr isolates are more closely related to each other than they are to other isolates of the same subspecies . Clusters 1 , 3 , 4 , 5 , 6 , 7 , 8 and 9 were each composed of isolates of both Tbb and Tbr ( Figure 4 ) and in Cluster 8 , two isolates of Tbb and Tbr differed from each other by just one allele at one locus ( b179 = RUMP 503 ( Tbb ) and b021 = STIB 391 ( Tbr ) ) . We detected the SRA gene in all isolates of Tbr except for isolate KETRI 2538 , which had been designated as Tbr based on isolation from a human patient . Thus , the SRA gene occurred in seven of the ten genetic clusters containing at least one isolate of Tbb/Tbr from eastern Africa ( Figure 4 ) . Among the samples for which we were able to generate SRA sequence with primers A/E , we detected just two sequence variants . Across the 420 bp of sequence , SRA type 1 was identical to the sequence previously deposited under GenBank accession no . AJ345057 and SRA type 2 was identical to GenBank accession no . AJ345058 . These two sequence fragments differed by just three polymorphic sites . SRA type 1 was found in 12 isolates from Uganda , Kenya and Tanzania , while SRA type 2 was found in 11 isolates from Zambia , Ethiopia and Tanzania ( Table S1 ) . In the one location where both types occurred sympatrically ( Serengeti National Park , Tanzania ) , we detected isolates in which the two different SRA types associated with the same kDNA lineage ( Hap1 ) , but these isolates belonged to different clusters based on their microsatellite genotypes . Across our wider sampling , SRA type 1 was associated with two different kDNA lineages ( Hap1 and Hap5 ) , while SRA type 2 was associated with four different lineages ( Hap1 , Hap4 , Hap6 and Hap19; Figure 3 and Figure 4 ) . Permutation tests indicated that isolates of Tbb/Tbr found within the same genetic cluster were more likely to originate from the same country than expected by chance alone ( p<0 . 001 ) . This relationship remained significant after excluding individuals from Cluster 2 ( p<0 . 001 ) , i . e . when only those clusters were considered that contained both Tbb and Tbr . Similarly , individuals from the same genetic cluster were more likely to have been sampled within a similar time period than expected by chance ( p<0 . 001 ) . This , too , remained significant after excluding individuals from Cluster 2 , all of which had been isolated between 1970 and 1973 ( p = 0 . 009 ) . The broad geographical and temporal scale over which samples were collected limited our ability to quantify genetic differences among populations defined by narrow sampling in time and space . Among the groups of isolates that were most cohesive , we observed strong differentiation between isolates from the “Kiboko B” cluster of Tbb ( Cluster 2 ) sampled in Tanzania between 1970–1971 and other isolates of Tbb sampled in the same place and time ( DEST = 0 . 59±0 . 11; Table 1 ) . Isolates of Tbr sampled concurrently in Tanzania were similarly divergent from the “Kiboko B” cluster ( DEST = 0 . 53±0 . 12 ) , but exhibited lower differentiation from other Tbb ( DEST = 0 . 10±0 . 06 ) . The low differentiation observed between Tbr and Tbb ( excluding “Kiboko B” ) in Tanzania was similar to that observed between isolates of Tbr sampled 30 years apart in Uganda ( DEST = 0 . 08±0 . 08 ) . Across all levels of partitioning , isolates of Tbg group 1 formed a single uniform cluster in STRUCTURE analyses ( Cluster 11; Figure 4 ) . Isolates of Tbg group 1 also formed a relatively tight and distinct group of genotypes in PCA analysis ( Figure 5 ) . Only one isolate identified as Tbg group 1 ( b028 = STIB 368 ) did not join this cluster . Within Cluster 11 , genetic divergence was low between groups of isolates defined by disease focus and collection date ( Table 2 ) . The average pairwise differentiation among all foci was DEST = 0 . 12 . Reflecting this low level of genetic divergence among Tbg group 1 , we identified just 21 multilocus genotypes among the 54 isolates sampled across central and western Africa . While most of the genotypes that were recovered more than once originated in the same or adjacent countries , two multilocus genotypes were shared between the Ivory Coast and either Equatorial Guinea or the Democratic Republic of Congo ( Table S3 ) . One of these multilocus genotypes had persisted for a period of about 18 years ( 1960–1978 ) , and we identified a second multilocus genotype that had persisted for at least 22 years ( 1968–1990 ) . Clustering of microsatellite genotypes from isolates identified as Tbg group 2 , all originating from the Ivory Coast , indicated a close association between these parasites and isolates of Tbb and Tbr ( Figure 4 ) . At K = 11 , two Tbg group 2 isolates clustered together with isolates of Tbb from Uganda , Burkina Faso and Cameroon ( Cluster 10 ) . One of these Tbg group 2 isolates ( b151 = TH02 ) shared a kDNA haplotype ( Hap6 ) with Tbb isolates from Uganda and Tanzania , and Tbr isolates from Tanzania and Ethiopia . The other isolate ( b032 = STIB386 ) shared a kDNA haplotype with Tbb . The remaining isolate representing Tbg group 2 ( b146 = TH113 ) also shared a haplotype ( Hap12 ) with an isolate of Tbb ( b152 = TSW65 , isolated from a pig in the Ivory Coast ) and exhibited a signal of mixed ancestry between Tbg group 1 ( Cluster 11 ) and Tbb/Tbr ( Cluster 9 ) based on STRUCTURE analysis ( Figure 4 ) . Assignment probabilities for this isolate exhibited 95% credible limits that excluded zero for membership in both Cluster 9 and Cluster 11 ( data not shown ) . The results of Bayesian clustering were reflected in the PCA plot , which placed Tbg group 2 genotypes intermediate to Tbb/Tbr and Tbg group 1 ( Figure 5 ) . The two Tbb genotypes most closely related to the Tbg group 2 cluster derived from Uganda ( b009 ) and Ivory Coast ( b152 ) .
Human infective trypanosomes from eastern Africa fall into two groups based on clinical characteristics and are characterized by two SRA variants [33] , [44] . Our results generally confirm the previously observed geographical partitioning: we found SRA type 1 in Uganda , Kenya and Tanzania , and SRA type 2 in Tanzania , Zambia and Ethiopia . While prior detection of SRA type 2 had been limited to patients sampled in Zambia , Malawi and Ethiopia , we have extended the known range of SRA type 2 to wildlife reservoir hosts in northwest Tanzania . Consequently , Tanzania appears to be a rare location where both SRA types co-occur . Here , trypanosome lineages with SRA type 1 and type 2 were associated with the same kDNA haplotype but distinct microsatellite genotypes . Presuming that an opportunity for dispersal exists , the distinct SRA types may eventually be expected to co-occur elsewhere , raising the potential need for diagnostics that differentiate between these two types . If the SRA gene , which is responsible for human infectivity of Tbr [39] , [71] , is freely transferable across trypanosome genomes via sexual recombination , then the SRA gene should be associated with trypanosome genetic backgrounds that encompass the diversity observed in Tbb . Our results largely corroborate this scenario . We detected SRA in trypanosomes from both of the well-sampled kDNA clades and in seven of the ten genetic clusters inferred from microsatellite-based analysis that contained Tbb and/or Tbr isolates . Among Tbb/Tbr , only one cluster of isolates ( Cluster 2 , Figure 4 ) appeared to lack SRA while also exhibiting strong differentiation at microsatellite loci . These trypanosomes also possessed a unique group of kDNA haplotypes , potentially indicating that they have not exchanged genes with the other trypanosome lineages represented in our sample . Therefore , this group , containing individuals previously identified as “Kiboko B” , and isolated in the early 1970's from Kenya and Tanzania , may represent true animal-restricted trypanosomes , i . e . Tbb [68] . However , this would be a surprising outcome given that at least one cross between the “Kiboko B” group and an unrelated trypanosome lineage ( TREU927×STIB386 ) has been demonstrated in the laboratory [72] . Furthermore , we identified two isolates of Tbr that possessed kDNA haplotypes distinct from those possessed by the “Kiboko B” group , but exhibited nuclear genotypes very similar to the “Kiboko B” group ( Figure 5 ) . This is consistent with a recombination event between the “Kiboko B” group and an unrelated SRA-positive trypanosome lineage . Assessed more broadly , our results suggest that SRA has been gained ( by recombination ) or lost ( e . g . by gene conversion ) during multiple independent events in the past . For example , Cluster 5 and Cluster 8 are each composed of SRA-positive ( type 1 ) and SRA-negative trypanosomes that are more closely related to each other than they are to trypanosomes in the other cluster . The same is true for Cluster 7 and Cluster 9 . Previous work has revealed that human infective and animal-restricted trypanosomes from the same focus showed distinct allele sets , suggesting little recent exchange [18] , [19] . On the other hand , our results , which place the results from individual foci in the context of broader geographical sampling , demonstrate that parasites sampled in a restricted time and space often consist of SRA-positive and SRA-negative individuals that may be more closely related to each other than to SRA-positive and SRA-negative parasites recovered from another time and place . In other words , human infective and animal-restricted trypanosomes represent phenotypic variation in a single structured species [73] , [74] . Reconciling the apparent lack of interaction between Tbb and Tbr in a single focus with the capacity for the two to share genes will require more in depth ecological and functional molecular work . Nonetheless , the wide distribution of the SRA gene across trypanosome genotypes has important consequences for the evolution of human infectivity in Tbb/Tbr as it suggests that a large amount of genetic diversity is potentially available with which human-infective trypanosomes may eventually respond to selective pressures such as those exerted by drugs . Understanding the time-frame in which SRA can move between trypanosome groups will become particularly important as these genetic groups become better defined with respect to underlying phenotypes of importance , such as drug resistance and disease severity . High throughput next generation sequencing technologies offer the possibility of generating thousands of markers with which to more precisely circumscribe trypanosome groups . Linking these groups to important phenotypes will require large-scale field collections combined with dedicated collaborations with medical staff in disease-endemic countries . Tbg group 1 is the most common form of Tbg and is widespread across West and Central Africa . With the exception of one anomalous isolate ( STIB368 ) , which is very old ( collected in 1959 ) and may well have been mixed up during prolonged maintenance in the lab , trypanosomes identified as Tbg group 1 formed a cohesive genetic group . Tbg group 1 genotypes formed a single cluster at all levels of K in STRUCTURE analyses , and all isolates shared just two sister haplotypes within clade A of the kDNA phylogeny . Previous studies have used microsatellites to demonstrate limited genetic diversity within the nuclear DNA of Tbg group 1 [21] , [23] , [70] . Our data indicate that this taxon also shows limited diversity in kinetoplast DNA sequence and that extant Tbg group 1 kDNA haplotypes fall within a well supported clade representing just a fraction of overall Tbb/Tbr diversity . These results suggest that the mechanism governing human infectivity and reproductive isolation of Tbg group 1 arose relatively recently . The low virulence in this system is thus not correlated with age of the host-parasite association , as is sometimes suggested based on the wrong assumption [26] , [27] that parasites generally evolve towards being more benign as they become better adapted to the host ( and vice-versa ) . The low extant diversity in Tbg group 1 may be attributable to a recent and extreme bottleneck . Whatever the underlying cause of the low genetic diversity , the broad distribution of the most common Tbg group 1 haplotype across central and western Africa is consistent with the rapid colonization of hosts in this region . Tbg group 2 was originally identified among patient isolates from Ivory Coast; these trypanosomes do not share the low virulence of typical Tbg isolates , show variable resistance to the trypanolytic factor in human serum [11] and they do not possess the SRA gene [33] , [52] , [53] . Identical isolates were recovered from wild and domestic animals in Ivory Coast and Burkina Faso [11] . Relatively few isolates of this type have been recovered , but they have been reported to be genetically heterogeneous [70] , distinct from Tbg group 1 [11] , [75] , and closely related to Tbb [17] , [76] . In our analysis , kDNA haplotypes obtained from Tbg group 2 were distinct from haplotypes possessed by isolates of Tbg group 1 but fell within a single clade representing all three T . brucei subspecies . Each of the three haplotypes possessed by Tbg group 2 were shared with isolates classified as Tbb/Tbr . Clustering of microsatellite genotypes at K = 3 and K = 5 also supported a close ancestry between Tbg group 2 and Tbb or Tbr . At K = 11 in STRUCTURE analysis , two isolates of Tbg group 2 formed a discrete cluster with five isolates of Tbb . The remaining isolate exhibited approximately equal probability of membership in Tbg group 1 and Tbb/Tbr Cluster 9 , supporting a hybrid origin for some members of Tbg group 2 . Although many of the associations above point to close relationships between isolates of Tbg group 2 from western Africa and isolates of Tbb or Tbr originating in eastern Africa , these results are likely biased by a lack of sampling of Tbb in central and western Africa . Future sampling and genotyping of Tbb in these regions should help to resolve the evolutionary origins of human infectivity in the gambiense group of trypanosomes .
|
Trypanosoma brucei , the parasite causing human African trypanosomiasis ( sleeping sickness ) across sub-Saharan Africa is traditionally split into three subspecies: T . b . gambiense ( Tbg ) , causing a chronic form of human disease in West and Central Africa; T . b . rhodesiense ( Tbr ) , causing an acute form of human disease in East and Southern Africa; and T . b . brucei ( Tbb ) , which is restricted to animals . Tbg is further split into Tbg group 1 and Tbg group 2 . Better understanding the evolutionary relationships between these groups may help to shed light on the epidemiology of sleeping sickness . Here , we used three different types of genetic markers to investigate the phylogeographic relationships among the four groups across a large portion of their range . Our results confirm the distinctiveness of Tbg group 1 while highlighting the extremely close relationships among the other three taxa . In particular , Tbg group 2 was closely related to Tbb , while Tbr appeared to be a variant of Tbb , differing only in its phenotype of human infectivity . The wide geographic distribution of the gene conferring human infectivity ( SRA ) and the fact that it is readily exchanged among lineages of T . brucei in eastern Africa suggests that human-infective trypanosomes have access to an extensive gene pool with which to respond to selective pressures such as drugs .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"ecology/evolutionary",
"ecology",
"public",
"health",
"and",
"epidemiology/epidemiology",
"public",
"health",
"and",
"epidemiology/infectious",
"diseases",
"infectious",
"diseases/tropical",
"and",
"travel-associated",
"diseases",
"genetics",
"and",
"genomics/population",
"genetics"
] |
2011
|
Phylogeography and Taxonomy of Trypanosoma brucei
|
Catalysis of ADP-ATP exchange by nucleotide exchange factors ( NEFs ) is central to the activity of Hsp70 molecular chaperones . Yet , the mechanism of interaction of this family of chaperones with NEFs is not well understood in the context of the sequence evolution and structural dynamics of Hsp70 ATPase domains . We studied the interactions of Hsp70 ATPase domains with four different NEFs on the basis of the evolutionary trace and co-evolution of the ATPase domain sequence , combined with elastic network modeling of the collective dynamics of the complexes . Our study reveals a subtle balance between the intrinsic ( to the ATPase domain ) and specific ( to interactions with NEFs ) mechanisms shared by the four complexes . Two classes of key residues are distinguished in the Hsp70 ATPase domain: ( i ) highly conserved residues , involved in nucleotide binding , which mediate , via a global hinge-bending , the ATPase domain opening irrespective of NEF binding , and ( ii ) not-conserved but co-evolved and highly mobile residues , engaged in specific interactions with NEFs ( e . g . , N57 , R258 , R262 , E283 , D285 ) . The observed interplay between these respective intrinsic ( pre-existing , structure-encoded ) and specific ( co-evolved , sequence-dependent ) interactions provides us with insights into the allosteric dynamics and functional evolution of the modular Hsp70 ATPase domain .
Many proteins are molecular machines . They function because their three-dimensional structure allows them to undergo cooperative changes in conformation that maintain the native fold while enabling their biological functions . The changes have been pointed out to be structure-encoded , intrinsically accessible to proteins , as can be deduced from simple physics-based approaches [1] . Yet , amino acid specificity is another important property that selectively mediates the interactions with specific partners and ligands [2] . Overall , a subtle balance exists between structure-encoded mechanical properties and sequence-encoded specific properties , and this balance must be evolutionarily optimized to achieve precise functioning . The interplay between these two effects becomes particularly important in the case of a number of proteins or domains that play a modular role in a variety of biomolecular interactions . The ATPase domain ( also called nucleotide-binding domain ) of the Hsp70 family of proteins is a typical example . This domain plays a critical role in regulating the activities of these molecular chaperones , which , in turn , promote accurate folding , and prevent unwanted aggregation by either unfolding and refolding misfolded proteins or regulating their intracellular trafficking to the protein degradation machinery [3]–[5] . Chaperones of the Hsp70 family contain two domains: the N-terminal ATPase domain and the C-terminal substrate-binding domain ( SBD ) , which regulate each other's activity via allosteric effects . ATP hydrolysis at the ATPase domain increases the substrate-binding affinity of the SBD , thus lowering the substrate exchange rate; on the other hand , the dissociation of the ADP produced upon ATP hydrolysis and its replacement by a new ATP trigger the release of substrate by the SBD , and therefore enhance the substrate exchange rate [3] . Regulation of substrate-binding affinity by the ATPase domain forms the basis of the chaperone activity of Hsp70s [6] , [7] . The precise functioning of the Hsp70 ATPase domain involves an interaction with two families of co-factors , also called co-chaperones: the J-domain proteins that catalyze ATP hydrolysis [8] , and the nucleotide exchange factors ( NEFs ) that assist in the replacement of ADP with ATP , by significantly increasing the ADP dissociation rate [9] . A molecular understanding of Hsp70 function requires a systemic analysis of the structural basis and mechanism of interaction with these co-chaperones . Here we focus on the interaction of their ATPase domain with NEFs . The Hsp70 ATPase domain is composed of four subdomains: IA and IB in lobe I , and , IIA and IIB in lobe II ( Figure 1a ) . ATP binds the central cleft between the two lobes at the interface between subdomains IIA and IIB such that the geometric and energetic effects of its binding and hydrolysis are efficiently transmitted throughout the ATPase domain . To date , four classes of NEFs have been identified: GrpE in prokaryotes [10] , and BAG-1 [11] , HspBP1 [12] and Hsp110 [13] in eukaryotes . Their diverse three-dimensional structures ( Figure 1b–e ) exhibit a variety of binding geometries and interfacial interactions with the Hsp70 ATPase domain . In the present study , we examine these interactions , using sequence- , structure- and dynamics-based computations and identify their shared features . Our analysis provides insights into the generic and specific aspects of ATPase domain-NEF interactions , as well as the molecular machinery and sequence design principles of this highly versatile module , the Hsp70 ATPase domain , thus reconciling robust structure-encoded cooperative dynamics properties and highly correlated amino acid changes that enable specific recognition .
We began with 4839 sequences retrieved from the Pfam database 22 . 0 [14] for the Hsp70 family of molecular chaperones ( Pfam id: PF00012 ) . We refined the generated multiple sequence alignment ( MSA ) by using the consensus sequence of the ATPase domain ( 380 residues ) in the bovine cytosolic homolog of Hsp70 [15] . The refinement consists of three steps: ( i ) iterative implementation of Smith-Waterman algorithm ( SW ) for pairwise alignment [16] using our consensus sequence , and elimination of those sequences below a threshold SW score ( or less than 40% sequence identity; see details in Supplementary Material ( SM ) Figure S1 and Text S1 ) to retrieve the closest orthologs to human ( Hsc70 ) and bacterial ( DnaK ) chaperones; ( ii ) deletion of MSA columns that correspond to insertions with respect to the consensus sequence , and ( iii ) removal of the sequences containing more than 10 gaps . These three steps resulted in a MSA of 1627 sequences with N = 380 columns ( corresponding to residues 6 to 385 in Hsc70 ATPase domain ) , which has been subjected to evolutionary trace ( ET ) and mutual information ( MI ) analyses for detecting residue conservation and co-evolution patterns , respectively . We retrieved from the Protein Data Bank ( PDB ) [17] structural data for HSP70 ATPase domains complexed with GrpE ( PDB id: 1DKG [10] ) , BAG-1 ( PDB id: 1HX1 [18] ) , HspBP1 ( PDB id: 1XQS [19] ) , and Sse1 ( Hsp110 , PDB id: 3D2E [20] ) , shown in Figure 1b–e . Additionally , the structure of the above mentioned bovine Hsc70 ATPase domain resolved at 1 . 7 Å resolution ( PDB id: 1HPM [15] ) was used for the unbound form , and the PDB structure 1S3X [21] of the human Hsp70 served as a template to reconstruct the lobe I missing in the complex with HspBP1 using the method described in the SM Figure S2 and Text S2 . We performed Gaussian Network Model ( GNM ) [22] , [23] and anisotropic network model ( ANM ) [24] , [25] analyses for elucidating the equilibrium dynamics of Hsp70 ATPase domain both in the unbound form and in the complexes with different NEFs , including the reconstructed complex with HspBP1 . Details on the methods can be found in our previous work [22]–[26] . Mainly , knowledge of the distribution of inter-residue contacts in the native structure permits us to construct the Kirchhoff ( GNM ) and Hessian ( ANM ) matrices , which , upon eigenvalue decomposition , yield information on the collective modes spectra . We focused on the low frequency modes , also called global modes , as the major determinant of functional movements . In the GNM , each mode k is represented by an N-dimensional eigenvector , u ( k ) , and eigenvalue λk , describing the mode shape and frequency ( squared ) , respectively . The ith element , [u ( k ) ]i , of u ( k ) describes the displacement of residue i along the kth mode axis; the plot of [u ( k ) ]i2 as a function of residue index i defines the mobility profile Mi ( k ) in mode k . See for example , the mobility profile Mi ( 1 ) for the first ( lowest frequency ) mode accessible to Hsp70 ATPase domain in Figure 2a . By definition , eigenvectors are normalized , i . e . , the mobility profile also represents the normalized distribution of square displacements in mode k . The reciprocal λk−1 serves as the weight of mode k , such that the slower modes , also called softer modes , make larger contributions to observed dynamics . The mobility of residue i driven by a subset of m soft modes is found from the weighted average ( Figure 2b ) ( 1 ) The modes predicted by the ANM for the ATPase domain both in NEF-bound and –free forms were compared to the experimentally measured changes in structure ( designated by a 3N-dimensional deformation vector d ) using two metrics: ( i ) the correlation cosine ( |v ( k ) ·d|/|d| ) between the kth ANM eigenvector v ( k ) ( k = 1 , … , 3N-6 ) and d , and ( ii ) the cumulative overlap achieved by the m softest modes [26] , ( 2 ) The deformation d , is obtained by superposing the known NEF-bound and -free structures of ATPase domain and evaluating the differences in the Cα-coordinates . Kabsch's algorithm [27] is used for optimal superposition that eliminates rigid-body translations and rotations . The ET method [28] identifies conserved residues using the MSA-derived phylogenetic tree for a given family . The application of the procedure to the Hsp70 family of chaperones is outlined in Figure 3 , and details can be found in previous work [28] , [29] . In summary , the method consists of three steps: ( i ) the phylogenetic tree is partitioned into multiple levels [30] as indicated by the vertical bars in Figure 3a; ( ii ) at each level , sequences are grouped into classes , each being characterized by a “class consensus sequence” . The consensus sequences are cross-examined to identify fully conserved ( across classes ) and class-specific or trace residues ( conserved within classes but not across classes ) . The ET sequence for the particular level lists the fully conserved residues by their single-letter code , the trace residues by the symbol ‘X’ , and the remaining residues as blank; and ( iii ) The ET sequences generated at each level are organized in rows ( Figure 3b ) . An ET rank ( leftmost column ) is assigned to each residue . A fully conserved residue is assigned the highest rank ( rank of 1 ) . In the present case , Gly201 is the only residue with ET rank 1 , i . e . , it is fully conserved among the set of 1627 sequences ( see SM Figure S3 for a larger version of this panel ) . The conservation of a given residue in all subfamilies is a very strict condition when large sets of aligned sequences are considered . This limitation restricted the previous applications of the ET method to MSA of 100 and 200 sequences [31] . To adapt the ET method and its variations [32]–[34] to our dataset of >1 , 600 sequences , we relaxed the condition for defining an ET residue from conservation across “all” members in a given level to “90%”of members , and we allowed for gaps [34] . The ET method identifies conserved residues , but does not provide information on co-evolutionary relations between residues . Co-evolving residues are usually indicative of structural or functional constraints [35]–[38] . We adopted the MI content as a measure of the degree of intra-molecular co-evolution between residues in the Hsp70 ATPase domain [37] , [39]–[41] . In this method , each of the N columns of the MSA is considered as a discrete random variable that takes on one of the 20 amino-acid types , or an insertion ( gap , as the 21st type ) , with some probability . The MI associated with the ith and jth sequence positions is defined as an N×N matrix ( for a MSA of N columns ) of the form ( 3 ) where P ( xi , yj ) is the joint probability of observing amino acid types x and y at the respective sequence positions i , and j; P ( xi ) is the marginal/singlet probability of amino acid of type x at the ith position . I ( i , j ) varies in the range [0 , Imax] , where the lower and upper limits correspond to fully uncorrelated and most correlated pairs of residues .
Organisms comply with the evolutionary pressure to maintain their phenotype by genotypic variations that are compensated or correlated as needed , conserving certain sequence fragments vital to preserving their functions [57] . Understanding the co-evolving and conserved sequence patterns in modular domains is an interesting problem in its own right [58] , [59] . Understanding these patterns in the light of structural data , if available , provides us with further insights into shared mechanisms of interactions that form the molecular basis of the biological function of such modular domains . The Hsp70 ATPase domain is such a modular protein common to functionally diverse actin , hexokinase , and Hsp70 protein families [60] . The present combined analysis of structure-encoded dynamics and sequence evolution for Hsp70 ATPase domain discloses a subtle interplay between conserved interactions and those involving co-evolved residues . Conserved interactions define generic properties of the Hsp70 ATPase domain: these include the concerted dynamics of its four subdomains , which allow for sampling functional conformations ( e . g . , that stabilized upon NEF binding , allowing for ADP release; shown in Figure 3 ) , and the physicochemical events ( ATP hydrolysis ) at the nucleotide-binding site . Those residues involved in NEF recognition , on the other hand , show low-to-moderate conservation , but exhibit a remarkably high tendency to co-evolve , or undergo correlated mutations , again to achieve specific NEF-dependent recognition and binding activities . Interestingly , NEF residues that interact with the Hsp70 ATPase domain appear to be rather conserved ( Figure S11 ) to maintain this specificity . An observation of interest is the similarity between the interactions of the Hsp70 ATPase domain with different NEFs , in terms of structural dynamics . While Hsp70 ATPase domains are highly conserved both sequentially and structurally , the four NEFs examined have distinct structures and consequently different dynamics . The key point is that their binding to the ATPase domain involves in all cases the subdomain IIB of the ATPase domain , although not in exactly the same arrangement . Their binding to a common interfacial region on the ATPase domain point to a shared mechanism of interaction: The ATPase subdomain IIB is originally distinguished by its high mobility in the slowest mode , especially at the β-sheet E and the exposed loop connecting the two strands of this sheet; and after NEF binding , there is a significant suppression in its mobility . The conserved dynamics of the complexes suggests a role of subdomain IIB as an “adjustable handle” , which regulates the Hsp70 chaperone machine , to facilitate other proteins making use of its SBD . Many applications using the ANM have shown that the substrate recognition involves a region distinguished by its enhanced mobility in the most cooperative ( or softest ) modes , which enables the molecule to optimize its interactions with the substrate . Here we can see that the C-terminal part of helix 8 and the loop of β-hairpin E enjoy this type of high mobility/adaptability . On the other hand , substrate ‘binding’ may also involve more constrained residues in the close neighborhood , which may play a role in transmitting allosteric effects . In the opposite case of a binding site composed exclusively of floppy residues , the structural changes induced upon substrate binding could dissipate locally and not efficiently transmitted . In this respect , we propose that the involvement of residues such as Arg258 , Arg261 and Arg262 in subdomain IIB , or N57 , A60 and M61 in subdomains IB is critically important in establishing the communication between subdomains and transmitting allosteric signals between NEF-binding and nucleotide binding sites . A putative communication pathway that couples distant residues in different subdomains of the Hsp70 ATPase domain is suggested here by the structural mapping of correlated and conserved residues , which needs to be further established . Figure 6a displays those residues identified to be co-evolving . Notably , we observe several pairs making interdomain contacts , in addition to spatially distant residue pairs ( e . g . H23 in subdomain IA and N57 , A60 and M61 in subdomain IB correlated with R258 , R261 , E283 and D292 in subdomain IIB ) . In a recent study , R272 , R261 , Y15 and Y41 have been identified to play a central role in establishing the allosteric communication in the unbound Hsp70 ATPase domain , along with highly conserved residues K71 , R72 , E175 and H227 [45] . It remains to be seen if these central residues play a key role in mediating between these co-evolving , spatially distant residues . We also note that Smock et al . recently identified a sparse but structurally contiguous group of co-evolving residues at the interface between the ATPase domain and the SBD in Hsp70/110 protein family , which has been proposed to underlie the inter-domain allosteric coupling [61] , in support of the role of co-evolved residues in mediating allosteric signaling . Many recent studies have pointed out the validity of “pre-existing equilibrium” concept where a substrate or ligand simply selects from amongst an ensemble of conformations already accessible to the protein prior to binding [49] , [62]–[67] . The present results , and recent applications of ENMs , suggest that more important than the pre-existence of these ‘states’ , is the existence of energetically accessible ‘paths’ that provide access to those states , or the intrinsic tendency of the native structure to reconfigure towards such functional states . In terms of energy landscape description , what is needed is not the existence of multiple minima , the depths of which change upon ligand or substrate binding , but the existence of one or more directions of reconfigurations , or paths along the energy landscape , that are easily accessible to the protein and lead to the targeted ( functional ) conformer . The softest modes provide such paths . They define directions of motion in the space of collective coordinates , which incur a minimal energy ascent as the molecule moves away from its original energy minimum . They also present the best mechanisms of dissipating energy , if the system is perturbed . These are the modes that are being exploited when proteins bind ligands or substrates . Notably these functional conformations accessible near the native state can be observed by NMR residual dipolar coupling , as shown for Hsp70 ATPase domain by Zuiderweg and coworkers [52] . Figure 4 clearly shows that movements along a handful of modes satisfactorily ensures the passage to the alternative ( functional ) open form , and that the open form itself has a strong tendency to restore its conformation back to the closed form , in the absence of NEF . Protein-ligand binding interfaces and protein-protein contact interfaces are characterized by different sequence variation patterns . The protein-protein contact interfaces usually expose larger contact areas [68] and exhibit high mutation rates . Moreover , if the contact interface is a common recognition site for multiple targets ( possibly in different organisms ) , co-evolution is likely to occur among the binding residues to preserve specific interactions and conformations at the sequence motif . On the other hand , the protein-ligand interface is usually buried in the folded core of the protein; in contrast to protein-protein interaction , the protein-ligand interaction is usually characterized by higher specificity , requiring sequence conservation [28] , [29] . The Hsp70 ATPase domain exhibits patterns in close agreement with these general features: Its ligand ( nucleotide ) binding site essentially consists of highly conserved residues , which not only precisely coordinate the ligand , but also take part in a global hinge-bending region so that they are both chemically and mechanically required to be highly conserved . NEF recognition sites , on the other hand , exhibit much lower conservation properties; and in addition to their sequence variability , the subdomain IIB , which is observed to be most often involved in NEF binding , enjoys enhanced mobility . Briefly , global dynamics requirements entail residue conservation , and specific recognition entails sequence variation along with enhanced mobility . However , neither the sequence variability , nor the conformational mobility at NEF recognition sites , is random . The sequence variability takes place under unique restrictions , compensating mutations , as unraveled by the MI map . Conformational variability , on the other hand , is uniquely defined by the ATPase architecture , and precisely adept to accommodate the passage to the functional open state that is stabilized upon NEF binding . The ATPase domain uniquely juxtaposes such structure-encoded dynamics and sequence-specific interactions , which underlie its ubiquitous activities . In general , subdomains IA and IIA are more conserved and more rigid than subdomains IB and IIB [69] , as also indicated by the ET in Figure 3b; notably , they also serve as binding site to a number of proteins . For example , subdomain IA accounts for the binding of J-domain proteins [70]; subdomain IIA is reported to contain a putative binding site near its interface with subdomain IA ( V189-V195 ) to the chaperonin-containing TCP-1 [71] , and it is connected to the SBD by an inter-domain linker , which is considered important for the allosteric interactions between the two domains [72] , [73] . It remains to be seen if the correlated sites on Hsp70 ATPase domain emerging from the MI analysis play a role in the functional communication with other co-chaperones or the SBD . Extensive experimental studies have been performed to date with the E . coli Hsp70 , DnaK , to understand the molecular mechanism of activity of the molecular chaperones in the Hsp70 family . The analysis in the present paper will guide our interpretation of the NMR , FRET , and EPR data on different states accessible to DnaK . Each of these methods gives us a different window into the ensemble of conformational states populated in response to ATP , ADP and NEFs . Excitingly , a detailed chemical shift analysis of six different ligand bound states for the nucleotide-binding domain of DnaK , with and without the linker that connects it to the substrate-binding domain ( i . e . , 12 NMR samples compared pairwise and as a group ) has pointed to the same subdomain interface rearrangements indicated in the present study ( Zhuravleva & Gierasch , in preparation ) . Moreover , the NMR results point to the fundamental feature that subdomain IIB can undergo a hinge-like movement to enable nucleotide entry and release . It is this fundamental movement , intrinsic to Hsp70 ATPase domains , that different NEFs have exploited . They bind in different , sequence-specific ways , but modulate the same fundamental movement . Further detailed analysis of the ensemble distributions and rates of interconversion between states can be achieved using a synergistic battery of computational and experimental tools .
|
The heat shock protein 70 ( Hsp70 ) serves as a housekeeper in the cell , assisting in the correct folding , trafficking , and degradation of many proteins . The ATPase domain is the control unit of this molecular machine and its efficient functioning requires interactions with co-chaperones , including , in particular , the nucleotide exchange factors ( NEFs ) . We examined the molecular motions of the ATPase domain in both NEF-bound and -unbound forms . We found that the NEF-binding surface enjoys large global movements prior to NEF binding , which presumably facilitates NEF recognition and binding . NEF binding stabilizes the ATPase domain in an open form and thereby facilitates the nucleotide exchange step of the chaperone cycle . A series of highly correlated amino acids were distinguished at the NEF-binding sites of the Hsp70 ATPase domain , which highlights the adaptability of the ATPase domain , both structurally and sequentially , to recognize NEFs . In contrast , the nucleotide-binding residues are tightly held near a global hinge center and are highly conserved . The contrasting properties of these two groups of residues point to an evolutionarily optimized balance between conserved/constrained and co-evolved/mobile amino acids , which enables the functional interactions of the modular Hps70 ATPase domains with NEFs .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Discussion"
] |
[
"computational",
"biology/macromolecular",
"structure",
"analysis",
"computational",
"biology/macromolecular",
"sequence",
"analysis",
"evolutionary",
"biology/bioinformatics",
"computational",
"biology"
] |
2010
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Role of Hsp70 ATPase Domain Intrinsic Dynamics and Sequence Evolution in Enabling its Functional Interactions with NEFs
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In mammals , male sex determination is governed by SRY-dependent activation of Sox9 , whereas female development involves R-spondin1 ( RSPO1 ) , an activator of the WNT/beta-catenin signaling pathway . Genetic analyses in mice have demonstrated Sry and Sox9 to be both required and sufficient to induce testicular development . These genes are therefore considered as master regulators of the male pathway . Indeed , female-to-male sex reversal in XX Rspo1 mutant mice correlates with Sox9 expression , suggesting that this transcription factor induces testicular differentiation in pathological conditions . Unexpectedly , here we show that testicular differentiation can occur in XX mutants lacking both Rspo1 and Sox9 ( referred to as XX Rspo1KOSox9cKO ) , indicating that Sry and Sox9 are dispensable to induce female-to-male sex reversal . Molecular analyses show expression of both Sox8 and Sox10 , suggesting that activation of Sox genes other than Sox9 can induce male differentiation in Rspo1KOSox9cKO mice . Moreover , since testis development occurs in XY Rspo1KOSox9cKO mice , our data show that Rspo1 is the main effector for male-to-female sex reversal in XY Sox9cKO mice . Thus , Rspo1 is an essential activator of ovarian development not only in normal situations , but also in sex reversal situations . Taken together these data demonstrate that both male and female sex differentiation is induced by distinct , active , genetic pathways . The dogma that considers female differentiation as a default pathway therefore needs to be definitively revised .
Mammalian sex determination depends on the primary developmental decision of the gonad to differentiate as testis or ovary . The gonad develops as a bipotential organ with the capacity to respond to two different genetic stimuli: the activation of the SRY/SOX9 pathway that induces testicular development , or the expression of the R-spondin1 ( RSPO1 ) /beta-catenin pathway that regulates ovarian differentiation [1] . Indeed in humans and mice , male sex determination is initiated by the expression of the Y-linked gene SRY [2] , [3] , [4] . Sry expression in turn activates the transcriptional regulator SOX9 [5] . Subsequently , SOX9 initiates Sertoli cell differentiation , the supporting cell of the testicular sex cords [6] , [7] . Signaling pathways initiated in these cells contribute to the organization of the XY gonads [8] , as well as to the differentiation of other testicular cell lineages such as the Leydig steroidogenic cells [9] , [10] and the pro-spermatogonia [11] , [12] , ultimately leading to testis formation and , in turn , male development . In 46 , XY patients , loss-of-function mutations in SRY and SOX9 promote male-to-female sex reversal [13] , [14] , whereas translocations of the SRY locus to another chromosome can yield 46 , XX patients with female-to-male sex reversal [3] . Loss-of-function mutations [6] , [7] , [15] , [16] and gain-of-function mutations [4] , [17] , [18] of Sry and Sox9 have been generated in mouse models , showing that Sry and Sox9 are necessary and sufficient to induce testis differentiation and the associated male development . As a consequence , these genes have been considered as the master inducers of testis differentiation and male development . In the absence of SRY ( XX individuals ) , up-regulation of RSPO1 , an activator of the WNT/beta-catenin signaling pathway , promotes ovarian differentiation . Mutations in RSPO1 are responsible for skin disorders and female-to-male sex reversal in 46 , XX patients [19] . Similarly , ablation of Rspo1 in mice yields female-to-male sex reversal and promotes Sox9 up-regulation correlated with differentiation of Sertoli cells and formation of testis cords at birth [20] . This gonadal dysgenesis yields development of an ovotestis , a gonad displaying both testicular and ovarian regions [20] , [21] . Rspo1 expression in turn activates expression of Wnt4 [21] , another activator of the WNT/beta-catenin signaling pathway involved in ovarian differentiation [22] , [23] . When the canonical beta-catenin signaling pathway is activated in XY gonads , this induces male-to-female sex reversal indicating that this pathway acts on top of ovarian differentiation [23] . Indeed , activation of WNT/beta-catenin is required for expression of Foxl2 [24] , a transcription factor involved in folliculogenesis [25] , [26] and homeostasis of the ovary [27] . Thus Rspo1 appears to be the gene instructing the molecular network leading to ovarian development . Since ablation of Rspo1 promotes SOX9 expression concomitantly with Sertoli cell differentiation [20] , it was assumed that Sox9 is the sex reversal inducer in XX Rspo1KO mutants . We now show that i ) testicular differentiation occurs in XX Rspo1KOSox9cKO mutants indicating that neither Sry nor Sox9 are required for female-to-male sex reversals; ii ) testicular differentiation also occurs in XY Rspo1KOSox9cKO mutants indicating that Rspo1 is required for male-to-female sex reversal in XY Sox9cKO mutants .
Sox9 is required for Sertoli cell differentiation , testis formation and male development . Indeed , deletion of Sox9 in XY Sox9fl/fl; Sf1:creTg/+ , ( referred to as XY Sox9cKO ) triggers male-to-female sex reversal [16] . However the factor ( s ) inducing sex reversal in XY Sox9cKO remained to be identified . Given ( i ) the prominent role of RSPO1 , an activator of beta-catenin signaling , in female sex determination [19] , and ( ii ) the fact that ectopic activation of beta-catenin in XY gonads can induce male-to-female sex reversal [23] , we hypothesized that Rspo1 expression induced male-to-female sex-reversal in XY Sox9cKO gonads . According to this scenario , neither testicular ( which is Sox9-dependent ) nor ovarian ( which is Rspo1/beta-catenin-dependent ) differentiation should occur in XY Sox9cKO gonads additionally lacking Rspo1 . To test this hypothesis , we have generated and analyzed double loss-of-function mice ( i . e . XY Rspo1−/−; Sox9fl/fl; Sf1:creTg/+ , referred to as XY Rspo1KOSox9cKO ) . Since previous results have shown that sex reversal can appear quite late during fetal development [20] , [22] , we first analyzed adult stages when the sexual development is likely to be completed . At P60 ( postnatal day 60 ) , the anogenital distance in XY Rspo1KOSox9cKO mice was equivalent to that of XX controls but the internal genitalia contained both male and female organs including oviducts , uterine horns and vaginal tissues , as well as epididymides , vasa deferensia , seminal vesicles and prostate ( Figure S1 ) . The XY Sox9cKO developed as ovaries ( Figure 1b , 1g , 1l , 1q and Figure S1 ) , as expected from a previous report [16] . Interestingly , XY Rspo1KOSox9cKO gonads developed as hypoplastic testes containing well-defined seminiferous tubules as evidenced by histological analysis ( Figure 1c , 1h and Figure S1 ) . We next examined whether the supporting cells forming the seminiferous tubules differentiated as granulosa cells , the ovarian supporting cells expressing FOXL2 [25] , [28] or as Sertoli cells expressing DMRT1 [29] . In P21 gonads , immunostaining experiments showed that the supporting cells forming the seminiferous tubules in XY Rspo1KOSox9cKO gonads were DMRT1-expressing Sertoli cells ( Figure 1r ) , even though SOX9 was clearly missing ( Figure 1m ) . However , a few FOXL2-positive granulosa cells were found within the alignment of the Sertoli cells forming the seminiferous tubules ( Figure 1m , r ) and in a few XY Rspo1KOSox9cKO mice ( 3 out of 18 ) , rare and abnormal follicles were observed ( Figure S2A ) . The mixed genetic background of Rspo1KOSox9cKO mice is a likely factor causing the variation of this phenotype . Altogether this shows that a genetic pathway activated by RSPO1 is required for the male-to-female sex-reversal of XY Sox9cKO and indicates that Sertoli cell differentiation and seminiferous tubules formation can occur in the absence of SOX9 . Our study also allowed us to evaluate the effect of Sox9 removal in a female-to-male sex reversal context ( i . e . in XX Rspo1KOSox9cKO ) . Given that homozygous mutations of Rspo1 promote Sertoli cell differentiation around birth , a process that is associated with Sox9 up-regulation in these cells [20] , we hypothesized that Sox9 is the inducing factor of testicular differentiation in XX Rspo1KO mice . If Sox9 is indeed the main switch for female-to-male sex reversal in XX individuals , one expects an impaired differentiation of Sertoli cell and seminiferous tubules in the absence of both Rspo1 and Sox9 in XX Rspo1KOSox9cKO gonads . Unexpectedly , at P60 , these XX double mutants displayed hermaphroditism of the reproductive tracts ( Figure S1 ) . Histological analysis revealed that XX Rspo1KOSox9cKO mice exhibited ovotestes with an extensive presence of sex cords ( Figure 1d , 1i and in Figure S2B , f ) as do XX Rspo1KOgonads ( shown in Figure S2B , S2e and in previous analyses [20] , [21] ) . Thus , the development of XX Rspo1KOSox9cKO mouse genitalia is indistinguishable from that of XX Rspo1KO mice indicating that the additional deletion of Sox9 in XX Rspo1KOSox9cKO gonads does not change the fate of XX Rspo1KO gonads . We next examined whether the supporting cells forming the sex cords differentiated as granulosa cells , the ovarian supporting cells expressing FOXL2 [25] , [28] , or as Sertoli cells expressing DMRT1 [29] . In three weeks old mice ( P21 ) , Sox9-depleted cells forming the seminiferous tubules generally lacked the follicular cell marker FOXL2 and instead expressed DMRT1 ( Figure 1n , 1s ) . These data clearly indicate that Sertoli cell , seminiferous tubule and testis differentiation can occur in the absence of Sry and Sox9 in XX Rspo1KO gonads . Previous studies clearly show that the development of male genitalia depends on androgens secreted by the embryonic testis [30] . In XX Rspo1KO gonads , steroidogenic cells appear before Sertoli cell differentiation [20] , [31] and this was also observed in Wnt4KO gonads [22] , [32] , Wnt4 being up-regulated upon Rspo1 expression in XX gonads [20] , [21] . In addition , lack of Wnt4 expression was shown to allow ectopic migration of steroidogenic cells from the neighboring adrenals into gonads [32] , [33] and subsequent androgen synthesis [34] , which explains the development of male genitalia in these mutants . When investigating whether steroidogenic cells were present in XX and XY Rspo1KOSox9cKO gonads , we found that P450Scc , a gene encoding for a precursor involved in androgen synthesis was expressed at 14 . 5 dpc in XY controls , XY and XX Rspo1KOSox9cKO gonads and XX Rspo1KO gonads , but not in XX controls ( Figure S3A ) . However , Cyp21 , a marker for adrenal cells [35] , was not strongly expressed in Rspo1KOSox9cKO gonads at 13 . 5 dpc ( Figure S3A ) , suggesting that the steroidogenic cells in Rspo1KOSox9cKO gonads , did not come from the arenals or , alternatively , have undergone reprogramming as Leydig cells . Whatever the situation , it is likely that male hormones synthesized in the developing mutant gonads can contribute to stimulate epididymides , vasa deferentia and seminal vesicles development . We next investigated the timing of testicular cord formation in XY and XX Rspo1KOSox9cKO gonads . In wild-type embryos , the earliest morphological sign of testis development occurs at 12 . 0–12 . 5 dpc when testis cord are formed [36] . Accordingly at 13 . 5 dpc , the testis cords were highlighted in XY controls by the prominent expression of SOX9 and AMH , two markers of Sertoli cells ( Figure 2a ) . In contrast , Rspo1KOSox9cKO gonads did not show a clear testicular organization as they lack AMH at 13 . 5 dpc ( Figure 2b , 2c , 2f , 2g ) . As AMH synthesis and secretion by Sertoli cells promotes the elimination of the female reproductive tract during embryogenesis [37] , the absence of AMH in Rspo1KOSox9cKO gonads provides an explanation for the maintenance of the Mullerian derivatives ( oviducts , uterine horns and vaginal tissues ) in these mutant mice . In addition , Sertoli cell differentiation is delayed in gonads lacking both Sox9 and Rspo1 , as indicated by the maintenance of SRY expression , in the XY Rspo1KOSox9cKO gonads at 13 . 5 dpc ( Figure 2f ) , a stage at which SRY expression has already ceased for one day in the control situation [38] , [39] , [40] . Along these lines , the maintenance of SF1 expression in XX Rspo1KOSox9cKO gonads at 13 . 5 dpc ( Figure 2j , 2k ) , a factor whose expression is normally down-regulated between 13 . 5 and 16 . 5 dpc in the ovary [41] ( Figure 2l ) , also suggests that the XX Rspo1KOSox9cKO gonads are still undifferentiated or have differentiated as testis . However , with respect to the latter , the absence of AMH expression shows that no Sertoli cell differentiation has occurred ( Figure 2c , 2g ) . Altogether these data indicate that the Rspo1KOSox9cKO gonads are still undifferentiated at 13 . 5 dpc . The first signs of Sertoli cell differentiation appeared at 16 . 5 dpc in Rspo1KOSox9cKO gonads , with some rare DMRT1-positive cells in comparison to XY controls ( Figure S3B ) . Then , few rudimentary testis cords were observed around 17 . 5 dpc ( Figure S3B ) . At P0 , Sertoli cells aligned to form sex cords as evidenced by the localization of DMRT1 positive-cells ( Figure S4A c , d ) . Quantitative PCR experiments further confirmed that Dmrt1 expression was strongly expressed in XY Rspo1KOSox9cKO gonads and weakly in XX Rspo1KOSox9cKO gonads at P0 , highlighting that more Sertoli cells were present in XY Rspo1KOSox9cKO gonads in comparison to XX Rspo1KOSox9cKO gonads ( Figure S4C ) . In addition , some FOXL2-positive cells were also detected in Rspo1KOSox9cKO gonads ( Figure S4A c , d ) . However , quantitative PCR experiments showed that Foxl2 expression was significantly reduced in comparison to XX control or XY Sox9cKO gonads ( Figure S4B ) as expected for a gonad developing as ovotestis or testis . We then studied SDMG1 expression , a cytoplasmic marker of Sertoli cells and of granulosa cells when follicles form ( Best et al . 2008 ) . Using this marker , sex cords were evident at P0 ( Figure 3c , 3d and Figure S5c , S5d ) and , at puberty ( P12 ) , development of the seminiferous tubules appeared complete in Rspo1KOSox9cKO gonads ( Figure 3h , 3i and Figure S5h , S5i ) . At puberty , androgen receptor ( AR ) immunostaining indicated that , in addition to Sertoli cells , peritubular myoid and Leydig cells were also present both in XY Rspo1KOSox9cKO testes ( Figure 4i ) and in testicular parts of the XX Rspo1KOSox9cKO ovotestes ( data not shown ) . In addition , follicle development appeared at P12 in XX Rspo1KOSox9cKO ovotestes and XX control ovaries ( Figure S2B d , f ) . Together our results indicate that seminiferous tubule development is delayed in the absence of Sox9 and Rspo1 , thereby explaining the small size of the XY Rspo1KOSox9cKO testes ( Figure 1c ) . We next investigated whether the Sertoli cells that differentiate in the Rspo1KOSox9cKO gonads can support germ cell differentiation . Since XX germ cells cannot survive in a testicular environment [42] , [43] , the analysis was only carried out in XY Rspo1KOSox9cKO gonads . In the normal fetal testis , following Sertoli cell differentiation , prospermatogonia become quiescent from 14 . 5 dpc and express the multipotency marker Oct4 until 17 . 5 dpc [44] . At that time , Cyp26b1 , a protein involved in retinoic acid degradation , contributes to prevent germ cells from entering meiosis [45] , [46] . As expected , the majority of prospermatogonia in XY Rspo1KOSox9cKO gonads expressed Oct4 at 14 . 5 dpc ( Figure S6o ) . Nonetheless , some cells had already committed to meiosis ( Figure S6k ) and expressed the meiotic marker Stra8 [47] , possibly because of the low level of Cyp26b1 expression in XY Rspo1KOSox9cKO gonads ( Figure S6g ) . The reduced level of Cyp26b1 expression is however not sufficient to allow all germ cells to enter meiosis in XY Rspo1KOSox9cKO gonads . At P10 , GATA1 , Androgen Receptors ( AR ) and Clusterin ( Clu ) were normally expressed in Sertoli cells of XY Rspo1KOSox9cKO gonads ( Figure 4c , 4i , 4l ) , suggesting that these cells have acquired their identity and may be capable to support spermatogenesis . Accordingly , XY germ cells had committed to meiosis at P10 , as assessed by immunodetection of the pre-meiotic and meiotic markers STRA8 and γH2AX , respectively ( Figure 4c , 4f , 4i ) . However , later stages of spermatogenesis cannot however be analyzed , as hypoplasia of germ cells occurred within the seminiferous tubules of adult XY Rspo1KOSox9cKO gonads ( Figure 1h and Figure S1m ) , most likely because of cryptorchidism . Interestingly , we found that AMH was expressed in Sertoli cells of both XX and XY Rspo1KOSox9cKO gonads at P12 ( Figure 5A ) . Given that ( i ) Amh is a target-gene of SOX9 [48] , [49] , ( ii ) Amh expression can be induced by SOX8 [50] and SOX10 [51] , and ( iii ) Sox10 ectopic up-regulation in XX gonads can induce testis differentiation [51] , we hypothesized that a Sox factor distinct from Sox9 could have induced late AMH expression in Rspo1KOSox9cKO gonads and delayed testicular differentiation . In agreement with this possibility , expression of both Sox8 and Sox10 was activated in Rspo1KOSox9cKO mutants at P12 and P0 respectively ( Figure 5B , 5C ) . Previous data have shown that Sox8 becomes crucial from 14 . 5 dpc onwards , for the maintenance of testis development [52] , suggesting that Sertoli cell differentiation can be induced by Sox genes other than Sox9 during late embryogenesis . However , the function of these Sox genes during late development is likely not sufficient to replace the role of Sox9 in early Sertoli cells development , thus leading to the formation of an hypoplastic testis , as is the case in the XY Rspo1KOSox9cKO mice . To date , the only factors that have been shown to be able to induce Sertoli cell differentiation are Sox genes [51] , [53] , while other factors such as Dmrt1 are required after birth ( P7 ) for the maintenance of Sertoli cell identity [54] . Further studies are required to address whether DMRT1 is able to allow Sertoli cell differentiation from undifferentiated supporting cells . Given that Sox9 expression is controlled by Wt1 when Sry expression has ceased [55] , we can speculate that Wt1 might also be involved in Sox8 and Sox10 expression in these mutants . Furthermore , FGF9 or PGD2 , two secreted factors synthesized in the undifferentiated gonads , [56] , [57] can also contribute to Sertoli cell differentiation [58] , [59] , [60] . Whether Wt1 , PGD2 or FGF9 signaling also regulate Sox8 and Sox10 remains to be investigated . In addition , when XX and XY Rspo1KOSox9cKO gonads are compared at the same stage , XY gonads always appear more masculinized than XX gonads ( Figure 1 , Figure 3 , Figure S1 , Figure S3 , Figure S5 ) , because they contain more sex cords/seminiferous tubules . At a molecular level , the main difference between XX and XY Rspo1KOSox9cKO gonads is the expression of SRY in XY gonads ( Figure 2 ) . Indeed , SRY expression is maintained in XY Rspo1KOSox9cKO gonads at 13 . 5 dpc , while at this time point its expression has ceased in XY control gonads . This suggests that SRY participates in the masculinization of the XY Rspo1KOSox9cKO gonads by inducing the expression of genes other than Sox9 to promote sex cord formation . In summary , here we have shown that ( i ) both SRY and SOX9 are dispensable for female-to-male sex reversal in Rspo1KO , ( ii ) RSPO1 signaling is required for male-to-female sex reversal in Sox9cKO , ( iii ) Sertoli cell differentiation and seminiferous tubule formation can occur in the absence of SOX9 , possibly because of a functional redundancy with other SOX proteins such as SOX8 and SOX10 . Indeed , ectopic expression of Sox10 in XX gonads has been shown to promote testicular differentiation [51] . Altogether these data show that SRY and SOX9 are not the only masculinizing factors , since other SOX proteins can induce female-to-male sex reversal in pathophysiological conditions ( Figure 6 ) . Following Sertoli cell differentiation , DMRT1 expression becomes required for the maintenance of the Sertoli cells and the testicular tissue [54] . Furthermore , our data suggest that the feminizing factors remaining in Rspo1KO mice can be overtaken by SOX proteins , when they are activated in XX gonads . Testicular differentiation in the absence of Rspo1 expression in XY Sox9cKO gonads was unexpected since female development is thought to be a default pathway [61] , [62] . Our results imply that instead the female pathway needs to be activated . Therefore our genetic study suggests that mammalian sex determination is regulated by a finely tuned balance between two main factors [56] , [63] , which are the SOX genes on the one hand and the RSPO1/WNT/beta-catenin signaling pathway on the other hand .
The experiments here described were carried out in compliance with the relevant institutional and French animal welfare laws , guidelines and policies . They have been approved by the French ethics committee ( Comité Institutionnel d'Ethique Pour l'Animal de Laboratoire; number NCE/2011-12 ) . All mouse lines were kept on a mixed 129SV/C57BL6/J background . Rspo1−/− , Sox9flox/flox mice and Sf1:creTg/+ transgenic mice ( a kind gift from Keith Parker ) were described previously [64] . Rspo1−/− male were mated with Sox9flox/flox; Sf1:creTg/+ female [16] to obtain Rspo1+/−; Sox9flox/+; Sf1:creTg/+ females and Rspo1+/−; Sox9flox/+ males . Matings between these littermates allowed us to generate Rspo1−/−; Sox9fl/fl; Sf1:creTg/+ mice , referred to as Rspo1KO Sox9cKO mice , and controls . Gonad samples were collected from timed pups ( day of birth = P0 ) . Genotyping was performed as described [7] , [20] , [64] using DNA extracted from tail tip or ear biopsies of mice . The presence of the Y chromosome was determined as described previously [65] . Pax6 primer set ( 5′-GCAACAGGAAGGAGGGGGAGA-3′; 5′-CTTTCTCCAGAGCCTCAATCTG-3′ ) was included in each PCR reaction as an internal control . Urogenital organs were dissected , fixed in Bouin's solution overnight , and then embedded in paraffin . Microtome sections of 5 µm thickness were stained with periodic acid Schiff ( PAS ) or hematoxylin and eosin ( H&E ) according to standard procedures . Pictures were taken with an Axiocam mrm camera ( Zeiss ) and processed with Adobe Photoshop . Gonad samples were fixed with 4% ( w/v ) paraformaldehyde overnight and then processed for paraffin embedding . Gonad samples for cryosections were successively fixed for 2 hours in 4% ( w/v ) paraformaldehyde , washed in cold phosphate-buffered saline ( PBS ) , equilibrated in 10% ( w/v ) sucrose solution during 3 hours , then in 30% ( w/v ) sucrose solution overnight at 4°C , embedded in Cryomount ( Histolab ) and stored at −80°C . For paraffin-embedded and Cryomount-embedded samples , sections of 5 and 8 µm thickness were processed , respectively . The following dilutions of primary antibodies were used: AMH/MIS ( C-20 , sc-6886 , Santa Cruz ) , 1∶200; AR ( sc-816 , SantaCruz ) , 1∶100; DMRT1 ( kindly provided by David Zarkower ) , 1∶500; FOXL2 ( ab5096 , Abcam ) , 1∶250; γH2AX ( U5-636 , Upstate ) , 1∶500; GATA1 ( sc-265 , SantaCruz ) , 1∶50; SDMG1 ( a kind gift from Ian Adams ) 1∶2000; SF1 ( kindly provided by Ken Morohashi ) 1∶1500; SOX9 ( HPA-001758 , Sigma ) 1∶250 and SRY [59] , [66] 1∶100 , STRA8 ( ab49602 , Abcam ) , 1∶100 . Counterstain with 4′ , 6-diamidino-2-phenylindole ( DAPI ) was used to detect nuclei ( in blue ) . Fluorescent studies were performed with a motorized Axio ImagerZ1 microscope ( Zeiss ) and pictures were taken with an Axiocam mrm camera ( Zeiss ) and processed with Axiovision LE . Embryos were fixed with 4% paraformaldehyde ( PFA ) in 1×PBS at 4°C overnight . Further processing of embryos and in situ hybridization were carried out essentially as described [67] . Sox9 riboprobe was synthesized according to [68] and Sox8 to [69] , P450scc , Stra8 and Oct4 riboprobes synthesis were carried out as described previously [20] . In situ hybridisation ( ISH ) with digoxigenin–labelled probes was performed as described [70] , using 10 µm–thick cryosections . Each experiment was repeated on at least two gonads . Post–hybridization washes were done in 100 mM maleic acid pH7 . 5 , 150 mM NaCl , 0 . 1% ( v/v ) tween–20 ( MABT ) . To increase the sensitivity , 5% ( v/v ) polyvinyl alcohol ( Sigma ) was added to the staining solution [71] . Nuclei were counterstained with DAPI diluted in the mounting medium at 10 µg/ml ( Vectashield , Vector laboratories ) . ISH signals corresponding to Clu-positive cells were converted into a red false color on the merged pictures . The plasmids containing Lgals1 ( 366 bp–long; exons 2–4; MGI:96777 ) or Clu ( 942 bp–long; exons 5–9; MGI:88423 ) cDNA fragments were linearized and used as a templates for the synthesis of the sense or antisense riboprobes . Individual gonads were dissected in PBS from P0 animals ( day of birth ) and immediately frozen at −80°C . RNA was extracted using the RNeasy Qiagen kit , and reverse transcribed using the RNA RT–PCR kit ( Stratagene ) . Primers and probes were designed at Roche Assay design center ( https://www . rocheappliedscience . com/sis/rtpcr/upl/adc . jsp ) . Primers are 5′-TCCTCCTCAGACCGCTTTT-3′ and 5′-CCTGGTTCATCATCGCTAATC-3′ ( probe 95 ) for Hprt1 , and 5′-ATGTCAGATGGGAACCCAGA-3′ and 5′-GTCTTTGGGGTGGTTGGAG-3′ ( probe 21 ) for Sox10 , 5′-aagaagtgcagcctgattgc-3′ and 5′-ggtggctgatacccagttct-3′ ( probe 40 ) for Dmrt1 , and 5′-ggcgtcgtgaactcctaca-3′ and 5′-tgcagatgatgtgcgtgag-3′ ( probe 51 ) for Foxl2 . All real-time , quantitative , PCR assays ( QPCR ) were carried out using the LC-Faststart DNA Master kit Roche , according to the manufacturer's instructions . QPCR was performed on cDNA from one gonad and compared to a standard curve . QPCR were repeated at least twice . Relative expression levels of each sample were quantified in the same run , and normalized by measuring the amount of Hprt1 cDNA ( which represents the total amount of gonadal cells ) . For each genotype ( n = 6 individuals ) , the fold-change was the mean normalized expression levels divided by the mean normalized expression levels of the XY samples considered as the reference . Graphs illustrate fold-changes +1 s . e . m . The results were analyzed using Graphpad for statistical significance that was assessed using one-way ANOVA followed by Tukey-Kramer post-test for selected pairs of genotypes . Asterisks indicate : * p<0 . 05 , ** p<0 . 01 and *** p<0 . 001 .
|
Mammalian sex determination is controlled by the paternal transmission of the Y-linked gene , SRY . Using mouse models , it has been shown that the main , if not the only , role of Sry is to activate the transcription factor Sox9 , and these two genes are necessary and sufficient to allow male development . Indeed , defects in Sry and/or Sox9 expression result in male-to-female sex reversal of XY individuals . In XX individuals , Rspo1 is important for ovarian development as evidenced by female-to-male sex reversal of XX Rspo1 mutants . Since testicular differentiation appears concomitantly with Sox9 expression , it was assumed that Sox9 is the inducer of testicular differentiation in XX Rspo1 mutants . Our genetic study shows that i ) neither Sry nor Sox9 are required for female-to-male sex reversals; ii ) other masculinizing factors like Sox8 and Sox10 are activated in sex reversal conditions; iii ) Rspo1 is the main effector of male-to-female sex reversal in the XY Sox9 mutants . Together these data suggest that male and female genetic pathways are both main effectors involved in sex determination and that the long-standing dogma of a default female pathway should definitively be revised .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"animal",
"models",
"developmental",
"biology",
"animal",
"genetics",
"model",
"organisms",
"mouse",
"genetics",
"biology",
"genetics",
"of",
"disease",
"genetics",
"and",
"genomics",
"cell",
"fate",
"determination"
] |
2012
|
Testicular Differentiation Occurs in Absence of R-spondin1 and Sox9 in Mouse Sex Reversals
|
Increased expression of Notch signaling pathway components is observed in Kaposi sarcoma ( KS ) but the mechanism underlying the manipulation of the canonical Notch pathway by the causative agent of KS , Kaposi sarcoma herpesvirus ( KSHV ) , has not been fully elucidated . Here , we describe the mechanism through which KSHV directly modulates the expression of the Notch ligands JAG1 and DLL4 in lymphatic endothelial cells . Expression of KSHV-encoded vFLIP induces JAG1 through an NFκB-dependent mechanism , while vGPCR upregulates DLL4 through a mechanism dependent on ERK . Both vFLIP and vGPCR instigate functional Notch signalling through NOTCH4 . Gene expression profiling showed that JAG1- or DLL4-stimulated signaling results in the suppression of genes associated with the cell cycle in adjacent lymphatic endothelial cells , indicating a role for Notch signaling in inducing cellular quiescence in these cells . Upregulation of JAG1 and DLL4 by KSHV could therefore alter the expression of cell cycle components in neighbouring uninfected cells during latent and lytic phases of viral infection , influencing cellular quiescence and plasticity . In addition , differences in signaling potency between these ligands suggest a possible complementary role for JAG1 and DLL4 in the context of KS .
The Notch pathway is an evolutionarily conserved signaling mechanism that transduces signals between adjacent cells and has an established role in cell fate determination during development , tissue homeostasis and stem cell maintenance [1] , [2] . The Notch receptors ( NOTCH1–NOTCH4 ) and ligands ( JAG1 , JAG2 , DLL1 , DLL3 , and DLL4 ) are membrane-bound proteins that associate through their extracellular domains . Receptor-ligand interaction stimulates sequential proteolytic cleavage events at the receptor that release the intracellular domain ( ICD ) for translocation to the nucleus of the receiving cell . The ICD contributes to a ternary complex , involving the transcription factor CSL ( CBF-1 , Su ( H ) , Lag-1 ) , and upregulates transcription of target genes , primarily members of the HES and HEY families of transcriptional repressors [3] . The outcome of Notch signaling is cell-type dependent [4]–[6] and this pathway has essential roles during physiological and pathological angiogenesis [7] . NOTCH1 , NOTCH4 , JAG1 and DLL4 are expressed on vascular endothelium . New vessel “tip” cells form the guiding cells of endothelial sprouts and Notch signaling is essential for the specification of these cells . Ligand expression confers the tip phenotype and suppresses it in neighbouring receiving cells under physiological ( DLL4 ) and pathological ( JAG1 ) conditions [8]–[11] . Distinct spatial expression of DLL4 and JAG1 in normal developing vasculature suggests that ligand-specific outcomes of Notch signaling are required for normal development [12] , [13] . Cells adjacent to the tip cells form the stalk of the vessel and are subject to quiescent growth arrest . Notch signaling is also implicated in the maintenance of a reversible , quiescent state in stem cell progenitors [14] , [15] and is associated with growth arrest in a number of systems through manipulation of cell cycle components including minichromosome maintenance ( MCM ) proteins and cyclin dependent kinase inhibitors ( CDKIs ) [16]–[19] . Kaposi sarcoma herpesvirus ( KSHV , also called HHV-8 ) is an oncogenic γ-herpesvirus that is the etiological agent of Kaposi sarcoma ( KS ) , a neoplasm of lymphatic endothelial cells ( LEC ) [20] . KSHV is also associated with lymphoproliferations such as multicentric Castleman's disease ( MCD ) [21] . KS is an angioproliferative disease composed of sheets of spindle cells ( the KS tumour cells ) , an inflammatory infiltrate and abnormal slit-like blood vessels . All KS spindle cells are infected by KSHV [22] . During the establishment of host infection , two phases of viral infection exist: latent and lytic . The majority of spindle cells are latently infected and express a limited number of viral genes including the viral FLICE inhibitory protein ( vFLIP ) ; productive ( lytic ) viral infection is associated with expression of an increased number of viral genes including the multifunctional viral G protein-coupled receptor ( vGPCR ) [23] . KS lesions express elevated levels of Notch signaling components and experimental lesions appear sensitive to inhibition of this pathway [24] , [25] . The KSHV ORF50 gene product , RTA , has been shown to induce expression of HEY1 during lytic reactivation of the virus [26]–[28] , but a mechanism through which KSHV alters the expression of the other Notch-associated proteins , specifically during latency , has not been described . Here we show that KSHV specifically increases the expression of the Notch ligands JAG1 and DLL4 and the receptor NOTCH4 in LEC . The increase in JAG1 and DLL4 is attributable to the viral genes vFLIP and vGPCR , through mechanisms dependent on the NFκB and ERK pathways respectively . We demonstrate that JAG1 and DLL4 stimulate Notch signaling in adjacent LEC and alter the expression of cell cycle-associated genes . The suppression of a number of these genes is observed in LEC adjacent to vFLIP- and vGPCR-expressing cells and during KSHV infection of LEC; the effect of Notch on cell cycle components could offer a growth advantage to infected cells during the pathogenesis of KS . These data also suggest that DLL4 and JAG1 may have a similar role during sprouting lymphangiogenesis as has been observed in blood vessel endothelial cells during angiogenesis , where Notch induces quiescence in developing vascular sprouts .
KS has been shown to be sensitive to γ-secretase inhibition in murine models [24] , [25] . We have previously described the transcriptional signature of KSHV-infected LEC ( KLEC ) [29] and therefore analysed these data with respect to the expression of the core components of the Notch signaling pathway including HES and HEY Notch targets ( Figure 1A ) . This analysis indicated significant changes in the expression of specific members of the pathway at the mRNA level following KSHV infection ( false discovery rate threshold q≤0 . 005; Figure 1A ) . Significant increase in expression was restricted to three ligands ( DLL4 , JAG1 and DLL3 ) , the HES1 and HEY1 targets and the NOTCH4 receptor . The expression of all other Notch receptors was significantly decreased along with the remaining Notch target genes analysed . The expression of an additional Notch target , HES5 , and the Notch ligand DLL1 were not significantly altered . The established role of Notch signaling through these components during angiogenesis prompted us to consider these data with respect to a list of 79 angiogenesis-associated genes that are significantly altered in KLEC [30] . Notch ligands , JAG1 and DLL4 , the NOTCH4 receptor and the Notch target , HEY1 were among the most highly upregulated ( in bold in Figure 1A ) . The gene expression microarray ( GEM ) data for these components were validated at the mRNA and protein levels ( Figure 1B ) . Western blotting for NOTCH4 indicated a band at ∼70 kDa , the predicted size of the NOTCH4 intracellular domain ( N4-ICD ) ; no band corresponding to the full-length protein was observed . This indicated that the increased NOTCH4 could be involved in active Notch signaling in LEC . Notch pathway activation has been described in cells from plaque stage KS lesions [25] , suggesting that this pathway has a role in latent infection . NOTCH4 was the most significantly upregulated receptor in the context of primary KSHV infection . We therefore investigated whether the upregulation of HEY1 in KLEC was dependent on the expression of this receptor ( Figure 1C ) . LEC were transfected with siRNA against the NOTCH4 receptor , and NOTCH1 as a control , to achieve knock-down of the corresponding mRNA of 50% and 40% respectively ( Figure S1 ) . KSHV induced a reproducible five-fold increase in HEY1 expression . This increase was unaffected by the knock-down of NOTCH1 , but was reduced by approximately 60% in the presence of NOTCH4 siRNA ( Figure 1C ) . These data suggest KSHV increases HEY1 levels in LEC through a mechanism dependent on the NOTCH4 receptor . They also indicate that there is no functional redundancy between NOTCH1 and NOTCH4 in this system as the maintained expression of NOTCH1 was not sufficient to rescue HEY1 levels in the presence of NOTCH4 knock-down . HEY1 levels were not reduced to baseline levels in the presence of NOTCH4 siRNA; this may be attributed to a receptor-independent induction of HEY1 as a consequence of the expression of low levels of KSHV ORF50 during primary infection [28] , [31] , or due to incomplete knock-down of NOTCH4 . JAG1 is an NFκB-responsive gene [32] and is induced in endothelial cells through an NFκB-dependent mechanism [8] , [11] . Treatment of LEC and KLEC with a chemical inhibitor of the NFκB pathway , BAY11-7082 [33] , significantly reduced the basal and KSHV-induced expression of JAG1 in LEC ( Figure 2A ) . These data suggest that the NFκB pathway is important in the maintenance and induction of JAG1 levels . JAG1 expression is not induced in LEC following exposure to KLEC-conditioned media ( Figure S2A ) , suggesting that the KSHV-induced increase in JAG1 in LEC does not occur via a paracrine mechanism . Increased expression of JAG1 has been suggested to correlate with later-stage KS , specifically plaque and nodular lesions where latent KSHV infection predominates [25] , [34] . The restricted number of viral genes expressed during latency includes vFLIP , a potent activator of the NFκB pathway [35] . To examine whether vFLIP can induce JAG1 expression , LEC were infected with lentivirus expressing vFLIP . Increased expression of MHC-I in vFLIP-LEC was used as a control for vFLIP functional expression ( [29] , data not shown ) . Compared to control cells infected with pSIN lentivirus , levels of JAG1 mRNA were increased by approximately three-fold in vFLIP-expressing LEC; this induction was abrogated by BAY11-7082 ( Figure 2B ) . vFLIP increased JAG1 protein expression in LEC as measured by western blotting and increased JAG1 was also observed in the spindle-shaped cells characteristic of vFLIP infection ( Figure 2C ) . LEC infected with lentivirus expressing the lytic viral gene K15-P , another inducer of the NFκB pathway [36] did not induce JAG1 expression in LEC ( data not shown ) . Collectively , these data suggest that JAG1 expression is induced primarily by vFLIP through an NFκB-dependent mechanism . We next investigated whether vFLIP could induce Notch signaling in LEC by measuring levels of the Notch target , HEY1 . Expression of vFLIP induced a four-fold increase in HEY mRNA that was abrogated in cells treated with a γ-secretase inhibitor ( GSI-I ) ( Figure 2D ) , suggesting this induction of HEY1 may depend on canonical Notch signaling . The NOTCH4 receptor expression appeared to be most significant in the context of primary KSHV infection of LEC ( Figure 1C ) . We therefore investigated if the increase in HEY1 by vFLIP was dependent on NOTCH4 . vFLIP-expressing LEC had elevated levels of NOTCH4-ICD , detectable by western blot ( Figure 2E ) , and siRNA knockdown of NOTCH4 ( Figure S2B ) reduced HEY1 mRNA to near basal levels ( Figure 2E ) . These data suggest that vFLIP induces HEY1 expression by way of NOTCH4 . Using the co-culture assay described in Figure 2F , we investigated if vFLIP could induce Notch signaling between adjacent cells . HEY1 expression in the receiving cells was assessed by qRT-PCR and was found to be significantly increased in cells exposed to vFLIP or JAG1 donors ( Figure 2G ) . No significant change in another Notch target gene , HES1 , was observed . These data suggest that vFLIP can induce HEY1 expression in adjacent cells , through a mechanism involving JAG1 and NOTCH4 . DLL4 expression in LEC is not significantly affected by vFLIP ( Figure S3A ) suggesting an alternative mechanism for its induction in KLEC . The Notch pathway is required for arterial specification [37]–[39] , and DLL4 expression is essential for arterial patterning and lymphatic sprouting [40]–[42] . Activation of extracellular-signal regulated kinase ( ERK ) is also required for the arterial commitment of angioblasts [43] , [44] suggesting a functional link between these two pathways . siRNA knock-down of ERK1 and ERK2 ( Figure S3B ) significantly reduced KSHV-induced DLL4 expression in LEC ( Figure 3A ) suggesting a role for this pathway in the upregulation of DLL4 during viral infection . The KSHV vGPCR is a potent activator of ERK signaling [45] , [46]; levels of DLL4 were therefore investigated in LEC infected with lentivirus expressing vGPCR . Compared to cells expressing pSIN , levels of DLL4 mRNA were increased approximately three-fold in vGPCR-LEC . This increase was also observed at the protein level ( Figure 3B ) . vGPCR activates multiple signaling cascades [47] . To confirm that the induction of DLL4 by vGPCR is dependent on the ERK pathway , pSIN- and vGPCR-expressing LEC were treated with pharmacological inhibitors of the NFκB , ERK and PI3K pathways ( Figure 3C ) . DLL4 expression was significantly reduced following ERK pathway inhibition only and was unaffected by the BAY11-7082 compound . This agrees with our observation that vFLIP fails to induce DLL4 expression ( Figure S3A ) . Inhibition of the PI3K pathway did not affect the vGPCR-induced increase in DLL4; however , increased basal DLL4 expression was observed ( Figure 3C ) . This may reflect an antagonistic role for PI3K signaling in DLL4 levels in LEC , as has been observed during blood vessel specification [43] , [44] . Other KSHV genes can also activate ERK signaling . To investigate the specificity of DLL4 induction by vGPCR , LEC were infected with lentivirus expressing K15-P or Kaposin A as examples of genes known to activate ERK signaling during lytic and latent infection respectively [36] , [48] . Neither of these viral genes increased DLL4 levels ( Figure S3C ) . Collectively , these data suggest that vGPCR induces DLL4 expression through an ERK pathway-dependent mechanism . VEGF can induce DLL4 expression in blood vessel endothelial cells during physiological and pathological angiogenesis [49]–[53] and vGPCR can stimulate VEGF production [54] , [55] . We observed a two-fold increase in the expression of VEGF in vGPCR-expressing LEC ( Figure S3D ) ; however , LEC grown in vGPCR-conditioned media in the presence or absence of a VEGFR inhibitor , did not show increased DLL4 expression ( Figure S3E ) . LEC did not demonstrate increased levels of DLL4 in response to VEGF at concentrations previously reported to induce DLL4 expression [49] , [50] , [53] ( Figure S3F ) . These data suggest that the induction of DLL4 in LEC by vGPCR does not occur through a paracrine mechanism involving VEGF and that direct activation of ERK by vGPCR is sufficient to elevate levels of DLL4 in these cells . JAG1 expression can also be affected by ERK signaling [56] , so we investigated levels of JAG1 in vGPCR LEC . We observed reduced levels of JAG1 in these cells ( Figure S3G ) suggesting that vGPCR preferentially induces DLL4 and this may indicate a role for DLL4 during KSHV lytic infection . To begin to investigate this hypothesis , we expressed KSHV ORF50 in the BCBL1 PEL cell line , which is sufficient to reactivate KSHV from latency [57] . The induction of lytic infection was confirmed by measuring significantly increased expression of ORF50 and the late-lytic gene ORF26 . An accompanying 2 . 5-fold increase in vGPCR expression was also observed ( Figure S3H ) . Compared to control , ORF50 cells expressed significantly more DLL4 ( 1 . 8-fold , Figure 3D ) , while levels of JAG1 remained unchanged . The outcome of Notch signaling , including signaling strength , can be influenced by the type of ligand expressed [58] , [59] . These data suggest that DLL4 expression may have a role during the lytic phase of the KSHV cycle and complements signaling established by JAG1 during latency . DLL4 has been shown to elevate levels of both HES1 and HEY1 in HUVEC [53] , [60] , so we examined levels of these Notch targets in vGPCR-LEC . Levels of HEY1 and HES1 mRNA were increased about 2 . 5-fold in vGPCR-LEC and these increases were abrogated following GSI-I treatment ( Figure 3E ) . These data suggest the Notch pathway is involved in the upregulation of HES1 and HEY1 in response to vGPCR . siRNA-mediated silencing of NOTCH4 ( Figure S3I ) significantly reduced HEY1 expression ( Figure 3F ) , suggesting that vGPCR induces HEY1 through a canonical Notch signaling mechanism involving NOTCH4 and emphasising the importance of this receptor during KSHV infection . In agreement , vGPCR-expressing cells have elevated levels of NOTCH4-ICD protein indicating activation of this receptor ( Figure 3F ) . HES1 expression was not significantly reduced in the presence of NOTCH4 siRNA alone ( data not shown ) but combined knock-down of NOTCH1 and NOTCH4 significantly reduced HES1 levels and increased levels of NOTCH1-ICD protein were observed in vGPCR-LEC ( Figure S3J ) . These suggest that induction of HES1 in vGPCR-expressing LEC can occur through either NOTCH1 or NOTCH4 , indicating a specific role for NOTCH1 in DLL4-stimulated signaling . Utilising our co-culture assay , we examined levels of HES1 and HEY1 in cells adjacent to vGPCR or DLL4-expressing donors . Under both these conditions , HES1 and HEY1 were significantly increased ( Figure 3G ) . These data indicate that DLL4-stimulated Notch signaling can induce HES1 and HEY1 in adjacent LEC and that this signaling is mimicked by vGPCR . To investigate the role of DLL4- and JAG1-stimulated Notch signaling in LEC , we performed gene expression microarray ( GEM ) analysis on LEC co-cultured with DLL4- or JAG1-expressing donors . The ligands were expressed to equivalent levels in donor cells as analysed by western blot ( Figure S4A ) . Stringent selection ( false discovery rate threshold q<0 . 05 ) generated a list of the most significantly altered genes in the receiving cells as a consequence of exposure to DLL4 or JAG1 ( Table S1 ) . We confirmed the GEM data by validating members of this genelist by qRT-PCR and observed increased expression of CD38 and LYVE1 , and decreased levels of NRP1 , a known target of DLL4-induced Notch signaling [60] , [61] ( Figure S4B ) . We confirmed that receiving cells upregulated Notch target genes in response to both DLL4 and JAG1 compared to pSIN ( Figure 4A ) . In agreement with our previous data , DLL4 stimulated significant increase in HEY1 and HES1 expression ( approximately four-fold ) whereas JAG1 resulted in significant increase in HEY1 only ( nearly three-fold ) . These data indicate that DLL4 induced a more pronounced change in Notch target gene expression , which is reflected in the heatmaps from two HEY1 probes ( Figure 4A ) . Similarly , when the 165 genes most significantly altered in response to DLL4 are considered , ( Figure S4C and Table S2 ) ; these changes are mimicked in JAG1-stimulated cells but are less pronounced . Less stringent selection using an unadjusted P threshold of 0 . 005 generated a larger data set of significantly regulated genes on which gene ontology analysis was performed using GENECODIS [62] . This analysis indicated that , amongst genes suppressed in cells stimulated by both DLL4 and JAG1 , cell cycle , cell division and mitotic pathways were enriched . Specifically , cyclins ( CCN ) , cyclin-dependent kinase 1 ( CDK1 ) , mitotic arrest deficient-like 1 ( MAD2L1 ) and MCM proteins , were indicated by the GEM data as significantly down-regulated in both JAG1- and DLL4-receiving cells ( Figure 4B ) . The CKI p57Kip2 ( CDKN1C ) was uniquely upregulated by JAG1 ( Figure 4B ) ; no significant change in other CKIs , such as p21Cip1 , p27Kip1 or members of the INK4 family , was observed , despite associations with Notch signaling in other systems [16] , [63] . Significant down-regulation of CCNA1 , CCNB1 , CCNE1 , CCNE2 and CCNF; CDC2 ( CDK1 ) , MCM4 , MCM10 and MAD2L1 was confirmed by qRT-PCR for both JAG1- and DLL4-receiving cells ( Figure 4C , left panel ) . The JAG1-dependent increase in p57 predicted by GEM analysis was also confirmed ( Figure 4C , right panel ) . MAD2L1 is part of a six-gene expression signature , including the upregulation of HES1 , characteristic of quiescence triggered by a variety of arrest signals [64] . The co-ordinated expression of p57Kip2 and HES1 has also been associated with quiescence reversibility [14] , [15] . Collectively , these observations suggest a role for DLL4 and JAG1 in manipulating the cell cycle in adjacent LEC . To investigate whether the changes observed in Notch ligand-stimulated cells were recapitulated by vGPCR or vFLIP , we measured the expression of these genes in receiving cells co-cultured with vGPCR- or vFLIP-expressing LEC ( Figure 4D ) . We confirmed significant reduction in CCNA1 , CCNB1 , CCNE1 , CCNE2 , MAD2L1 and CDK1 in response to vGPCR and vFLIP co-culture respectively . CCNA1 expression was reduced in all four co-culture conditions . To investigate a role for the suppression of these genes in the context of KSHV infection , we measured their expression in KLEC and observed that these genes were significantly down-regulated ( Figure 4E ) , with the exception of the E-type cyclins ( not shown ) . Significantly reduced expression of CCNF , MCM4 and MCM10 was also observed in KLEC ( Figure S4D ) . These data suggest that cell cycle components are targets of Notch signaling in LEC and are suppressed in cells adjacent to those expressing KSHV viral genes , suggesting that Notch signaling may influence the cell cycle in cells adjacent to those infected by KSHV .
The expression of Notch signaling components has been reported in KS , but the molecular mechanisms underlying the activation of this pathway by KSHV have not been established . Here we show that KSHV manipulates canonical Notch signaling in LEC by increasing the expression of JAG1 and DLL4 through vFLIP and vGPCR . The vFLIP-induced increase in JAG1 occurs through an NFκB-dependent mechanism and mimics the induction of blood vessel tip cells during pathological angiogenesis by TNF [8] , [11] . This provides a new example of the manipulation of a host endothelial signaling mechanism by KSHV . vGPCR is a multifunctional protein , but here we show that its induction of DLL4 is specifically ERK-dependent . How ERK signaling relates to the Notch pathway in endothelial cells has previously been unclear and our data indicate a direct link between ERK and DLL4 expression in LEC . Interestingly , our data also indicate that the induction of DLL4 in LEC is unlikely to occur as a result of VEGF stimulation . This is also the first report of a functional association between KSHV and DLL4 . We show that the increase in levels of the Notch target gene , HEY1 , in KLEC occurs through NOTCH4 . Whereas there is an established functional association between DLL4 , NOTCH4 and NOTCH1 in terms of expression patterns [9] , [40] , [49] , [65] , JAG1 has been shown to be a ligand for multiple Notch receptors , but not directly for NOTCH4 [66] , [67] . The induction of HEY1 in response to vFLIP is dependent on NOTCH4 , confirming an association between JAG1 and NOTCH4 in LEC . We show that DLL4 can induce expression of an additional Notch target gene , HES1 , though a mechanism dependent on NOTCH1 and NOTCH4 , indicating a specific role for NOTCH1 in LEC . The outcome of Notch signaling , including signaling strength , can be influenced by the type of ligand expressed [59] . Using gene expression profiling , we show that the most significant changes in gene expression elicited by DLL4 in adjacent cells are more pronounced compared to the changes elicited in the same genes by JAG1 . HEY1 and HES1 are basic helix-loop-helix transcription factors that can heterodimerise to enhance Notch signaling effects [68]–[70] . The induction of both HES1 and HEY1 by DLL4 could explain why signaling induced by this ligand is more potent than JAG1 . Our data also indicate a distinct role for these Notch ligands during latent ( vFLIP ) and lytic ( vGPCR ) infection of LEC by KSHV . The majority of cells in KS are latently infected with virus , whereas lytic infection is short-lived and only accounts for a small percentage of cells [23] . In addition , vGPCR-induced transcripts are associated with limited temporal expression [71] . The periodic expression of DLL4 during lytic infection may contribute to “topping up” Notch signaling established by JAG1 during latency . The increased potency of DLL4-induced signaling may compensate for its potentially restricted expression to permit functional signaling . The functional outcome of DLL4-stimulated signaling is dose-dependent [40] , [65] , [72] , [73] and can operate through distinct spatial expression patterns . [13] , [58] . While complementary roles for DLL4 and JAG1 have been suggested during angiogenesis [58] , a mechanism through which expression of these ligands can be differentially regulated in this context has not been determined . Our work suggests that KSHV can establish differential upstream signaling events leading to the expression of DLL4 and JAG1 coincident with lytic and latent infection respectively . Gene ontology analysis of our expression profiling data did not indicate significant overall changes in angiogenesis-associated genes in either DLL4- or JAG1-stimulated cells . However , both ligands elicited significant suppression of the expression of cell cycle components in adjacent LEC . A number of these genes were also suppressed in LEC adjacent to vFLIP- and vGPCR-LEC and were down regulated in KLEC . Cyclin A1 ( CCNA1 ) expression was suppressed under all these conditions and has been indicated as a target of activation of NOTCH1 [74] . Cyclin A1 is functionally associated with multiple cell cycle components including CDK1 [75]–[77] , which is also suppressed in our system . Suppression of cyclin A induces cell cycle arrest in arterial endothelial cells [78] . The effect of the Notch pathway on the cell cycle has been associated with quiescence and reduced proliferation in a number of systems [15]–[19] , [61] , [63] , [79] , [80] . In the context of KS , suppression of cell cycle components could provide a growth advantage to infected ( signal generating ) cells over uninfected surrounding cells ( Figure 5 ) . Alternatively , instigation of Notch signaling in adjacent immune cells could halt them to provide a means of immune escape for the virus . To fully address the effect of KSHV-induced Notch signaling on modulation of the host immune response to KS would require an immunocompetent model of KS , which does not yet exist . Our data also show that JAG1-induced Notch signaling increases p57Kip2 expression in adjacent LEC . Inspection of the p57Kip2 promoter reveals two CSL binding sites ( data not shown ) , making this gene a potentially direct target for JAG1-induced activation of Notch in LEC . The co-ordinated expression of p57Kip2 and HES1 has been associated with quiescence reversibility [14] , [15] . Our predicted periodic expression of DLL4 provides the potential for co-ordination between p57Kip2 and HES1 in KS , suggesting a mechanism by which KSHV may influence the plasticity of the surrounding cells during lytic infection , thereby making them more susceptible to reprogramming by the virus [14] , [15] , [20] , [81] . Our co-culture model is representative of the “tip hypothesis” of branching angiogenesis , whereby the tip cells of developing vessels express ligand and signal to adjacent cells to adopt the quiescence-associated stalk phenotype [50] , [82]–[84] . The presence of specialised tip cells has not been described for developing lymphatic vessels [85] , but DLL4 has been implicated in lymphatic sprouting [42] . Our findings indicate a potential role for DLL4 and JAG1 in sprouting lymphangiogenesis . Furthermore , elucidation of the mechanism by which canonical Notch signaling is manipulated by KSHV in LEC raises the possibility that KS may be susceptible to treatment with the NOTCH1 Decoy [86] , an inhibitor of NOTCH1 and NOTCH4 signaling , shown to be effective in neuroblastoma and mouse mammary carcinoma xenografts . Anti-DLL4 antibodies have been reported to reduce tumour size in multiple tumour xenograft models [51] , [87] and could also be therapeutically relevant in KS treatment . Further studies modelling KSHV-induced oncogenesis in the context of KSHV deletion mutants [88] , [89] or in the presence of these Notch pathway inhibitors or would provide insight into this pathway as a possible target in the management of KS .
LEC were cultured as described and KSHV was produced from BCBL1 cells and used to infect LEC as previously described ( [29] , [30] and Protocol S1 ) . GFP expression in KLEC was used as an indicator of KSHV infection; GFP-positive KLEC was typically approximately 35% , three days post-infection ( p . i . ) . KSHV genes were cloned from the BC3 and BC1 PEL cell lines and were expressed using a modified pSIN-MCS lentiviral vector and produced in 293T cells as previously described [29] , [30] . DLL4 and JAG1 cDNAs were cloned from HUVEC cDNA into pSIN-MCS as described in Protocol S1 . Lentiviral copies per cell were determined by qPCR and a maximum of ten copies per cell were used to avoid cytopathic effects . All experiments shown were performed three days post-lentiviral infection ( p . i . ) . LEC were infected with the appropriate lentivirus as described to generate the ligand-expressing ( signal generating ) cells . LEC to be designated “receiving cells” were stained with CellTracker Green CMFDA ( Invitrogen ) diluted to a final concentration of 5 µM in Optimem ( Invitrogen ) . Receiving cells were mixed with ligand-expressing donor cells on 10 cm dishes at a ratio of 3∶1 [90] , [91] and co-cultured for 60 hours . Pure populations of signal-receiving cells were obtained by flow cytometric sorting of the CellTracker-labelled LEC ( MoFlo XDP , Beckman-Coulter ) directly into Qiazol Lysis Reagent ( Qiagen ) . RNA extraction was performed as described [30] . For GEM experiments , one10 cm dish was used per Affymetrix chip and 1 µg of total RNA was used to generate cDNA using T7-linked oligo ( dT ) primer and the custom SuperScript dscDNA synthesis kit ( Invitrogen ) . After second-strand synthesis , in vitro transcription was carried with biotinylated UTP and CTP using GeneChip® IVT Labeling Kit ( http://www . affymetrix . com/support/technical/technotes/ivt_technote . pdf ) LEC were seeded in 5×104 cells per well in six-well plates one day prior to transfection with 100 nM NOTCH1- or NOTCH4-tagetting or non-targeting siRNA ( OnTargetPlus SmartPool , Dharmacon ) . Transfections were performed using Oigofectamine reagent ( Invitrogen ) . Cells were infected with KSHV 48 hours post siRNA transfection or with the appropriate lentivirus 24 hours post-transfection . The following chemical inhibitors were used: BAY11-7082 ( NFκB pathway inhibitor , 5 µM ) , JNK inhibitor II ( 25 µM ) , SB202190 ( p38/MAPK inhibitor , 10 µM ) , UO126 ( MEK inhibitor , 10 µM ) , LY294002 ( PI3K inhibitor , 5 µM ) and γ-secretase Inhibitor I ( GSI-I , Z-Leu-Leu-Nle-CHO ( Nle = Norleucine ) , 5 µM ) , all from Calbiochem . For LEC and KLEC at 72 hours p . i . , and LEC infected with lentivirus , the inhibitors were added to the cells for 6 hours , apart from BAY11-7082 and LY294002 , which were added for 2 hours and 4 hours , respectively . Extraction of genomic DNA and RNA was performed using QIAamp DNA mini and RNEasy mini kits ( Qiagen ) respectively . DLL4 , JAG1 , NOTCH4 , HEY1 and HES1 mRNA levels were quantified by qRT-PCR using Taqman Gene Expression Assays ( Applied Biosystems ) . GAPDH was used as a housekeeping reference gene and quantified using the SYBR Green Master Mix ( Applied Biosystems ) and optimised forward and reverse primers at a final concentration of 300 nM . Levels of genes highlighted in the co-culture microarray were quantified in the same way as GAPDH ( primers listed in Table S2 ) . qPCR for lentiviral copy number was performed as described [30] . Cells were lysed on ice for 30 minutes in buffer ( PBS containing 1% NP40 and 0 . 1% SDS , supplemented with Protease Inhibitor Cocktail ( Sigma ) ) before clearing by centrifugation . Western blotting was performed as described [30] using equal amounts of total protein ( 20 µg–30 µg ) per sample . The following antibodies were used: goat anti-JAG1 ( C-20 , 1∶500 ) , rabbit anti-NOTCH4 ( H225 , 1∶200 ) from Santa Cruz Biotechnology; rabbit anti-DLL4 , rabbit anti-cleaved NOTCH1 ( both 1∶1 , 000 ) from Cell Signalling Technology; mouse anti-GAPDH ( 6C5 , 1∶5 , 000 ) from Advanced Immunochemical Inc . Secondary antibodies were from DAKO and used at a dilution of 1∶5 , 000 . For immunofluorescent assay ( IFA ) , cells were fixed and permeabilised using formalin 3 . 7% and PBS-T-0 . 1% Triton X-100 , and slides were stained as previously described [92] . The anti-JAG1 antibody was used at 1∶50 and anti-goat-FITC ( DAKO ) was used at 1∶200 . Images were taken using an UltraVIEW ERS confocal microscope ( Perkin Elmer ) . Affymetrix hgu133plus2 GEM data was background corrected , normalised and summarised using the robust multiarray average ( rma ) algorithm [93] , from the Bioconductor ‘affy’ package for R [94] . All subsequent analyses and plots show Log2 expression units . Statistical analyses , p-values and false discovery rates where shown were calculated using the ‘limma’ package , again from Bioconductor [95] . Where expression values are shown as a heatmap the data has been row scaled with standardised expression values ( Z-scores ) obtained for each probeset by subtracting the mean of each row and dividing this by the standard deviation . KLEC GEM profiles of six pairs of LEC and KLEC were generated and analyzed as described [29] . KLEC GEM data are available in the ArrayExpress database with accession numbers E-MEXP-561 . Co-culture GEM data have been submitted to Gene Expression Omnibus ( GEO ) and assigned accession number GSE16547 . All experiments were performed in independent replicates and error bars correspond to standard deviation from the mean . Statistical significance ( P values ) was calculated with a two-sided unpaired Student's t test . Statistical analysis of the KLEC GEM was performed as described using a moderated t statistic and a false discovery rate correction [29] .
|
Kaposi sarcoma herpesvirus ( KSHV ) is a tumour virus associated with Kaposi sarcoma ( KS ) . Most KS tumor cells are latently infected with the virus , while a small number are lytically infected and produce KSHV . The Notch signaling pathway is highly conserved and important in development and disease . Classical activation of this pathway occurs through direct interaction between ligands and receptors bound to the surface of adjacent cells and influences gene expression in cells receiving the signal . KS tumour cells express Notch pathway components and are sensitive to inhibition of Notch signaling , suggesting this pathway may be important in the development of KS; however , no mechanism behind the classical activation of Notch by KSHV has been established . We describe the molecular mechanisms through which KSHV hijacks the Notch signaling pathway by directly increasing the expression of two Notch ligands ( JAG1 and DLL4 ) through two KSHV genes expressed during latent and lytic infection , respectively . We show the effect of JAG1- and DLL4-stimulated signaling on gene expression in adjacent cells and show that both ligands affect cell cycle-associated genes and may co-operate to permit functional signaling in the context of both latent and lytic infection .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"oncology/sarcomas",
"cell",
"biology/cell",
"signaling",
"biochemistry/cell",
"signaling",
"and",
"trafficking",
"structures",
"virology/viruses",
"and",
"cancer",
"virology/effects",
"of",
"virus",
"infection",
"on",
"host",
"gene",
"expression"
] |
2009
|
KSHV Manipulates Notch Signaling by DLL4 and JAG1 to Alter Cell Cycle Genes in Lymphatic Endothelia
|
Noma ( cancrum oris ) is a gangrenous disease of unknown etiology affecting the maxillo-facial region of young children in extremely limited resource countries . In an attempt to better understand the microbiological events occurring during this disease , we used phylogenetic and low-density microarrays targeting the 16S rRNA gene to characterize the gingival flora of acute noma and acute necrotizing gingivitis ( ANG ) lesions , and compared them to healthy control subjects of the same geographical and social background . Our observations raise doubts about Fusobacterium necrophorum , a previously suspected causative agent of noma , as this species was not associated with noma lesions . Various oral pathogens were more abundant in noma lesions , notably Atopobium spp . , Prevotella intermedia , Peptostreptococcus spp . , Streptococcus pyogenes and Streptococcus anginosus . On the other hand , pathogens associated with periodontal diseases such as Aggregatibacter actinomycetemcomitans , Capnocytophaga spp . , Porphyromonas spp . and Fusobacteriales were more abundant in healthy controls . Importantly , the overall loss of bacterial diversity observed in noma samples as well as its homology to that of ANG microbiota supports the hypothesis that ANG might be the immediate step preceding noma .
Noma , also known as cancrum oris , is a gangrenous disease that typically affects soft as well as hard tissues of the maxillo-facial region . The disorder occurs in young children from poor or less developed areas of the world , mainly in sub-Saharan Africa , but also in Latin America [1] and Asia [2] , [3] . The high mortality rate associated with the disease during the acute stage ( 90% lethality when children are not treated ) can be associated with starvation , sepsis or aspiration pneumonia [4] . In 1998 , the World Health Organization estimated a global yearly incidence of 140 , 000 cases [2] , but the exact prevalence of the disease is not really known due to distance of villages from medical centers in numerous countries affected by noma . Although the exact etiology of noma remains unknown , several risk factors are thought to play a role in the emergence of the disease [5] , [6] , such as malaria , measles , tuberculosis , malnutrition or poor oral hygiene . In children living in these less developed countries , acute necrotizing gingivitis ( ANG ) is suspected to be a precursor stage of noma [7]–[9] , but only a small proportion of patients suffering ANG will actually evolve to noma . Falkler [10] suggested that multiple factors such as malnutrition , weakened immune functions and prior viral infections ( measles , Herpesviridae ) , all worsened by poor oral hygiene , could play in unison to reduce host resistance and promote the development of oral ulcers . These lesions would then serve as entry sites for the microorganism ( s ) responsible for the disease . Potential bacterial candidates include Fusobacterium necrophorum and Prevotella intermedia [10]–[13] . An understanding of the role of specific microorganisms or consortia of multiple organisms in the pathogenesis of noma remains incomplete due to fundamental shortcomings of currently available data . First , when culture techniques are used , the diversity of the captured microbiota inevitably underestimates a large range of fastidious or uncultivable organisms [14] . Second , because the disease develops rapidly and strikes remote geographical areas with poor access to medical facilities , microbiological findings from early cases of noma are sparse . Furthermore , results reported from advanced lesions may reflect changes in local oral ecological conditions enabling or reflecting the development of the disease , rather than identifying its real etiology . Third , this disease prevails in populations by whom even the normal oral flora remains poorly investigated . It therefore remains to be determined if seemingly unusual microbiological findings truly reflect the presence of disease or just geographic location , a particular lifestyle , or a specific socio-economical status . To improve our knowledge on the etiology of noma , it appears essential to obtain access to early acute cases of the disease , to study in parallel the microbiota of related conditions including healthy controls , and to apply advanced microbiological techniques for studying the diversity of the sampled microbiota . The first step consisted of inventorying the bacterial diversity in oral samples of children in Niger suffering from acute noma or acute necrotizing gingivitis as well as healthy controls by using culture-independent phylogenetic techniques previously described and validated [15] . The specific aim of the next step , reported here , was to validate and extend these observations by developing and using two distinct microarray approaches on an extensive collection of 584 microbial samples obtained during a vast clinical campaign in Niger , where we collected samples in various villages .
Eighty-four samples were hybridized on the high-density phylogenetic arrays ( Table 1 ) . To obtain an overview of the similarity of the bacterial content between all the study subjects we performed a PERMANOVA analysis . The largest differences were found when comparing samples originating from diseased subjects compared to healthy controls ( Table 2 , p-value between 0 . 002 and 0 . 003 ) . To a lesser extent , noma lesion site and non-lesion sites showed significant variations ( p = 0 . 041 ) . However , the comparison of lesion sites sampled from gingivitis and noma subjects showed less variation . The PCO analysis showed some separation between noma , gingivitis and healthy subjects along first two axes , but greater variations were observed in noma samples ( Figure 1A ) . We also did not notice any correlation when taking into account demographic variables of the subjects ( data not shown ) . In order to highlight statistically significant differences , among the probes of the phylogenetic microarray , reflecting imbalance in flora composition across the 5 defined conditions , ANOVA was performed on this dataset . The analysis identified 517 probes showing a p-value≤0 . 05 , of which 123 probes showed a ≥2 fold-change in at least one of the four pair-wise comparisons ( Figure 2 ) . The comparison between lesion-sites of noma samples ( N ) and healthy controls ( C ) clearly showed the highest number of probes with a significant difference ( n = 82 ) . Compared to the healthy controls , a lower bacterial diversity was found in ANG and even a lower one was recorded in noma samples . In both gingivitis and noma lesion-sites , Peptostreptococcus spp . , Prevotella spp . and Nocardioidaceae were significantly more represented in lesion sites as compared to their cognate non-lesion sites . On the other side , some taxa are less represented in diseased samples , such as Cetobacterium spp . , Rothia spp . ( part of the normal oral flora ) , Cardiobacterium spp . and Alcaligenaceae . When comparing diseased sites to healthy controls , the largest difference was found in the groups of the Firmicutes , the Bacteroidetes and the Fusobacteriales . Indeed , less Porphyromonadaceae , Tannerella spp . , Capnocytophaga spp . ( up to 10-fold changes ) , Fusobacteriales ( probe matching Leptotrichia and Streptobacillus moniliformis ) and Cetobacterium spp . ( on the basis of the latest RDP release - version 10 - the corresponding probes would be more precisely redefined as matching the Leptotrichia genus ) were found in noma samples . Other taxa such as Lautropia spp . ( Burkholderiaceae involved in periodontal disease ) and Mycobacterium spp . were also significantly less represented in noma lesions . However , Syntrophomonadaceae and saprophyte phylotypes such as Nitrospina , Haliangium , Saprospiraceae , and Hymenobacter appeared over-represented in gingivitis samples only . When comparing diseased sites to healthy controls , one single taxon ( described as Unnamed in the RDP and included in the Cyanobacteria phylum ) was found to be more abundant in noma lesions only . This “taxon” includes various environmental organisms such as Phormidium spp . , Synechococcus spp . , Scytonemacrustaceum and Calothrixcontarenii . Clearly , lesion-sites of gingivitis samples exhibit a higher bacterial diversity than lesion-sites of noma samples ( Figure 2: column N/G ) , notably within the Bacteroidetes ( including Porphyromonadaceae and Capnocytophaga spp . ) and Firmicutes ( particularly Peptococcaceae and Syntrophomonadaceae ) phyla . Given that noma lesions stay localized [24] , we compared bacterial content of lesion sites to non-lesion sites in an attempt to describe intra-patient differences in microbiota composition ( Figure 2: column N/Nn ) . Noticeably , a broader bacterial diversity is found in non-lesion sites of noma samples , particularly with groups such as Proteobacteria ( including Lautropia , an oral taxon of the Burkholderiaceae family ) , Bacteroidetes ( including Capnocytophaga spp . ) , Firmicutes ( notably with Veillonella spp . and Streptococcus spp . ) and Fusobacteriales . Conversely , the genus Peptostreptococcus is 2-fold more abundant in the lesion sites of our noma samples . ANG and noma samples , although extremely similar , exhibit some differences , notably in terms of phylotypes classically associated to various environmental milieus: Cyanobacteria ( including “unclassified Family 1 . 1” ) , Streptomycetaceae and Oceanimonas are more abundant in noma samples . On the contrary , probes matching phylotypes such as Microcoleus , Pasteurellaceae , Peptococcaceae , Bacteroidetes ( matching essentially Alistipes spp . and Porphyromonas spp . ) , Porphyromonadaceae ( including Porphyromonas spp . ) , Capnocytophaga , Sphingobacterium and Psychroflexus show limited hybridization signals with noma samples . We designed a 16S rRNA array based on the most relevant sequences inventoried by a cloning sequencing study [15] and obtained from our high-density microarray analysis , in noma , ANG and healthy controls . The purpose of this array is to extrapolate the results obtained by this previous study on a larger scale . We successfully labeled and amplified 500 samples , originating from 406 distinct subjects ( Table 1 ) from the five different conditions . ANOVA was performed on our dataset yielding 184 probes with a significant group p-value ( P≤0 . 05 ) . The microbiologic profiles of four groups ( noma lesion and non-lesion site , gingivitis lesion site and healthy controls ) are depicted in Figure 3 . Two genera are present in 100% of all four groups: Fusobacterium and Prevotella . Overall , some bacteria are found in high abundance in the lesion sites of gingivitis subjects when compared to the other groups . These genera/families include: some Lachnospiraceae ( including Catonella sp . and Oribacterium sp . ) , Lautropia sp . , Peptostreptococcus sp . , Spirochaetaceae ( more particularly Treponema sp . ) and some Prevotellaceae . Conversely , Capnocytophaga sp . is found in higher prevalence in healthy samples compared to the other groups . Several genera are found in high abundance in each 4 groups such as: Abiotrophia sp . , Clostridiales , Fusobacterium sp . , Gemella morbillorum , Neisseria cinerea , some Prevotellaceae , Streptococcus thermophilus and S . gordonii . Moreover , P . intermedia strain 6 is found in higher frequency in lesion sites of ANG and noma ( 93 . 5% and 62 . 6% of the subjects respectively ) in comparison to healthy subjects ( 11 . 3% ) ; same observation for P . melaninogenica with a prevalence of 100% and 94% in the lesion sites of gingivitis and noma respectively , and only 29 . 3% in healthy subjects . Strikingly , except for some Lachnospiraceae , no taxon appears more prevalent in the lesion sites of noma subjects as compared to the 3 other groups . ANOVA analysis reveals 135 probes that have statistically significant hybridization rates ( P< = 0 . 05 and |fold-change| ≥2 ) between gingivitis , noma and healthy groups ( Figure 4 ) . Remarkably , three genera and three species are more abundant in healthy sites/subjects when compared to the lesion sites of both noma and gingivitis: Aggregatibacter actinomycetemcomitans , Lautropia sp . , Neisseria sp . , Streptococcus sanguinis and Capnocytophaga sp . ; this last genus exhibiting the highest fold changes in the control conditions ( up to 20 . 5-fold in gingivitis and 13 . 5-fold in noma ) . Conversely , lesion sites present a higher abundance in Bacteroidetes , Dialister pneumosintes ( 11-fold in noma ) , Filifactor alocis ( Peptostreptococcaceae ) , Lachnospiraceae , Porphyromonas endodontalis , Prevotellaceae ( particularly Prevotella nigrescens and P . intermedia ) , Spirochaetaceae ( including Treponema sp . ) and Streptococcus oligofermentans . Note also that the potential pathogens Leptotrichia and some Porphyromonadaceae are less abundant in noma lesions compared to gingivitis or healthy controls . When compared to gingivitis and controls , lesion sites of noma samples clearly show a higher abundance in Actinobacteria ( probe matching Atopobium spp . ) , Prevotellaceae , Streptococcus pyogenes; and Staphylococcaceae and Streptococcus anginosus when compared to gingivitis only . Interestingly , Fusobacterium sp . appears under-represented in the lesion sites of noma samples . In order to evaluate the relatedness between conditions , we performed a PERMANOVA analysis on the 500 samples ( Table 3 ) . The largest variations were found when comparing the lesion sites of either gingivitis or noma to healthy controls ( p-value of 0 . 001 ) , while noma and gingivitis samples showed less , yet significant variations . The dimensional reduction of hybridization profiles by PCO explained 43 . 1% of the total variance between samples on the two first components ( Figure 1B ) . Although no clear separation between samples is observed , most lesion samples of noma and gingivitis clustered together .
This study is currently the largest effort to evaluate the contribution of microorganisms to the etiology of noma disease . A total of 584 samples were incorporated , representing 464 different subjects and including 84 noma subjects from the region of Zinder in Niger , an endemic noma area . Eighty four samples were hybridized on high density phylogenetic arrays in order to compare global bacterial profiles of our tested populations . In parallel , 500 samples were hybridized on the low-density microarray in an attempt to strengthen the results obtained by Bolivar et al [15] on a larger scale . Simultaneously to this work , an epidemiological study aiming at identifying risk factors for noma disease was conducted in the same patient population [17] . Results obtained with both arrays are in agreement and in line with the observations recently published by Bolivar et al . [15] using a cloning-sequencing strategy . However , a few taxa were only characterized by either one of the two approaches . Several factors can explain these disparities . First , the low-density arrays were designed in order to detect the most abundant phylotypes ( showing a prevalence ≥1% ) characterized with the cloning-sequencing strategy described by Bolivar et al . Much like a cloning-sequencing approach , our phylogenetic approach allows for relative quantification ( fold-change in respective abundance ) , meaning that we measure differences between two conditions . Second , the probe set of our phylogenetic array is not comprehensive; 78 . 3% of the sequences of the RDP ( release 9 . 34 ) are included , thus , some 16SrDNA sequences cannot be assessed . Finally , cloning sequencing for bacterial identification can suffer from biases inherent to the microbial DNA extraction [25] , the PCR technique or the sequences of primer Indeed , the specificity of the so-called “universal” primers can be limited for certain taxa [26]–[28] and 16S rRNA libraries are not necessarily representative of true prokaryotic diversity . Studies comparing subgingival samples from various forms of periodontal diseases have shown that bacterial microbiota diversity was higher in diseased patient than in healthy ones [29] . In our study , comparison between control samples from healthy donors with that obtained from sick patients ( ANG and noma ) showed the contrary . In interpreting this divergence one should consider that ANG and noma lesions are fundamentally different from periodontal pockets on the macro- and microscopic levels and may provide different local ecological conditions , hereby influencing the composition of the recovered microbiota . Whereas ANG and noma lesions are fresh acute ulcerations , the periodontal pocket is a physically protected habitat where microorganisms can develop and mature over prolonged periods of time to form a complex biofilm on a hard , non-shedding tooth surface . A decrease in bacterial diversity from ANG compared to noma may reflect the highly acute status of noma , with increased tissue turnover , imposing drastically higher ecological pressure . We observed that putative periodontal pathogens were more abundant in samples from healthy controls compared to diseased conditions . Known oral pathogens such as Aggregatibacter actinomycetemcomitans , Capnocytophaga , Porphyromonas and Fusobacteriales were more abundant in healthy samples compared to diseased conditions . This is not surprising given the fact that the periodontitis associated microbiota is predominantly anaerobic . Samples from controls and gingivitis represent the microbiota of an established dental plaque biofilm that is more likely capable of providing strictly anaerobic conditions than a recently exposed ulcerating soft tissue surface . One would expect bacterial taxa to play an important role in noma , either as an initiating primary agent , or as a secondary contributory factor , to be detectable in microbial samples from diseased sites . Fusobacterium necrophorum is an opportunistic pathogen associated with necrobacillosis in wallabies , a disease similar to noma in humans [30] . In a previous study [10] , this species was considered to play a role in the development of the disease . In our study , members of the genus Fusobacterium appear neither prevalent nor more abundant in noma lesions . In addition , other representatives of the Fusobacteriales order ( more precisely Cetobacterium , Leptotrichia and Streptobacillus moniliformis ) were significantly more abundant in samples from healthy donors . Our observations support results obtained during previous cloning-sequencing studies performed by Bolivar [15] and Paster [31] , and raise doubts on the involvement of the Fusobacterium genus as the etiological agent of noma . In the present study , Prevotellaceae and more precisely Prevotella intermedia were clearly associated with noma samples . The presence of P . intermedia in noma lesions was already documented in previous studies [10] , [15] , [32] , but was not detected in the 4 noma samples processed in the cloning-sequencing study of Paster and colleagues [31] . This pathogen is encountered in adult periodontitis [33] , [34] and is frequently isolated in endodontic infections [35]–[37] . It could participate in the etiology of noma by promoting tissue destruction by its ability to degrade lipids and produce proteolytic enzymes [38] . Moreover , P . intermedia produces immunoglobulin A1 ( IgA1 ) proteases which could play a critical role in decreasing the oral mucosal immunity [39] , hence promoting development of other pathogens in oral lesions . . In noma lesions , we observed a notable abundance of other phylotypes commonly associated with oral infections , such as dental caries ( Atopobium [40] ) , periodontitis ( Prevotella spp . [29] and Peptostreptococcus spp . [41] ) , dentoalveolar infections ( Prevotella spp . [42] ) or palatal abscess ( S . anginosus [43] ) . P . intermedia and Peptostreptococcus are microorganisms frequently recovered from children with various oral infections characterized by the formation of pus , such as retropharyngeal abscess [44] , purulent nasopharyngitis [45] , tonsillitis [46] , [47] , acute suppurative otitis media [48] and acute suppurative thyroiditis [49] . Additionally , Steptococcus pyogenes was recovered in noma lesions in significant amounts . This pathogen can cause a variety of infections such as skin infections ( impetigo ) and throat infections [50] . S . pyogenes is also involved in necrotizing fasciitis [51]; and could potentially participate in the development of noma lesions by the release of various virulence factors such as streptolysin , proteases and exotoxins . In the context of our primary study , it is interesting to note that P . intermedia group and Peptostreptococcus sp . are also frequently recovered from infections with a clearly non-bacterial initial cause , such as nosocomial sinusitis in mechanically ventilated children [52] , infections after trauma [53] , post thoracotomy [54] , following tracheotomy and intubation [55] , infected hemangioma [56] , wound infections following spinal fusion [57] and decubitus ulcers [58] . These findings point to the conclusion that high numbers of members of P . intermedia group and Peptostreptococcus sp . reflect the colonization or secondary infection of a previous lesion . The hypothesis that these microorganisms are not known as mono-infecting agents further supports this finding . The origin of these microorganisms is important to know as they are identified in mixed infections with aerobes distant from the oral and rhino-oto-laryngeal region , such as causative agents of aspiration pneumonia , lung abscess , empyema , or in intra-abdominal , hepatic , splenic and retroperitoneal abscesses [59] , or gangrenous appendicitis [60] . An interesting feature of our study is the presence of certain phylotypes never recovered from the oral cavity , such as Nitrospina , Haliangium or Saprospiraceae . A similar observation was made in a previous cloning-sequencing study by Paster et al [31] on noma subjects . These environment-associated taxa likely colonize the host as advanced noma lesions are open and subject to exogenous contamination . Our study on noma lesions and ANG showed the presence of species commonly recovered in purulent infections , and identified a microbiota that typically colonizes ulcerative oral conditions . Rather than being a classical bacterial infection , our data corroborate the concept of noma showing the characteristics of an opportunistic infection , implicating a change in the equilibrium between bacteria due to a derailment of host defenses or other influences , where microbial changes are quantitative and not only qualitative . The observed loss of bacterial diversity and appearance of oral pathogens in noma lesions is interesting for the understanding of the disease . Although no bacterial species was identified as the causative agent of noma , our study gives better insight on the bacteriology of noma . Therefore , we cannot confirm that the alteration of the oral microbiota in noma lesions explains the disease by itself , or if our observations reflect a secondary infection . Additional studies are necessary to decipher the etiology of the disease . Particularly , time series sampling and the utilization of high-throughput sequencing capacity will be instrumental to identify noma etiology .
|
Noma , or cancrum oris , is a mutilating disease affecting children in extremely limited-resource countries , suffering poor hygiene and chronic malnutrition . This devastating gangrenous disease affects the hard and soft tissues of the face . To date , the origin of the disease is still debated and current hypotheses rely on microbial diseases or on the immunologic status of the host . In an attempt to better understand the etiology of noma , the authors of the study used microarrays to assess the bacterial microbiota of gingival fluids sampled from 413 healthy and diseased children . Results obtained show reduced bacterial diversity and abundance in samples obtained from diseased patients compared to samples obtained from healthy donors , sharing identical social situation . Oral pathogens were found in both conditions but Fusobacterium necrophorum , a putative causative agent of noma , was not associated with the disease . On the other hand , no clear bacterial candidate could be identified as the etiological agent of the disease . However , a number of potential pathogens were found at higher abundance in disease patients compared to healthy donors . Finally , this study provides evidence that acute necrotizing gingivitis often evolves to noma , an observation of importance considering the dramatic condition of patients evolving to acute noma .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion"
] |
[] |
2013
|
Microarray Analysis of Microbiota of Gingival Lesions in Noma Patients
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Uridine-rich small nuclear RNAs ( snRNAs ) are the basal components of the spliceosome and play essential roles in splicing . The biogenesis of the majority of snRNAs involves 3′ end endonucleolytic cleavage of the nascent transcript from the elongating DNA-dependent RNA ploymerase II . However , the protein factors responsible for this process remain elusive in plants . Here , we show that DEFECTIVE in snRNA PROCESSING 1 ( DSP1 ) is an essential protein for snRNA 3′ end maturation in Arabidopsis . A hypomorphic dsp1-1 mutation causes pleiotropic developmental defects , impairs the 3′ end processing of snRNAs , increases the levels of snRNA primary transcripts ( pre-snRNAs ) , and alters the occupancy of Pol II at snRNA loci . In addition , DSP1 binds snRNA loci and interacts with Pol-II in a DNA/RNA-dependent manner . We further show that DSP1 forms a conserved complex , which contains at least four additional proteins , to catalyze snRNA 3′ end maturation in Arabidopsis . The catalytic component of this complex is likely the cleavage and polyadenylation specificity factor 73 kDa-I ( CSPF73-I ) , which is the nuclease cleaving the pre-mRNA 3′ end . However , the DSP1 complex does not affect pre-mRNA 3′ end cleavage , suggesting that plants may use different CPSF73-I-containing complexes to process snRNAs and pre-mRNAs . This study identifies a complex responsible for the snRNA 3′ end maturation in plants and uncovers a previously unknown function of CPSF73 in snRNA maturation .
Uridine-rich small nuclear RNAs ( snRNAs ) , ~60–200 nucleotide ( nt ) in length , are conserved noncoding RNAs in eukaryotes [1 , 2] . As the RNA components of the spliceosome , snRNAs ( U1 , U2 , U4 , U5 , and U6 ) play essential roles in spliceosome formation and splicing of pre-messenger RNAs ( pre-mRNAs ) [1–3] . Most snRNAs are derived from their primary transcripts ( pre-snRNAs ) generated by DNA-dependent RNA polymerase II ( Pol II ) , with the exception of Pol III-dependent U6 [4–7] . Like pre-mRNAs , pre-snRNAs are transcribed beyond the 3′ end of mature snRNAs [4 , 8 , 9] . Consequently , pre-snRNAs subject to 3′ maturation , a process involving endonucleolytic cleavage of the nascent transcript from the elongating polymerase in the nucleus followed by a 3′-to-5′ exonucleolytic trimming step in the cytoplasm [4 , 8 , 9] . Previous studies have identified three elements required for proper 3′ end cleavage of Pol II-dependent snRNAs in metazoans: an snRNA promoter containing the distal sequence element ( DSE ) and the proximal sequence element ( PSE ) , the C-terminal domain ( CTD ) of Rpb1 ( the largest subunit of Pol II ) , and the 3′ box that localizes the downstream of the cleavage site [5 , 8–13] . In metazoans , the integrator complex ( INT ) , which contains at least 14 subunits , is responsible for pre-snRNA 3′ end cleavage [14] . Among INT subunits , INT1 , 4 , 9 , and 11 are essential for snRNA 3′ processing , whereas INT3 and 10 are dispensable for maturation [14 , 15] . INT11 is a paralog of the cleavage and polyadenylation specificity factor 73 kDa ( CPSF73 ) , which is the catalytic component of the CPSF complex that cleaves mRNAs , but not snRNAs , at the 3′ end [14] . Because of this , INT11 was proposed to cleave pre-snRNA at 3′ end [14 , 16] . INT requires Pol II and the promoter elements for its recruitment to snRNA loci [6 , 17–21] . However , it is not clear how INT specifically recognizes snRNA loci and transcripts . Yeast uses different mechanisms to process the snRNA 3′ end because it does not contain INT , and its snRNA gene structures differ from their metazoan counterparts [6 , 7] . In plants , the major Pol II-dependent snRNAs include U1 , U2 , U4 , and U5 [22–29] . Each of them has more than ten copies in the Arabidopsis genome [30] . Although plant snRNA promoters have diverged from their metazoan counterparts and do not contain DSE and PSE , they do have an upstream sequence element ( USE ) and a proximal TATA box , which are conserved and essential for their transcription [25] . Plant snRNA genes have a conserved 3′ box ( CA ( N ) 3-10AGTNNAA ) downstream of mature snRNAs , which is necessary for snRNA processing [27 , 31] . In plants , the processing of snRNAs can be uncoupled from transcription initiation , because their promoters are not required for 3′ end cleavage [31] . In addition , many subunits of INT , including INT11 and the putative scaffold protein INT1 , are missing in plants [16] , suggesting that plants may use a mechanism different from that of metazoans to process snRNA 3′ end . Here , we report that snRNA 3′ end maturation in Arabidopsis requires a protein named DEFECTIVE in snRNA PROCESSING 1 ( DSP1 ) . DSP1 binds snRNA loci and interacts with Pol II in a DNA/RNA-dependent manner . A hypomorphic dsp1-1 mutation causes pleiotropic developmental defects , impairs snRNA 3′ maturation , and alters the occupancy of Pol II at snRNA loci . DSP1 forms a conserved complex with DSP2 , DSP3 , DSP4 , and CPSF73-I to process snRNAs . Unlike CPSF73-I , which is also the catalytic component of the plant CPSF complex , the DSP1 complex does not affect mRNA 3′ maturation . Based on these results , we propose that two CPSF73-I complexes separately process pre-snRNAs and pre-mRNAs in Arabidopsis . This study identifies an snRNA-processing complex and uncovers an unknown function for CPSF73 in plants .
In order to identify proteins involved in snRNA maturation in Arabidopsis , we screened for mutants containing increased levels of pre-U2 . 3 snRNA ( At3g57765 ) from a T-DNA collection obtained from the Arabidopsis Stock Center . We reasoned that impaired snRNA 3′ end cleavage should increase the levels of pre-snRNAs . From ~ 500 T-DNA insertion lines , we identified a mutant ( Salk_036641C ) containing elevated levels of pre-U2 . 3 snRNA relative to wild-type plants ( WT; Columbia-0 [Col] ) through reverse transcription PCR ( RT-PCR ) analyses ( Fig 1A and S1 Data ) . We named this mutant defective in snRNA processing 1–1 ( dsp1-1 ) . In dsp1-1 , a T-DNA insertion in the second intron of At4g20060 ( DSP1 ) reduced the transcript levels of DSP1 ( S1A–S1C Fig ) . However , dsp1-1 showed incomplete penetrance , as only a portion of plants showed increased levels of pre-U2 . 3 snRNA , accompanied with pleiotropic development defects such as smaller size , delayed flowering , reduced fertility , and enlarged cell size ( Fig 1B and S1D–S1F Fig ) . To demonstrate that dsp1-1 is responsible for the observed phenotypes , we crossed dsp1-1 to DSP1/dsp1-2 ( CS16199 ) , which contains a T-DNA insertion in the sixth exon of DSP1 ( S1A and S1B Fig ) . The F1 dsp1-1/dsp1-2 mutant displayed more severe growth defects and higher levels of pre-U2 . 3 snRNA than dsp1-1 ( Fig 1C and S1G Fig , S1 Data ) . Furthermore , a WT copy of DSP1 driven by its native promoter ( pDSP1::DSP1-Green Fluorescent Protein [GFP] ) in dsp1-1 rescued the developmental defects and restored the levels of pre-U2 . 3 snRNA ( Fig 1A and 1B , S1 Data ) , demonstrating that DSP1 is required for plant development and may be involved in snRNA biogenesis . We suspected that the dsp1-2 mutation might cause embryo lethality , because the homozygous dsp1-2 mutant could not be obtained , and aborted seeds were observed in siliques of DSP1/dsp1-2 ( Fig 1D ) . In fact , Nomarski microscopy showed embryos , whose terminal phenotype arrested at the globular stage , in the siliques of DSP1/dsp1-2 ( S1H Fig ) . Agreeing with this result , most dsp1-1/dsp1-1 seeds displayed delayed embryo development relative to WT ( Fig 1E ) . Furthermore , a small portion of dsp1-1 seeds contained abnormal embryos ( S1I Fig ) , suggesting that dsp1-1 might impair cell division and/or pattern formation . We also found that the transmission of dsp1-2 was reduced , as the ratio of DSP1/dsp1-2 versus WT ( 1:1 . 3 ) was less than the expected ratio ( 1:1 ) in offspring of DSP1/dsp1-2 . To determine whether DSP1 influences male or female gametophyte transmission , we performed reciprocal crosses between DSP1/dsp1-2 and WT and analyzed transmission of dsp1-2 . When WT was used as a pollen donor , dsp1-2 was transmitted normally ( S1 Table ) . However , when DSP1/dsp1-2 was used as a pollen donor , the transmission rate of dsp1-2 was reduced ( S1 Table ) , suggesting that dsp1-2 might affect male gametophyte transmission . In order to examine how dsp1 influences male gametophyte transmission , we first examined pollen viability using Alexander's staining . Although pollens from WT appeared full , round , and red-stained , many pollens from dsp1-1 could not be stained ( Fig 1F ) , suggesting that they are completely or partially devoid of cytoplasmic content , indicative of a defect in pollen viability . We also examined pollen germination and tube growth of the viable dsp1-1 pollen grains but did not observe obvious differences from WT ( S1J and S1K Fig ) . These results suggest that DSP1 participates in male gametophyte transmission by influencing pollen viability . Because the structures of Pol II-dependent snRNA genes share considerable similarities [30] , we hypothesized that DSP1 might have a general effect on pre-snRNA levels . To test this hypothesis , we randomly selected several Pol II-dependent pre-snRNAs from the U1 , U4 , and U5 gene families and examined their abundance in WT and dsp1-1 by qRT-PCR and RT-PCR . The accumulation of these selected pre-snRNAs ( pre-U1a , pre-U2 . 3 , pre-U4 . 2 , and pre-U5 . 6 snRNAs ) was much higher in dsp1-1 than that in WT , which was rescued by the GFP-DSP1 transgene ( Fig 2A and S2A Fig , S1 Data ) . In contrast , the abundance of Pol III-dependent pre-U6 . 26 snRNA was not affected by dsp1-1 ( Fig 2A and S2A Fig , S1 Data ) . These results suggest that DSP1 likely has a general role in the biogenesis of Pol II-dependent snRNAs . We further examined the effect of dsp1-1 on the accumulation of mature U1 and U2 snRNAs using northern blot . As observed in metazoans [14] , the abundance of mature U1 and U2 RNAs in dsp1-1 was comparable to that in WT ( Fig 2B ) , which could be explained by the facts that dsp1-1 is a hypomorphic mutation and snRNAs have a long half-life [32] . Cloning and sequencing analyses further showed that mature U2 RNAs were proper processing products ( S2B Fig ) . RNase protection assay showed the increased accumulation of pre-U1 and pre-U2 snRNAs in dsp1-1 and confirmed the results obtained from northern blot ( S2C and S2D Fig ) . Consistent with its effect on mature snRNAs , dsp1-1 did not impact the splicing of several examined mRNAs ( S2E Fig ) . The increased pre-snRNA levels in dsp1-1 could result from defection in pre-snRNA 3′ end cleavage or increased pre-snRNA transcription . To distinguish these two possibilities , we first evaluated if dsp1-1 influenced pre-U2 . 3 snRNA 3′ end cleavage with an in vitro assay using the U2 . 3 gene as reporter according to [13] . In this assay , a 5′ end [P32]-labeled pre-U2 . 3 snRNA was processed in nuclear proteins extracted from inflorescences of dsp1-1 or WT . We also included a pre-U2 . 3 snRNA with a poly-G tail at 3′ end ( pre-U2 . 3-pG ) , which prevents 3′ trimming activity [33] , to rule out the possibility that the product is generated from the 3′ end trimming rather than endonucleolytic cleavage . The accumulation of U2 . 3 snRNAs ( ~196 nt ) generated from both pre-U2 . 3 and pre-U2 . 3-pG was reduced in dsp1-1 relative to their levels in WT at various time points ( Fig 2C and S2F Fig ) . Quantification analysis of the 90-min reaction showed that the overall pre-U2 . 3 snRNA processing activity in dsp1-1 was approximately 40% of that in WT ( Fig 2D and S1 Data ) . In addition , the DSP1-GFP transgene restored pre-U2 . 3 snRNA processing in dsp1-1 ( Fig 2C and 2D , S1 Data ) . These results suggest that DSP1 might be required for the snRNA 3′ end maturation . To further test the effect of DSP1 on snRNA transcription and 3′ maturation , we used an in vivo GUS reporter gene assay . In this assay , the GUS gene was fused to the 3′ end of the U2 . 3 gene that contains the promoter , the coding region , and 3′ box region ( pU2::pre-U2-GUS; Fig 2E ) according to [15] . If properly cleaved , pre-U2-GUS RNAs would not be translated into GUS protein ( Fig 2E ) , whereas disrupted cleavage would result in GUS accumulation . As a control for transcription , we generated a GUS reporter fused with a mutated U2 . 3 gene ( pU2::pre-U2m-GUS ) , in which the 3′ box was mutated to disrupt pre-U2 snRNA processing ( Fig 2F ) , with expectation that the GUS protein would be accumulated ( Fig 2E and S2G and S2H Fig ) . The alteration of pre-U2m-GUS levels in dsp1-1 relative to WT would reflect the effect of DSP1 on pre-snRNA other than cleavage . Transgenic lines expressing pU2::pre-U2-GUS or pU2::pre-U2m-GUS were generated in a Col background and subsequently crossed to dsp1-1 ( S2H Fig ) . In F2 , DSP1+ ( DSP1/DSP1 or DSP1/dsp1-1 ) , or dsp1-1 , genotypes containing the GUS transgene were identified through PCR genotyping . GUS activities and the abundance of pre-U2m-GUS transcripts were slightly reduced in dsp1-1 relative to DSP1+ ( Fig 2F and 2G and S2I Fig , S1 Data; bottom panel ) , suggesting that dsp1-1 does not increase the transcription of pre-snRNAs . In contrast , relative to DSP1+ , the GUS activities and pre-U2-GUS transcript levels were increased in various tissues of dsp1-1 harboring pre-U2-GUS ( Fig 2F and 2G and S2I Fig , S1 Data; top panel ) . These results demonstrate that DSP1 is essential for snRNA 3′ end cleavage . In metazoans , the INT complex co-transcriptionally processes pre-snRNAs [16] . This led us to hypothesize that DSP1 , if it has a direct role in snRNA processing , might be a nuclear-localized protein that associates with the snRNA loci . To examine the subcellular localization of DSP1 , we expressed GFP-DSP1 from the CaMV35S promoter ( 35S::GFP-DSP1 ) in leaf epidermal cells of Nicotiana benthamiana . In these cells , GFP-DSP1 localized to the nucleus ( Fig 3A ) . Consistent with this result , GFP-DSP1 was detected in the nuclear protein fraction , but not in the cytoplasmic protein fraction ( Fig 3B ) , both of which were extracted from the dsp1-1 harboring 35S::GFP-DSP1 . To examine the association of DSP1 with the U2 . 3 locus , we performed a chromatin immunoprecipitation ( ChIP ) assay using dsp1-1 harboring GFP-DSP1 or GFP ( negative control ) and then checked the presence of the U2 . 3 locus in the ChIPs of GFP-DSP1 and GFP IPs using PCR and quantitative PCR ( qPCR ) . The USE , TATA box ( U2-TA ) , coding region ( U2-C ) , and 3′ box ( U2-3′ box; the highest signal ) of the U2 . 3 locus were enriched in the ChIPs of GFP-DSP1 , but not in the ChIPs for GFP , relative to “no-antibody” controls ( Fig 3C and 3D and S3A Fig , S1 Data ) . In addition , the downstream regions ( U2-DS1 and U2-DS2 ) of the 3′ box of the U2 . 3 locus and the ACTIN2 locus ( Pol II-dependent ) were not enriched in the ChIPs of GFP-DSP1 ( Fig 3C–3E and S3A and S3B Fig , S1 Data ) . DSP1 also occupied the USE , TATA-box , coding region , and 3′ box ( the highest signal ) of the U1a locus , but not in the downstream regions ( U1-DS1 and U1-DS2 ) of the U1a 3′ box ( Fig 3F and S3C Fig , S1 Data ) . These results show the occupancy of DSP1 at the snRNA loci , which , together with the fact that dsp1-1 causes the defection of snRNA processing , demonstrates that DSP1 has a direct role in snRNA biogenesis . Both U1a and U2 . 3 were transcribed through the DS1 region ( S3D Fig ) . The absence of DSP1 in the DS1 region suggests that DSP1 may not travel through the 3′ box or be released at the 3′ box after cleavage . The occupancy of DSP1 at the snRNA loci prompted us to test the interaction between DSP1 and Pol II by a co-immunoprecipitation ( Co-IP ) assay [34] . GFP-DSP1 and RPB2 ( the second-largest subunit of Pol II ) were able to reciprocally co-IP ( Fig 3G and 3H ) . In contrast , GFP did not interact with RPB2 ( Fig 3G and 3H ) . In addition , GFP-DSP1 and RPB2 proteins were not detected in the “no-antibody” reactions . These results confirm a DSP1-Pol II association . We further examined the dependence of the DSP1–Pol II interaction on DNAs/RNAs . Treatments with either DNase I or RNase A reduced the interaction of DSP1 with Pol II ( Fig 3I ) , whereas micrococcal nuclease , which acts on both RNAs and DNAs , abolished the DSP1–Pol II interaction ( Fig 3J ) . We next evaluated the effect of dsp1-1 on Pol II occupancy at the U2 . 3 locus in a ChIP assay using anti-RPB2 antibodies . As expected , Pol II occupied the U2 . 3 and ACTIN2 loci , but not the Pol II C1 locus ( an intergenic DNA fragment between At2g17470 and At2g17460 ) ( Fig 3K and 3L ) [35] . dsp1-1 reduced Pol II occupancy at the USE , TATA box , and U2 . 3C of the U2 . 3 locus , but not at the ACTIN2 locus ( Fig 3K and 3L , S1 Data ) . Interestingly , dsp1-1 did not alter the occupancy of Pol II at the 3′ box ( Fig 3K ) . dsp1-1 had a similar effect of Pol II occupancy at various regions of the U1a locus ( Fig 3M and S1 Data ) . These results suggest that DSP1 is required for proper occupancy of Pol II at snRNA loci . DSP1 does not contain any known nuclease domains , suggesting that it may associate with other proteins to act in snRNA maturation . DSP1 is a conserved protein in higher plants and contains an N-terminal armadillo ( ARM ) -like fold ( Fig 4A and S4A Fig ) , which arranges in a regular right-handed super helix that provides a solvent-accessible surface for binding large substrates , such as proteins and nucleic acids , and a C-terminal region of unknown function [36] . We found that the ARM domain of DSP1 shared ~25% similarity with that of the integrator subunit 7 ( INT7 ) of metazoans ( Fig 4A ) . This led us to suspect that an INT-like complex might exist in plants . If so , an INT11 ( the catalytic subunit of INT ) -like nuclease should function in snRNA processing in plants . Arabidopsis encodes two INT11-like nucleases , CPSF73-I and CPSF73-II , which are conserved in higher plants ( S4B Fig ) [37 , 38] . Because they lack the characteristic C-terminal region of INT11 ( Fig 4A ) , which is essential for snRNA maturation [39] , and act as the catalytic components of the CPSF complex to cleave pre-mRNA 3′ end [37 , 38] , CPSF73-I and CPSF73-II were never thought to act on snRNA processing . However , CPSF73-I is essential for both pollen and embryo development [38] . This resembles the effect of DSP1 on plant development , suggesting that CPSF73-I might be the nuclease that processes snRNAs in plants . To test this , we used an artificial miRNA ( amiRCPSF73-I ) to knockdown the expression of CPSF73-I ( Fig 4B and S4C Fig , S1 Data ) [40] . The reduced expression of CPSF73-I in the amiRCPSF73-I lines caused developmental defects and increased the levels of pre-U2 . 3 snRNAs ( Fig 4C and 4D , S1 Data ) . Expression of an amiRCPSF73-I-resistant CPSF73-I ( CPSF73-I-R ) in the amiRCPSF73-I lines recovered the levels of pre-U2 . 3 snRNA ( Fig 4E and S1 Data ) , suggesting that CPSF73-I is required for snRNA 3′ end maturation . Next , we tested if CPSF73-II also had a role in pre-snRNA processing ( S4C Fig ) . Although amiRCPSF73-II reduced the expression of CPSF73-II , resulting in pleiotropic developmental defects ( S4C–S4E Fig and S1 Data ) , it did not affect the levels of pre-snRNAs ( S4F Fig and S1 Data ) . We also examined whether CPSF100 , which partners with CPSF73-I to process pre-mRNAs , is required for snRNA processing . However , a knockdown of CPSF100 by amiRCPSF100 did not alter the levels of pre-U2 . 3 RNAs ( S4C , S4G and S4H Fig and S1 Data ) . The above results suggest the presence of a CPSF73-I containing complex that acts on pre-snRNAs . Indeed , size-exclusion high performance liquid chromatography ( HPLC ) detected a ~670 kDa ( eluted at 93–102 min ) CPSF73-I-containing complex and a larger complex ( eluted at 72–78 min ) besides CPSF73-I monomers in the protein extracts of the transgenic plants harboring a 35S::GFP-CPSF73-I transgene ( S4I Fig ) . The ~670 kDa complex , but not the larger one , was able to process pre-U . 2 snRNAs ( S4J Fig ) . Next , we tested if this ~670 kDa complex could act on pre-mRNAs using a 5′ end [P32]-labeled RNA ( RSB-3; ~380 nt ) that covers the 3′-UTR of an rubisco small subunit gene ( At5g38420 ) [41] . Proper 3′ end processing of RSB-3 would generate a ~240 nt RNA fragment and a ~190 nt RNA fragment due to the presence of two poly ( A ) sites ( S4L Fig ) [41] . The 670 kDa complex did not process RSB-3 ( S4L Fig ) . We also examined the effect of dsp1-1 on the formation of the CPSF73-I complex and its activity . The size of the snRNA-processing complex became smaller in dsp1-1 relative to that in Col , and its pre-U2 . 3 snRNA processing activity was reduced ( S4I and S4K Fig ) . In contrast , the larger complex was still intact in dsp1-1 ( S4D Fig ) . We further examined the occupancy of CPSF73-I at the U2 . 3 locus in N . benthamiana leaves harboring both GFP-CPSF73-I and pU2::pre-U2-GUS . ChIP assay detected the occupancy of CPSF73-I at the pU2::U2 . 3-GUS gene , with the highest occupancy at the 3′ box ( Fig 4F and S1 Data ) . We also tested the interaction of CPSF73-I with Pol II in Col harboring a 35S::GFP-CPSF73-I transgene . However , unlike DSP1 , CPSF73-I did not interact with Pol II ( Fig 4G ) . Next we sought to identify additional proteins acting in snRNA maturation by searching for Arabidopsis homologs of other INT subunits . We identified At4g14590 ( named DSP2 ) , At3g08800 ( named DSP3; also known as SHORT-ROOT INTERACTING EMBRYONIC LETHAL , SIEL ) [42] , and At3g07530 ( named DSP4 ) as potential homologs of INT3 , INT4 , and INT9 , respectively ( Fig 5A ) . Among them , DSP2 is approximately half size of INT3 and shares ~57% similarity with the N-terminal fragment ( aa , 1–490 ) of INT3 ( Fig 5A ) . The ARM domain , but not other regions , of DSP3 shared similarities with INT4 ( Fig 5A ) . DSP4 has ~46% similarity with INT9 ( Fig 5A ) . Like DSP1 and CPSF73-I , these proteins are conserved in higher plants ( S5A–S5C Fig ) . We evaluated if DSP2 , DSP3 , and DSP4 were required for snRNA processing using their loss-of-function mutants . The DNA knockout mutants for DSP2 ( CS848944 ) and DSP3 ( SALK_089544; dsp3-2; also known as seil-2 ) displayed embryo lethality ( Fig 5B and S5D Fig ) , whereas expression of DSP4 was not altered in the available T-DNA insertion mutants ( SALK_005904; S5D–S5F Fig ) . We thus obtained a weak allele of DSP3 ( SALK_086160 , dsp3-1; siel-4 ) , in which a T-DNA insertion reduced the expression levels of DSP3 and constructed knockdown lines of DSP2 ( amiRDSP2 ) and DSP4 ( amiRDSP4 ) with artificial miRNAs ( S5D–S5G Fig ) . dsp3-1 , amiRDSP2 , and amiRDSP4 reduced the expression of DSP3 , DSP2 , and DSP4 , respectively , and caused pleiotropic developmental defects ( Fig 5C–5E and S5F and S5H–S5I Fig , S1 Data ) . qRT-PCR showed that the levels of pre-U2 . 3 snRNAs were increased in dsp3-1 and amiRDSP4 relative to those in WT ( Fig 5F and 5G , S1 Data ) , suggesting that they might act in snRNA processing . However , the levels of pre-U2 . 3 snRNAs were not altered or slightly lower in amiRDSP2 relative to those in WT ( S5J Fig and S1 Data ) , agreeing with a dispensable role of INT3 for pre-snRNA maturation [15] . To confirm the role of DSP2 , DSP3 , DSP4 , and CPSF73-I in snRNA maturation , we examined their effect on the 3′ end cleavage of pre-U2 . 3 snRNAs using the in vitro processing assay . The accumulation of mature snRNAs generated from pre-U2 . 3 and pre-U2 . 3-pG was lower in nuclear protein extracts from dsp3-1 , amiRDSP4 , or amiRCPSF73-I than from WT ( Fig 5H and 5I and S6A Fig , S1 Data ) . In contrast , amiRDSP2 did not impair pre-U2 . 3 and pre-U2 . 3-pG processing ( Fig 5H and 5I and S6A Fig , S1 Data ) . In addition , the expression of CPSF73-I-R in the amiRCPSF73-I line fully recovered the processing of pre-U2 . 3-pG snRNA ( S6B Fig ) . These results demonstrate that , like DSP1 and CSPF73-I , DSP3 and DSP4 are required for snRNA processing . The involvement of CPSF73-I in both pre-mRNA and pre-snRNA processing raised the possibility that the DSP proteins might also function in pre-mRNA 3′ end cleavage . Therefore , we tested their effect on the 3′ end processing of the RSB-3 RNA ( S4L Fig ) using nuclear protein extracts . As expected , the 3′ end processing efficiency of RSB-3 was reduced in amiRCPSF73-I and amiRCPSF73-II relative to WT at various time points ( Fig 5J–5L and S6C Fig , S1 Data ) . In contrast , RSB-3 processing in protein extracts from dsp1-1 , dsp3-1 , amiRDSP2 , and amiRDSP4 was comparable with that of WT ( Fig 5J–5L and S1 Data ) , suggesting that DSP1 , DSP2 , DSP3 , and DSP4 are not required for pre-mRNA 3′ end processing . To further validate the result , we monitored the 3′ end formation of FCA mRNA , which is known to be affected by the pre-mRNA 3′ end processing complex [43] , in dsp , amiRCPSF73-I , and amiRCPSF73-II by northern blot . amiRCPSF73-I and amiRCPSF73-II , but not dsp1-1 , dsp3-1 , amiRDSP2 , and amiRDSP4 , altered the 3′ end formation of FCA ( S6D Fig ) . The involvement of DSP1 , DSP3 , DSP4 , and CPSF73-I in the snRNA maturation raised the possibility that they may form a complex to cleave pre-snRNAs . To test this possibility , we first examined the interaction of DSP1 with the other proteins using co-IP . DSP2 was included in this experiment because its homolog INT3 is a component of the INT complex [14] . We also included CPSF100 as a control because it is a homolog of DSP4 but does not affect snRNA processing . GFP-DSP1 was transiently co-expressed with MYC-DSP2 , MYC-CPSF100 , MYC-DSP4 , or MYC-CPSF73-I in N . benthamiana as described previously [34] . MYC-DSP4 and MYC-CPSF73I , but not MYC-DSP2 and MYC-CPSF100 , were detected in the GPF-DSP1 precipitates ( Fig 6A–6C and S7A Fig ) . In addition , the control , GFP , did not co-IP with MYC-DSP2 , MYC-DSP4 , and MYC-CPSF73-I ( Fig 6A–6C ) . We were unable to express the recombinant DSP3 protein in either N . benthamiana or Escherichia coli , likely because it is extremely unstable . To test the interaction of DSP1 with DSP3 , we generated a recombinant DSP3-MYC protein using an in vitro translation system as described [44] . However , DSP1 did not co-IP with DSP3-MYC ( Fig 6D ) . These results support the interaction of DSP1 with DSP4 and CPSF73-I , but not with DSP2 , DSP3 , and CPSF100 . We further tested the interaction of GFP-DSP2 with DSP3-MYC , MYC-DSP4 , or MYC-CPSF73-I . GFP-DSP2 interacted with MYC-DSP4 but not with DSP3-MYC and MYC-CPSF73-I ( Fig 6E–6G ) . Co-IP/pull down assays also showed that DSP4 did not interact with DSP3 and CPSF73-I , but DSP3 did interact with CPSF73-I ( Fig 6H–6J ) . To confirm these protein interactions , we performed a bimolecular fluorescence complementation ( BiFC ) assay ( S1 Text ) [45] . In this assay , the paired proteins , which were fused to the N-terminal fragment of yellow fluorescent protein ( nYFP ) or to the C-terminal fragment of YFP ( cYFP ) , respectively , were introduced into tobacco cells by infiltration . The interaction of the two protein partners will result in a functional YFP [34] . As expected , the DSP1–DSP4 , DSP1–CPSF73-I , DSP2–DSP4 interactions , but not the DSP1–DSP2 , DSP2–CPSF73-I , and DSP4–CPSF73-I interactions , were confirmed ( S7B Fig ) . We further validated the protein interactions using stable transgenic lines harboring GFP-DSP1/MYC-CPSF73-I , GFP-DSP1/MYC-DSP4 , or GFP-DSP4/MYC-CPSF73-I transgenes . As observed in tobacco , we detected the DSP1–DSP4 and DSP1–CPSF73-I interactions , but not the DSP4–CPSF73-I interaction , in Arabidopsis ( Fig 6K–6M ) . Next , we asked if these proteins could co-exist in a complex . We found that GFP-DSP1 pulled down both CPSF73-I and DSP3 from protein extracts containing DSP3-MYC , GFP-DSP1 , and HA-CPSF73-I ( Fig 6N ) . In addition , when co-expressed , DSP4 co-IPed with DSP1 , DSP2 , and CPSF73-I , while CPSF73-I co-IPed with DSP1 , DSP2 , and DSP4 ( Fig 6O and 6P ) . These results demonstrate that DSP1 , DSP2 , DSP3 , DSP4 , and CPSF73-I likely form a complex to process snRNAs ( Fig 7 ) .
We identified a conserved complex essential for 3′ end maturation of Pol II-dependent snRNAs in plants . This complex contains at least five proteins , including DSP1 , DSP2 , DSP3 , DSP4 , and CPSF73-I . In this complex , DSP1 bridges DSP4 and CPSF73-I , whereas DSP2 and DSP3 may act as accessory components of DSP4 and CPSF73-I , respectively ( Fig 7 ) . More importantly , we show that CPSF73-I likely is the catalytic component for snRNA 3′ end processing . This result shows that higher plants use the same enzyme to process both pre-mRNAs and pre-snRNAs . However , the two CPSF73-containing complexes might function separately in snRNA and pre-mRNA maturation ( Fig 7 ) , as the dsp mutations do not impair the mRNA 3′ end processing and a knockdown of CPSF-100 or CPSF73-II does not affect snRNA 3′ end maturation . Furthermore , mass spectrometry analyses did not identify any DSP proteins in the CPSF-100 complex [46] . Consistent with this , DSP1 interacts with DSP4 but not its homolog CPSF-100 . In contrast to what we have discovered in plants , in metazoans , CPSF73 and its paralog , INT11 , are used to process pre-mRNAs and pre-snRNAs , respectively . However , the similarities of some DSP proteins with their counterparts in INT raise the possibilities that a common ancestor complex containing CPSF73 might have been used to process pre-snRNAs before divergence between metazoans and plants and that CPSF73 may be subject to sub functionalization in metazoans . How does the DSP1 complex recognize and process pre-snRNAs ? The occupancy of DSP1 and CPSF73-I at snRNA loci and the DSP1-Pol II association support the idea that the DSP1 complex processes pre-snRNAs co-transcriptionally . Both CPSF73 and DSP1 have the highest occupancy at the 3′ box , and mutations in the 3′ box greatly reduced the activity of the DSP1 complex , demonstrating that the 3′ box is essential for the DSP1 complex to recognize the cleavage site . In metazoans , Pol II plays key roles in recruiting INT to snRNA loci and transcription initiation is essential for snRNA processing [6 , 17–21] . However , in plants , blocking transcription initiation only has a minor effect on snRNA processing [31] . In addition , DSP1 interacts with Pol II in a DNA/RNA-dependent manner , whereas CPSF73-I and DSP4 do not associate with Pol II ( Fig 4G and S5K Fig ) . These results suggest that Pol II is not crucial for recruiting the DSP1-CPSF73 complex to the snRNA loci , although we cannot completely rule out this possibility . Perhaps the DSP1 complex can recognize specific sequence in the promoters of snRNAs . Alternatively , the DSP1 complex might be recruited to snRNA loci through its interaction with some snRNA-specific transcription factors . Clearly , all these possibilities need to be examined in the near future . The DSP1 complex may have other roles in snRNA biogenesis . The facts that the DSP1 interacts with the snRNA promoters and that the dsp1-1 mutation reduced the occupancy of Pol II at the promoters and coding regions of U1 and U2 snRNA genes support that the DSP complex promotes the transcription of Pol-II dependent snRNAs . In further support of this , the transcript levels of preU2m-GUS RNAs are slightly lower in dsp1-1 than in WT ( Fig 2H ) . However , it is not clear whether the DSP1 complex directly or indirectly regulates snRNA transcription . The DSP complex may also positively contribute to Pol II releasing at the snRNA 3′ end , because the 3′ end cleavage will help transcription termination . If so , the Pol II occupancy at the 3′ end of snRNA loci should be increased in dsp1-1 . However , we observed unchanged Pol II occupancy at the 3′ end in dsp1-1 relative to Col . This result likely reflects the combined effects of DSP1 on snRNA transcription and 3′ end processing . Besides snRNA biogenesis , the DSP complex may have other functions , given the facts that lack of DSP2 , which has a minor role in snRNA processing , causes embryo lethality and developmental defects ( Fig 5 ) and that dsp1-1 , in which the abundance of mature snRNAs is comparable to that of WT , still displays pleiotropic developmental defects ( Figs 1 and 2 ) . In fact , DSP3 ( known as SIEL ) has been shown to promote root patterning through interacting with SHR , a transcription factor , and promoting its movement [42] . It will be interesting to test whether other DSP components have similar functions in root patterning . In metazoans , INT not only functions in snRNA processing , but also controls the transcription termination of some mRNAs , the biogenesis of enhancer RNAs , which are noncoding RNAs regulating gene expression , and the biogenesis of some viral-derived miRNAs [33 , 47–50] . It is possible that the DSP complex plays similar roles in plants . We identified several mRNAs containing the 3′ box at their 3′ end from the Arabidopsis genome . However , the DSP complex does not affect their processing . Thus , it remains to be determined if the DSP complex has other substrates and , if so , what these substrates are .
T-DNA insertion mutants including CS848944 , SALK_089544 , SALK_005904 , SALK_036641 , CS16199 , and SALK_086160 were obtained from the Arabidopsis stock center ( www . arabidopsis . org ) ; all are in the Col genetic background . Transgenic lines ( Col background ) harboring pU2::pre-U2-GUS or pU2::pre-U2m-GUS were crossed to dsp1-1 . In the F2 population , DSP1+ ( DSP1/DSP1; DSP1/dsp1-1 ) plants and dsp1-1 containing the transgenes were identified by genotyping of T-DNA and GUS using primers listed in S2 Table . A 6 . 4 kb genomic fragment containing the DSP1 promoter and coding regions was PCR amplified , cloned into pENTR/SD/D-TOPO , and subsequently cloned into the binary vector pGWB4 . The resulting plasmid was transformed into dsp1-1 , and transgenic plants were screened for Hygromycin resistance . DSP1 cDNA was amplified by RT-PCR , cloned into pENTR/SD/D-TOPO , and subsequently cloned into pEG104 [51] to generate the 35S::GFP-DSP1 fusion vector . A genomic fragment containing the U2 . 3 gene promoter , snRNA coding region , and 3′ box region was PCR amplified and cloned into pMDC164 to generate pU2::pre-U2-GUS . The 3′ box of pre-U2-GUS was then mutated to generate pU2::pre-U2m-GUS using a Site-Directed Mutagenesis Kit ( Stratagene ) . The primers used for plasmid construction are listed in S2 Table . Siliques of different developmental stages were dissected with hypodermic needles , mounted on microscope slides in a clearing agent ( Visikol ) overnight , and then observed with a confocal microscope . To visualize GUS expression , samples were immersed in the GUS staining solution for 12 h in the dark . The stained samples were treated with 70% ethanol to remove chlorophyll before observation using a dissecting microscope . cDNA was synthesized from 2 μg of total RNA with reverse transcriptase ( Invitrogen ) and random primers . qPCR was performed in triplicate on a Bio-Rad IQcycler apparatus with the Quantitech SYBR green kit ( Bio-Rad ) . The primers used for PCR are listed in S2 Table . In vitro processing assays of pre-U2 . 3 and the 3′ UTR of a Rubisco small subunit gene ( RSB-3 ) were performed as described [13 , 41] . Briefly , DNA templates used for in vitro transcription of pre-U2 . 3 and RBS-3 were amplified using T7 promoter-anchored primers ( S2 Table ) . A 5′ end [32P]-labeled pre-U2 . 3 snRNA was incubated with 2 μg nuclear proteins in a 20 μl reaction , while [32P]-labeled RSB-3 was cleaved by 4 μg nuclear proteins in a 20 μl reaction . After reactions were stopped at various time points , RNAs were extracted , purified , and resolved on a PAGE gel . Radioactive signals were detected by PhosphorImager and quantified by Quantity One . ChIPs with anti-GFP and anti-Pol II were performed as described [34] . Anti-RPB2 ( Abcam ) and anti-GFP antibodies ( Clontech ) were used for IP . Enrichment of DNA fragments was measured by qPCR . The primers used in ChIP-PCR are listed in S2 Table . To test DSP1–PoII interaction , proteins were extracted from dsp1-1 harboring the GFP-DSP1 transgene . To test CPSF73-I–Pol II interactions , proteins were extracted from N . benthamiana transiently expressing GFP-CPSF73-I . To test the interactions among DSP1 , DSP2 , DSP4 , and CPSF73-I , proteins were co-expressed in N . benthamiana . To analyze multi-protein–containing complexes , samples were treated with formaldehyde to fix protein–protein interactions as described [52] . To test the interaction of DSP3 with other proteins , a DSP3-MYC fragment was generated using primers containing elements required for in vitro transcription and translation ( S2 Table ) . The resulting DNA fragment was used as a template to synthesize DSP3-MYC protein using a PURExpress In Vitro Protein Synthesis Kit ( New England Biolabs ) . To obtain plants harboring two transgenes , transgenic Arabidopsis harboring GFP-DSP1 was crossed with transgenic plants containing MYC-CPSF73-I or MYC-DSP4 transgenic , whereas transgenic Arabidopsis harboring GFP-DSP4 was crossed with MYC-CPSF73-I transgenic lines . F1 plants harboring both transgenes were used for IP assay . Pollen viability was examined after Alexander’s staining [53] . In vitro pollen growth assays were performed as described [54] . To examine pollen tube growth in Col-0 and dsp1 in vivo , pistils were pollinated and collected 12 h later , then cleared and stained with decolorized aniline blue [54] .
|
snRNAs form the RNA components of the spliceosome and are required for spliceosome formation and splicing . The generation of snRNAs involves 3′ end endonucleolytic cleavage of primary snRNA transcripts ( pre-snRNAs ) . The factors responsible for pre-snRNA 3′ end cleavage are known in metazoans , but many of these components are missing in plants . Therefore , the proteins that catalyze pre-snRNA cleavage in plants and the mechanism leading to plant snRNA 3′ maturation are unknown . Here , we show that a DSP1 complex ( containing DSP1 , DSP2 , DSP3 , DSP4 , and CPFS73-I ) is responsible for pre-snRNA 3′ end cleavage in Arabidopsis . We further show that CPSF73-I , which is known to cleave the pre-mRNA 3′ end , is likely the enzyme also catalyzing snRNA 3′ end maturation in plants . Interestingly , plants appear to use two different CPSF73-I-containing complexes to catalyze the maturation of mRNAs and snRNAs . The study thereby identifies an snRNA-processing complex in plants and also elucidates a new role for CPSF73-I in this process .
|
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2016
|
snRNA 3′ End Processing by a CPSF73-Containing Complex Essential for Development in Arabidopsis
|
Trichomonas vaginalis is the causative agent of human trichomoniasis , the most common non-viral sexually transmitted infection world-wide . Despite its prevalence , little is known about the genetic diversity and population structure of this haploid parasite due to the lack of appropriate tools . The development of a panel of microsatellite makers and SNPs from mining the parasite's genome sequence has paved the way to a global analysis of the genetic structure of the pathogen and association with clinical phenotypes . Here we utilize a panel of T . vaginalis-specific genetic markers to genotype 235 isolates from Mexico , Chile , India , Australia , Papua New Guinea , Italy , Africa and the United States , including 19 clinical isolates recently collected from 270 women attending New York City sexually transmitted disease clinics . Using population genetic analysis , we show that T . vaginalis is a genetically diverse parasite with a unique population structure consisting of two types present in equal proportions world-wide . Parasites belonging to the two types ( type 1 and type 2 ) differ significantly in the rate at which they harbor the T . vaginalis virus , a dsRNA virus implicated in parasite pathogenesis , and in their sensitivity to the widely-used drug , metronidazole . We also uncover evidence of genetic exchange , indicating a sexual life-cycle of the parasite despite an absence of morphologically-distinct sexual stages . Our study represents the first robust and comprehensive evaluation of global T . vaginalis genetic diversity and population structure . Our identification of a unique two-type structure , and the clinically relevant phenotypes associated with them , provides a new dimension for understanding T . vaginalis pathogenesis . In addition , our demonstration of the possibility of genetic exchange in the parasite has important implications for genetic research and control of the disease .
Trichomoniasis is the most common non-viral sexually transmitted infection ( STI ) world-wide . Of the estimated 174 million new infections each year [1] – making it more prevalent than gonorrhea and chlamydia combined – ∼7 million occur in the United States [2] . Historically , trichomoniasis has been considered a self-clearing female ‘nuisance’ disease [3] , but recent studies indicate that without antimicrobial treatment women may maintain chronic infections indefinitely , while men usually resolve infection without treatment [4]–[6] . In women , symptoms include malodorous vaginal discharge , vulval irritation and inflammation , and punctate microhemorrhages on the cervix known as ‘strawberry cervix’ [7] . Males , though often asymptomatic , can present with urethritis , urethral discharge and dysuria [8] . Trichomoniasis has been associated with severe reproductive health sequelae in both sexes , including , pelvic inflammatory disease [9] and adverse pregnancy outcomes [10] , [11] in women , and prostatitis , infertility , and an increased incidence of aggressive prostate cancers in men [8] , [12] . Perhaps most importantly , trichomoniasis has also been implicated in increasing sexual transmission of HIV up to two-fold [13] , [14] . Because of the high prevalence of trichomoniasis , this translates into a significant number of global HIV infections [15] . Trichomonas vaginalis , the causative agent of human trichomoniasis , is a highly motile , aerotolerant , haploid eukaryotic parasite that resides in the urogenital tract and has an apparently simple life-cycle consisting of a trophozoite stage that is transmitted from host to host through sexual intercourse . The parasite itself can harbor a linear , double-stranded RNA virus known as T . vaginalis virus ( TVV ) , which has been reported in approximately 50% of isolates , and may have important implications in virulence [16] . Currently , only the 5-nitroimidazole family of drugs ( specifically metronidazole and tinidazole ) is approved for the treatment of trichomoniasis; however , drug resistance ( documented since this family of drugs was first used to treat the infection [17] ) is a major concern , with estimates of up to 10% of infections not responding to treatment in the United States [18] . Current knowledge of T . vaginalis population genetics has been limited by a lack of appropriate tools . Crude genotyping markers such as random amplified polymorphic DNA ( RAPDs ) and restriction fragment length polymorphisms ( RFLPs ) , have indicated genetic variation among T . vaginalis isolates and have inconclusively detected evidence of population structure [19]–[25] . These methods , however , are highly sensitive to contaminating DNA or to slight variation in conditions , which may influence the interpretation of data collected with these techniques . To address these limitations in existing methods of genetic characterization , we recently developed the first panel of T . vaginalis-specific microsatellite and single nucleotide polymorphisms ( SNPs ) as robust genetic markers [26] . These markers were utilized to sample the diversity of seven laboratory strains of T . vaginalis collected during the past >55 years and propagated in vitro , in some instances for more than a year . As a result of this work , we identified a significant amount of diversity across most loci in the seven strains , although differences existed between the topology of trees inferred from different types of markers . However , limitations of that study included ( a ) a focus on laboratory strains collected many years previously; ( b ) sampling of a limited number of geographical regions; and ( c ) the small number of strains characterized . To assay the global diversity and population structure of T . vaginalis , we present here analysis of 235 T . vaginalis isolates from ten world-wide regions in Mexico , Chile , India , Australia , Papua New Guinea , Italy , Africa and the United States , including 19 clinical isolates recently collected from 270 women attending New York City sexually transmitted disease ( STD ) clinics . We find high genetic diversity within the T . vaginalis parasite , and a two-type population structure that is distributed in near equal frequencies world-wide . In addition , we show that the two types differ in the frequency in which they harbor TVV and in their metronidazole sensitivity . Finally , we present evidence of recent intragenic recombination and speculate on the possibility of a sexual life-cycle of the parasite in the absence of obvious sexual stages . Our findings enhance the understanding of the population genetics and diversity of T . vaginalis and suggest the possibility of genetic exchange in the parasite , which has wide-ranging implications for the epidemiology and control of human trichomoniasis .
Global strains and isolates obtained from collaborators , including references that provide details of their collection , are given in Table S1 . All parasites were cultured in modified Diamond's media [27] , supplemented with 10% horse serum , penicillin and streptomycin ( Invitrogen ) and iron solution composed of ferrous ammonium sulfate and sulfosalicylic acid ( Fisher Scientific ) . Minimum lethal concentrations ( MLCs ) for metronidazole were determined under aerobic conditions by the lab of origin according to previously published protocols [22] , unless otherwise indicated . A subset of the samples , the Secor Lab isolates ( Table S1 ) are isolates sent to the CDC for drug resistance testing from patients who had previously failed at least two courses of standard therapy . Vaginal swabs were collected from 270 women attending eight New York City Department of Health and Mental Hygiene STD clinics in four of the five New York boroughs: Clinic G ( N = 29 ) in the Bronx , Clinic D ( N = 37 ) and Clinic F ( N = 29 ) in Queens , Clinic A ( N = 28 ) and Clinic E ( N = 37 ) in Brooklyn , Clinic B ( N = 36 ) , and Clinic C ( N = 41 ) , and Clinic H ( N = 33 ) in Manhattan . Approval for specimen collection and study was granted by the institutional review boards of NYU School of Medicine , the Centers for Disease Control and Prevention , and the New York City Department of Health and Mental Hygiene . IRB approval did not require informed consent since the study was deemed exempt . All samples were anonymized . Vaginal swabs collected during a routine pelvic examination , which would otherwise have been discarded , were used to inoculate InPouch TV culture kits [28] . Pouches were retrieved from clinics within three days of inoculation . Specimens were assigned a study ID and , after linkage to a limited set of demographic and clinical data , specimens were stripped of patient identifying information . Cultures were incubated at 37°C and examined for the presence of T . vaginalis by microscopy each day for five to seven days post-inoculation ( PI ) with vaginal swab . On day seven PI regardless of T . vaginalis diagnosis , 3 mL of culture was used for DNA extraction and the remaining culture media cryopreserved in 10% DMSO . Diagnostic PCR was also used to diagnose T . vaginalis , using published methods . Two primer sets TVK3/TVK7 [29] , [30] ( 5′-ATTGTCGAACATTGGTCTTACCCTC-3′ ) / ( 5′-TCTGTGCCGTCTTCAAGTATGC-3′ ) and TV16f-2/TV16r-2 [31] ( 5′-TGAATCAACACGGGGAAAC-3′ ) / ( 5′-ACCCTCTAAGGCTCGCAGT-3′ ) were used . Discrepancies between primer sets were resolved using a third primer set , BTub3f/BTUB_Bkmt [31] ( 5′-TCCAAAGGTTTCCGATACAGT-3′ ) / ( 5′-GTTGTGCCGGACATAATCATG-3′ ) . All diagnostic PCRs were performed at least twice , and samples were considered positive if T . vaginalis was detected by wet mount , in vitro culture and/or by PCR with two different primers . The UNSET buffer and phenol-chloroform extraction method was used for DNA isolation of all samples as described [26] . Isolates were genotyped at 21 T . vaginalis-specific microsatellite loci in 10 µL volumes as described [26] . Reactions were performed in duplicate and discrepancies were verified with a third reaction . GeneMapper 4 . 0 ( ABI , Foster City , CA ) was used to score MS allele sizes . All calls were manually edited to discard data from poorly amplified reactions and to ensure that proper allele calls were assigned . Mixed infections were detected by the presence of multiple alleles at two or more loci . Due to the amplification biases described by Havryliuk et al . ( 2008 ) [32] in validating the criteria for distinguishing between minor alleles and stutter peaks ( i . e . , minor peaks classified as >33% of the size of the major allele [33] , [34] ) , we relied on the reproducibility of minor alleles over three independent rounds of amplification and electrophoretic analysis of labeled PCR products , and the presence of multiple alleles at two or more loci , in order to detect mixed infections . Electrophoretic readouts were also compared between samples to determine stutter patterns , and ambiguous minor alleles were ignored . To ensure that the association of type 1 with TVV infection was not caused by interaction with the MS locus specific primers and the TVV genome , we performed a BLAST search of all known TVV genomes , including species I–IV , using each forward and reverse primer as a query . We found no more than 75% identity to any single primer , indicating that the TVV genome differs enough from the MS loci examined to prevent any unintended complementation . Microsatellite allelic richness was estimated using ADZE [35] , which implements the rarefaction method for analyzing allelic diversity across populations while correcting for sample size difference . Allelic richness estimates were graphed for each sample size ( g ) to estimate the sample size necessary to ensure that the majority of non-rare alleles had been detected . Isolates were grouped according to geographical origin ( or status as a laboratory strain ) using the following ten categories: Laboratory ( N = 5 ) , Western United States i . e . west of the Mississippi River ( N = 31 ) , Eastern United States i . e . east of the Mississippi River ( N = 51 ) , Mexico ( N = 11 ) , Chile ( N = 14 ) , Italy ( N = 12 ) , Southern Africa ( N = 19 ) , Australia ( N = 14 ) , Papua New Guinea ( PNG ) ( N = 30 ) , and India ( N = 1 ) . Genetic diversity was determined by calculating expected heterozygosity ( HE ) at each locus , using the formula HE = [n/ ( n−1 ) ][1−∑ni = 1 p2] where p is the frequency of the ith allele and n is the number of alleles sampled and confirmed with Arlequin3 . 11 [36] . Allelic richness ( a measure of the number of alleles independent of sample size ) per locus and sample ( Rs ) and over samples ( Rt ) was estimated using Mousadik and Petit's ( 1996 ) [37] method in FSTAT 2 . 9 . 3 . 2 [38] . FSTAT estimates the expected number of alleles in a sub-sample of 2n genes , given that 2N genes have been sampled ( N≥n ) , where n is fixed as the smallest number of individuals typed for a locus in a sample . The estimation is performed using the formula Rs = Σni = 1[1−[[ ( 2N−Ni ) /2n]/ ( 2N/2n ) ] , where Ni is the number of alleles of type i among the 2N genes . For Rt , the same sub-sample size n is kept , but N becomes the overall sample number of individuals genotyped at the locus under consideration . This program was chosen because allele frequencies are weighted according to sample sizes , important in our study due to the variation in the number of samples from different geographical regions . Arlequin3 . 5 [36] was used to test for Fst between geographical origins . The Bayesian clustering program STRUCTURE 2 . 2 was used to assign isolates to K populations according to allele frequencies at each locus [39] . The program was run 10 times each for six K values ( K = 1–6 ) with a burn-in period of 5×105 iterations followed by 105 iterations . The number of populations was inferred by plotting the log probability of the data [Ln P ( D ) ] for each K value , followed by clusteredness calculations . Clusteredness measures the average relatedness of the individual membership coefficients ( Q ) and estimates the extent to which individual infections belong to a single cluster , rather than to a combination of clusters [40] . Population differentiation was confirmed using Arlequin 3 . 5 . Two-way hierarchical clustering and inference of a minimum spanning network ( MSN ) were performed to validate clustering assignments determined using STRUCTURE 2 . 2 . Two-way hierarchical clustering was performed on MS data using JMP Genomics 5 . 0 ( SAS ) , and missing data points were assigned a unique number ( 999 ) to allow for the inclusion of all samples in the analysis . MSNs were inferred from individual MS haplotypes profiles using Network 4 . 516 [41] , software developed to reconstruct all possible least complex phylogenetic trees using a range of data types . Loci TVAG_005070 ( DNA mismatch repair homolog , postmeiotic segregation increased-1 , PMS1 ) , TVAG_302400 ( MutL homolog 1a , Mlh1a ) , and TVAG_021420 ( coronin , CRN ) were PCR amplified , purified , and sequenced as described [26] . Nucleotide sequence data is available in the EMBL , GenBanks and DDBJ data bases under the accession numbers: JN380351–JN380802 . Sequences were aligned to the reference sequence in GenBank and the alignments manually edited using Sequencher 4 . 8 ( Gene Codes Corporation , Ann Arbor , MI ) . SNPs were manually verified and included any single nucleotide change that occurred in any single strain . All three genes were successfully sequenced in 94 isolates . ModelGenerator v . 0 . 85 [42] was used to infer phylogenies from single copy gene sequences , with the number of gamma categories set at 10 to identify appropriate nucleotide substitution models for each of the loci . PhyML [43] as part of SeaView v . 4 . 2 . 4 [44] was used to infer maximum likelihood ( ML ) phylogenies reconstructed by applying simultaneous NNI ( Nearest Neighbor Interchange ) and SPR ( Subtree Pruning and Regrafting ) moves on five independent random starting trees . Substitution rate categories were set at ten and transition/transversion ( Ts/Tv ) ratios , invariable sites and across-site rate variation were selected as indicated by ModelGenerator . Support values for the tree were obtained by bootstrapping 1000 replicates . We inferred the evolutionary relationship of type 1 and type 2 isolates by phylogenetic analyses of the concatenated protein sequences of the three single copy genes and their orthologs in Tritrichomonas foetus and Pentatrichomonas hominis . Sequences for T . foetus and P . hominis orthologs were obtained from mining low coverage Roche 454 sequence data of each species , and contigs with high sequence similarity were aligned and manually edited using SeaView v . 4 . 2 . 4 [44] . Indel regions were deleted from the alignment , leaving 1147 aa aligned sequence . BioNJ [45] , a distance based phylogeny reconstruction method packaged in SeaView v . 4 . 2 . 4 was used to infer the phylogeny , using Poisson protein-level distances and 1000 bootstrap replicates . To detect linkage disequilibrium ( LD ) between the MS loci we calculated pairwise LD using the exact test for haplotypic data encoded in Arlequin . For single copy gene loci , we utilized the 49 SNPs found in alleles of the 94 isolates , and used the LDheatmaps [46] package in R [47] to plot the standardized measure of linkage disequilibrium between pairs of sites , r2 . As this program is designed for diploid organisms , we modified our haploid data by making all SNP genotypes homozygous . LIAN software version 3 . 5 was used to calculate ISA , a standardized index of association that tests for multilocus linkage disequilibrium , for MS loci . ISA is defined as ISA = ( VD/VE−1 ) ( r−1 ) , where ( VD ) is the variance of the number of alleles shared between all pairs of haplotypes observed in a population ( D ) , ( VE ) is the variance expected under random association of alleles , and r is the number of loci analyzed . VE is derived from 10 , 000 simulated data sets in which alleles were randomly reshuffled among haplotypes . For single copy gene loci , we used the software package MultiLocus 1 . 3b [48] . This program can accommodate haploid sequencing data and implements an algorithm for ISA that is independent of the number of loci analyzed . TVV infection in each parasite isolate was determined by isolating total RNA from 8–10 mls of late log phase cultures . RNA isolation was performed using Trizol ( Invitrogen ) according to the manufacturer's instructions . A total of 1 µg of total RNA was electrophoresed on a 1% agarose gel; the presence of rRNA bands on the gel served as a loading control to ensure that RNA from approximately equal number of parasites was examined . Gels were stained with ethidium bromide and isolates were considered positive for TVV if the characteristic ∼4 . 5 kb dsRNA genome band was detected .
In order to sample the genetic diversity and deduce the population structure of extant T . vaginalis in the local population , we collected a total of 270 vaginal swabs from female patients undergoing a pelvic examination at eight STD clinics in four boroughs of New York City ( NYC ) during the Summer of 2008 ( Table 1 ) . The average patient age was 27 . 7 years , and the majority self-identified as black non-Hispanic ( N = 133 , 49% ) or Hispanic ( N = 83 , 31% ) , with the remainder reporting as white non-Hispanic ( N = 17 , 6% ) , Asian non-Hispanic ( N = 9 , 3% ) , American Indian ( N = 1 , 0 . 4% ) , Multi-ethnic ( N = 3 , 1% ) , or other ( N = 10 , 4% ) . Data on ethnicity was unavailable for 14 patients ( 5% ) . Wet mount diagnosis was performed in all clinics whenever a laboratory technician was available . Wet mount detected five T . vaginalis infections , while in vitro culture using InPouch TV packs diagnosed 19 T . vaginalis infections , and PCR amplification using three different sets of diagnostic primers detected a total of 26 infections . All culture-positive infections were detected by PCR as well , and all wet mount-positive infections were detected by both culture and PCR diagnosis . Thus wet mount , when performed , detected a mere 36% of the infections detected by PCR , and only 42% of infections detected by InPouch culture , suggesting that this method of diagnosis is highly insensitive and detection and treatment of T . vaginalis would be improved through the use of more sensitive point-of-care tests . We detected T . vaginalis infections in 10% of women attending NYC STD clinics , which is lower than the prevalence found in other STD clinics in the United States , but remains within the published range of 8–47% [8] . To gauge the extent of T . vaginalis genetic diversity within NYC , we used our panel of 21 polymorphic microsatellite ( MS ) markers [26] to genotype 19 isolates ( seven infections detected by PCR could not be revived in culture to produce sufficient quantities of DNA for genotyping ) . One of the 19 isolates genotyped ( NYCE32 ) had more than two alleles at four MS loci , indicating a mixed infection , and was excluded from further analyses . We found that each of the remaining 18 single infections had a unique haplotype , indicating high genetic diversity of the parasite even within the geographically limited area of NYC . This finding was also reflected in the moderately high average expected heterozygosity ( HE = 0 . 67 ) and allelic richness estimate for a population size of g = 4 ( A = 3 . 24 ) . An average of 4 . 29 distinct alleles were identified per locus ( Table 2 ) . To determine if the moderately high genetic diversity exhibited by T . vaginalis isolates in NYC was unique to this geographic region , we extended our studies to include a set of 231 global samples , collected from nine countries: the United States , Mexico , Chile , Italy , South Africa , Mozambique , Australia , Papua New Guinea ( PNG ) , and India , and five standard laboratory strains commonly used in research labs ( Table S1 ) . Each sample was genotyped in duplicate for all 21 MS markers , and 216 were successfully genotyped at ≥13 of the loci ( Table 2 ) . A total of 23 mixed infections ( 10 . 6% ) was identified among the 216 isolates , 22 of which were double infections , while a Chilean isolate ( ANT1 ) appeared to be a triple infection ( two alleles identified at six loci and three alleles at a seventh locus ) . These isolates were excluded from further analysis . We found only four pairs of isolates from different geographical regions that shared haplotypes: three pairs were isolated from the Western United States ( isolates 886 and 1135; 938 and 907; 1020 and 1025 ) , and one was collected from both the Eastern and Western United States ( isolates 1027 and 1162 ) . In contrast , all eleven Indian isolates shared the same haplotype , unfortunately due to cross-contamination during their continuous culture in the same laboratory over many years . For this reason , we collapsed the same 11 genotypes to a single data point . A lack of shared haplotypes exhibited by our world-wide collection of T . vaginalis isolates is not due to incomplete sampling because graphing allelic richness estimates for each sample size revealed that the sampling had captured the majority of non-rare alleles ( Figure S1 ) . A variety of population genetics statistics for the 183 clinical single infections and 5 laboratory strains indicate that the global genetic diversity of T . vaginalis is high and stable from region to region . The mean expected heterozygosity across all MS loci is 0 . 66±0 . 197 , ranging from 0 . 04 ( MS03 ) to 0 . 83 ( MS17 ) respectively , and with an average of 8 . 52 alleles per locus ( minimum 3 . 0 at MS03 and maximum 29 at MS17; Table S2 ) . The expected heterozygosity is similarly high throughout all regions ( Table 2 ) , although statistically significant differences were apparent . For example , the T . vaginalis isolates from Chile , Western and Eastern United States and Australia are more diverse while the Southern Africa , Mexico and PNG isolates comprise a slightly less diverse group . We measured population differentiation between the geographical regions using FST measurements in Arlequin , and found that the Southern African and PNG parasite populations were significantly differentiated from the other global populations . The Mexican population differed from all other populations with the exception of the Italian population . The Chilean population differed from that of the Eastern United States , which was similar to the populations of both the Western United States and Australia ( Table S3 ) . Overall , we found that the least diverse groups differed the most from other global populations . Next , we looked for population structure among the 188 global isolates ( Table S1 ) . Using the Bayesian clustering model implemented in STRUCTURE 2 . 2 [39] , the most probable number of clusters ( populations ) was determined by plotting the log probability of the data [Ln P ( D ) ] for each k value followed by clusteredness scores . K = 2 coincided with a significant dip in the log probability of the data and received the highest clusteredness value ( 0 . 95 averaged across 10 independent simulations; Figure 1 ) . Interestingly , the two clusters , which we refer to as ‘type 1’ and ‘type 2’ , are present at nearly equal frequencies and are well distributed among all geographical locations as defined in Table S1 . Two exceptions to this are isolates from Southern Africa and Mexico , which are significantly biased towards type 1 and type 2 , respectively . Independent testing of this population structure was provided by two-way hierarchical clustering , which produced an identical clustering pattern , assigning the same isolates to the same clusters , and provided further evidence for a distinct two-type structure ( Figure S2 ) . Minimum spanning networks showed similar population differentiation , although we did not find perfect correlation ( Figure S3 ) . No evidence for further sub-population structure was found after repeating the analysis on each type individually . In addition to their geographical distribution , we investigated the temporal distribution of these two types . Likelihood ratios revealed no significant difference in the frequencies of the two types when isolates were categorized by the year in which they were isolated ( Figure S4 ) . To deduce which type is older in evolutionary history , we sequenced three single-copy genes – Coronin ( CRN ) , MutL Homolog 1a ( Mlh1a ) , and postmeiotic segregation increased 1 ( PMS1 ) , validated for phylogenetic analyses and described previously [26] – from 94 T . vaginalis isolates . Orthologs of these single-copy genes in two distant relatives of T . vaginalis [49] , Tritrichomonas foetus ( a trichomonad that infects the bovine urogenital tract ) and Pentatrichomonas hominis ( a human intestinal trichomonad ) , were identified , and used as outgroups to construct the phylogenetic tree for the concatenated protein sequences of the three genes . Although support at several nodes is weak , the phylogeny suggests that parasites more similar to type 1 existed before the emergence of parasites characteristic of type 2 ( Figure 2a ) . We also found that type 1 has greater allelic richness than type 2 , which further supports its ancestral nature , as the ancestral node would be expected to have accrued greater diversity ( Figure 2b ) . We sought to identify phenotypic differences between the two types by correlating available clinical data with genotype ( Table 3 ) . Although the mean age of women infected with type 1 parasites is 35 . 0 years compared to 30 . 9 years for women infected with type 2 parasites , this difference was not statistically significant . We also found no statistically significant difference in the vaginal pH of women infected with the different types , nor did we find a significant difference in the percentage of isolates associated with a positive whiff test ( a test used in the diagnosis of trichomoniasis ) . However , a highly significant difference was found in the minimum lethal concentration ( MLC ) of metronidazole necessary to kill isolates of the two types , with type 2 isolates demonstrating a mean MLC of 228 . 4 µg/ml of metronidazole , while type 1 isolates exhibited a mean MLC of 76 . 6 µg/ml of metronidazole . We also found that infection of T . vaginalis with the T . vaginalis virus ( TVV ) occurred significantly more frequently in type 1 isolates ( 112 of 154 isolates tested ) than in type 2 ( 42 of 154 isolates; Table 3 ) . Finally , our data – albeit insufficient for reliable statistics on this point – suggest that infections with type 1 parasites are more likely to be detected by wet-mount ( microscopic ) diagnosis than are infections with type 2 parasites; easier detection by microscopy might indicate a higher parasite load in type 1 infections . We used several population genetics tools to address the question of whether genetic exchange occurs in T . vaginalis: ( 1 ) Pairwise linkage disequilibrium ( LD: a locus to locus comparison to detect cases where specific alleles are found together more frequently than would be expected by chance alone ) ; ( 2 ) the standardized index of association ( IAS , : a measurement of the variance of the genetic distance between pairs of strains compared to variance in a shuffled matrix [50] ) ; and ( 3 ) Maximum Chi-Squared Tests for recombination ( Max Χ2: compares the distribution of polymorphic sites along paired sequences with those expected to occur by chance [51] ) . For parasites with a clonal population structure , i . e . , with no genetic exchange , the expectation would be to observe significant LD between loci and significant IAS between MS and single-copy gene alleles . We measured pairwise LD for each type using both MS loci and single-copy gene SNPs ( Figure 3 ) . Analysis of type 2 MS data revealed 42 cases of pairwise LD , while analysis of type 1 data MS revealed 15 ( Figure 3a ) . This difference was confirmed upon calculation of IAS , which is highly significant in type 2 ( IAS = 0 . 0153 , p≤1 . 00×105 ) but not significant in type 1 ( IAS = 0 . 0006 , p = 0 . 396 ) , suggesting that the MS loci of isolates comprising the latter are in linkage equilibrium , while those in the former are not . We also measured LD within and between the single-copy genes and found minimal LD for both types ( Figure 3b ) . Interestingly , strong LD is restricted and rare between the three genes in type 1 , whereas type 2 is characterized by higher LD distributed among all three genes . However , in both cases , it appears that there is little genome-wide linkage , suggesting that the excess LD in type 2 may be due to either a recent bottleneck , a recent loss of recombination , or even to a recent expansion of evolutionarily favorable mutations within the population . These results are also consistent with the IAS measurements calculated using LDheatmap . Breaking the three genes into linkage groups , we find that type 1 sequences have a non-significant IAS ( IAS = 0 . 0296 , p = 0 . 153 ) , while type 2 is marginally non-significant ( IAS = 0 . 0598 , p = 0 . 057 ) , and becomes significant when the classical Index of Association ( IA ) is calculated ( IA = 0 . 1195 , p = 0 . 046 ) . We tested for recombination events using Max Χ2 analysis in the bioinformatic program START2 ( Table S4 ) . Among the 36 alleles found during sequencing of the single-copy gene CRN from 202 T . vaginalis isolates , we identified one putative recombination event between alleles CRN-25 and CRN-36 ( Max Χ2 = 54 . 6422 , p = 0 . 030 ) . Using a p = 0 . 05 cutoff , we also identified 89 putative recombination events within 37 unique alleles identified from sequencing PMS1 of 144 T . vaginalis isolates , and 15 putative recombination events among the 41 unique alleles identified through sequencing Mlh1a of 110 T . vaginalis isolates . Finally , we compared the type assignments inferred by STRUCTURE from MS genotyping with phylogenies constructed from DNA sequences of each of the three single-copy genes ( Figure 4 ) . We found that the topologies are similar , each supporting a two-type population structure; however , in a number of cases , isolates from different types had identical SNP haplotypes within one gene but very different haplotypes within the other two genes . This suggests that some of our T . vaginalis isolates are recombinants that were generated through genetic exchange , which appears to occur within types and rarely between types . The DNA phylogenies are also shown in Figure S5 , where isolates are color-coded by geographical origin .
The existence of genetically different T . vaginalis ‘types’ has been suggested by several previous studies . Stiles et al . ( 2000 ) found ten distinct HSP70 RFLP subtypes using 36 global reference strains and isolates collected from patients in Mississippi [19] . Rojas et al . ( 2004 ) utilized RAPD markers to genotype 40 isolates from Cuba and identified four subtypes with dendrograms inferred using UPGMA ( unweighted pair group methods analysis ) [23] . Meade et al . ( 2009 ) employed RFLPs to type 129 U . S . clinical isolates and used phylogenetic methods to identify two major groups composed of five subgroups [20] . Snipes et al . ( 2000 ) utilized RAPD polymorphisms and neighbor-joining phylogenetic methods with 63 U . S . strains and identified two groups [22] . Our own work with single-copy genes and microsatellites and a small number of laboratory strains also suggested a two-group genetic structure [26] . This current study , however , is the first to conclusively demonstrate the global distribution of two T . vaginalis types using robust , reproducible and diverse population genetic markers to genotype >230 isolates from nine regions around the globe . Our use of a set of powerful population genetic statistical tools , ranging from cluster analysis to two-way hierarchical clustering , along with tests for recombination , has allowed a rigorous investigation into T . vaginalis genetic diversity , population structure and genetic exchange . This information will be essential for future investigation into the parasite's biology . We also demonstrate the utility of our panel of microsatellite markers to detect mixed genotype clinical infections . In a recent review , Balmer and Tanner discuss the theoretical and experimental work that suggests that mixed infections have a broad range of clinically relevant effects in a number of human pathogens , including effects on the host immune response , the ability to efficiently prevent and treat infection , and changes to pathogen and disease dynamics caused by intraspecific interactions , many of which can lead to pathogen evolution [52] . The availability of sensitive methods allowing the detection of multiple genotype infections in T . vaginalis research is likely to prove highly significant in understanding clinical trichomoniasis , as the ∼11% prevalence of mixed isolates identified in our study represents a non-trivial number of real T . vaginalis infections . How did the striking two-type population structure of T . vaginalis arise ? We propose three scenarios . We have previously hypothesized that the ancestor of T . vaginalis was an enteric pathogen ( as are most trichomonads ) that transitioned to the urogenital tract during its evolution [53] . It is possible that two separate colonization events occurred , producing two genetically distinct lineages within the urogenital niche . If genetic exchange in T . vaginalis is a rare event , this could explain the maintenance of the two lineages . Alternatively , it is possible that the two types evolved sympatrically after a single colonization event . The presence of the two types in nearly equal frequencies globally suggests that some form of balancing selection is maintaining both types in natural infections . Potential drivers for this balancing selection , i . e . , what causes one type to have an evolutionary advantage over the other under different selective conditions , may become apparent from studies characterizing phenotypic differences between the types . At this point , we have identified type-specific differences in frequency of T . vaginalis virus ( TVV ) infection and in susceptibility to metronidazole . As yet untested phenotypes that may be important in this context are: ( a ) differences in the ability to colonize the urogenital tracts of male versus female hosts; ( b ) a reduction in parasite fitness associated with metronidazole resistance when metronidazole treatment is not a selective force; or ( c ) differences in growth rates and virulence . Third and finally , the population structure reported here may have evolved when barriers arose that reduced the parasite's ability to undergo genetic exchange , causing gradual genetic isolation . In this respect it is interesting to note that ∼60% of the ∼160 Mb T . vaginalis genome consists of active transposable elements , virus-like repeats and retrotransposons [53] , foreign DNA whose parasitism of the genome could have influenced the mechanics of genetic exchange , for example chromosome pairing . Indeed , transposable elements have been postulated to play a significant role in facilitating ectopic recombination in Drosophila melanogaster [54] . In contrast to the near-equal frequencies of the two T . vaginalis types detected in most regions , we found significant bias toward type 1 in Southern African samples and toward type 2 in Mexican samples ( Figure 1 ) . The low sample number ( N = 11 ) for the Mexican isolates may explain why the frequencies appear to differ in this region; however , the 23 Southern African sources were comparatively diverse , comprised of asymptomatic women attending an antenatal clinic and symptomatic women attending an STD clinic . Our finding of a highly significant difference in the frequency of TVV infection between type 1 and type 2 may have important implications for understanding variation in T . vaginalis virulence and disease pathogenesis . TVV has been implicated in affecting the expression of cysteine proteinases and of a highly immunogenic protein family ( P250 ) on the parasite's surface [55]–[57] . In regard to the potential of such double-stranded RNA viruses to influence pathogenicity , it has been recently demonstrated that Leishmania RNA virus-1 controls the severity of mucocutaneous leishmaniasis by inducing Toll-like receptor 3 , and ultimately inducing proinflammatory cytokines and chemokines that increases susceptibility to infection [58] . In addition , the greater prevalence of the virus in one type over the other may suggest differences in the functionality of the RNAi machinery that has been identified in the T . vaginalis genome [53] . The interesting observation that type 2 isolates have a significantly higher MLC for metronidazole may also have repercussions for understanding the mechanism ( s ) of metronidazole resistance in T . vaginalis , which has so far eluded scientists [59]–[61] . Isolates with an in vitro aerobic MLC of greater than or equal to 50 µg/ml are considered resistant to metronidazole [62] , suggesting that the difference in median metronidazole MLCs of the two types may be clinically relevant ( 25 µg/ml vs . 200 µg/ml ) , and may have influenced the evolution of the species . For example , it is tempting to speculate that type 2 isolates may have diverged from type 1 isolates due to a selective advantage in being able to evade higher levels of metronidazole . This could account for the derived position of type 2 isolates in the T . vaginalis evolutionary tree ( Figure 2 ) , and could also explain their relative lack of diversity and genetic recombination , since there has been less time for mutations to accumulate in the more recently-evolved lineage . In addition , through limiting recombination , type 2 isolates may maintain favorable gene combinations such as those for increased metronidazole tolerance . It should be noted , however , that it is unlikely that metronidazole treatment has been adequately widespread to induce such selective evolution . A major goal of this work was to use population genetics to identify evidence of genetic exchange in T . vaginalis . The parasite divides mitotically in the host , and no gamete form or cell fusion has been observed in vitro . However , circumstantial evidence suggests that T . vaginalis parasites may be capable of infrequent genetic recombination or may have only recently lost this ability . For example , analyses have revealed that closely related isolates share biologically relevant phenotypes , such as metronidazole resistance , but this pattern has no correlation with geographical origin , suggesting a spread of the phenotypes by recombination and the presence of strong selection [25] . In addition , Cui et al . ( 2010 ) found reassortment of polymorphic TMAC pseudogenes that cannot be explained by a strictly clonal population structure [63] . More persuasively , analyses of the T . vaginalis genome identified a complete set of conserved meiotic genes , suggesting that the meiotic process remains under , or has only recently been relieved of , conservative selection pressure [53] , [64] . Tibayrenc and Ayala ( 2002 ) have outlined criteria and tests of clonality relating to eukaryotic parasites [65] . Among the criteria clonal organisms should meet are ( 1 ) the presence of over-represented , identical genotypes that are widespread; ( 2 ) evidence of linkage disequilibrium; and ( 3 ) the absence of segregating or recombinant genotypes . To address the first criterion , our studies found significant genotypic diversity ( average HE 0 . 66 ) and few shared haplotypes ( total two in four isolates ) among 188 T . vaginalis global isolates . The second criterion was addressed through analysis of the haplotypes generated using 21 microsatellite markers . Results of these tests indicated that T . vaginalis populations – and in particular type 1 – are in linkage equilibrium , indicative of genomes that have recently undergone recombination . Finally , we have identified recombination events between alleles of three different single-copy genes , providing evidence of recombinant genotypes . Taking these data as a whole , we infer that T . vaginalis does not fit the clonality model but rather appears to have undergone frequent genetic exchange in its recent evolutionary past . In addition , the presence of a complete set of meiosis-specific genes and the frequency ( ∼11% ) at which mixed infections encounter each other in the host , suggest that the parasite continues to be capable of recombination , although the rate at which it occurs and under what conditions in natural populations remain to be determined . The ability of T . vaginalis parasites to undergo genetic exchange has significant implications for the epidemiology and control of trichomoniasis . The Weismann hypothesis argues that genetic recombination functions to provide variation for natural selection to act upon , giving recombining species an evolutionary advantage in responding to selective pressures [66] . In other words , it provides opportunities for newly emerged , beneficial genes to be exchanged , potentially allowing them to be combined with other favorable genes , which may ultimately allow for the novel gene to become widespread throughout a population . This has obvious implications for such phenotypic traits as drug resistance , where a rare gene favorable to the parasite ( and unfavorable to the host ) may become widespread , with grave implications for treatment of the host . Not all consequences of genetic recombination are negative , however; should the mechanisms and conditions conducive to meiosis and genetic recombination in T . vaginalis be elucidated , important resources such as genetic crosses and quantitative trait loci ( QTL ) maps could be developed , significantly advancing our understanding of this neglected parasite .
|
The human parasite Trichomonas vaginalis causes trichomoniasis , the world's most common non-viral sexually transmitted infection . Research on T . vaginalis genetic diversity has been limited by a lack of appropriate genotyping tools . To address this problem , we recently published a panel of T . vaginalis-specific genetic markers; here we use these markers to genotype isolates collected from ten regions around the globe . We detect high levels of genetic diversity , infer a two-type population structure , identify clinically relevant differences between the two types , and uncover evidence of genetic exchange in what was believed to be a clonal organism . Together , these results greatly improve our understanding of the population genetics of T . vaginalis and provide insights into the possibility of genetic exchange in the parasite , with implications for the epidemiology and control of the disease . By taking into account the existence of different types and their unique characteristics , we can improve understanding of the wide range of symptoms that patients manifest and better implement appropriate drug treatment . In addition , by recognizing the possibility of genetic exchange , we are more equipped to address the growing concern of drug resistance and the mechanisms by which it may spread within parasite populations .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"infectious",
"diseases",
"sexually",
"transmitted",
"diseases",
"biology",
"microbiology",
"evolutionary",
"biology",
"parasitic",
"diseases"
] |
2012
|
Extensive Genetic Diversity, Unique Population Structure and Evidence of Genetic Exchange in the Sexually Transmitted Parasite Trichomonas vaginalis
|
The anaphase-promoting complex ( APC ) is an E3 ubiquitin ligase which controls ubiquitination and degradation of multiple cell cycle regulatory proteins . During infection , human cytomegalovirus ( HCMV ) , a widespread pathogen , not only phosphorylates the APC coactivator Cdh1 via the multifunctional viral kinase pUL97 , it also promotes degradation of APC subunits via an unknown mechanism . Using a proteomics approach , we found that a recently identified HCMV protein , pUL21a , interacted with the APC . Importantly , we determined that expression of pUL21a was necessary and sufficient for proteasome-dependent degradation of APC subunits APC4 and APC5 . This resulted in APC disruption and required pUL21a binding to the APC . We have identified the proline-arginine amino acid pair at residues 109–110 in pUL21a to be critical for its ability to bind and regulate the APC . A point mutant virus in which proline-arginine were mutated to alanines ( PR-AA ) grew at wild-type levels . However , a double mutant virus in which the viral ability to regulate the APC was abrogated by both PR-AA point mutation and UL97 deletion was markedly more attenuated compared to the UL97 deletion virus alone . This suggests that these mutations are synthetically lethal , and that HCMV exploits two viral factors to ensure successful disruption of the APC to overcome its restriction on virus infection . This study reveals the HCMV protein pUL21a as a novel APC regulator and uncovers a unique viral mechanism to subvert APC activity .
Regulation of protein degradation plays a key role in many cellular processes ranging from cell cycle progression , innate immunity , and antigen presentation to the turnover of misfolded or oxidized proteins . Most degradation is carried out by the ubiquitin-proteasome system ( UPS ) . Ubiquitin is added to proteins by a cascade of ubiquitin conjugating enzymes , resulting in a polyubiquitinated protein which is subsequently degraded by the 26S proteasome . As a means to regulate protein function , it is no surprise that many viruses have co-opted the UPS for their own benefit . Viruses can promote proteasome degradation of antiviral host proteins either by encoding their own E3 ubiquitin ligase , targeting proteins to a cellular E3 ligase , or even inducing ubiquitin-independent degradation of targets . Examples of viral E3 ligases include the herpes simplex virus-1 protein ICP0 [1] and Kaposi's sarcoma-associated herpesvirus proteins K3 and K5 ( for a review , see [2] ) . Viral proteins that can hijack a cellular E3 ligase include human immunodeficiency virus-1 vpr and vif ( for a review , see [3] ) , paramyxovirus V [4] , and human papillomavirus E6 and E7 ( for a review , see [5] ) . Finally , the human cytomegalovirus ( HCMV ) protein pp71 uses a ubiquitin-independent mechanism to target the Rb and hDaxx proteins [6] , [7] . In fact , pharmacological inhibition of the proteasome blocks multiple stages of the viral life cycle , suggesting that viruses rely on activities of the UPS for their replication [8]–[12] . On the other hand , viruses must also modulate cellular E3 ligase activity in order to replicate because ubiquitination regulates many important cellular processes central to virus infection . The SV40 large T antigen inhibits the SCFfbw7 ubiquitin ligase to increase cyclin E levels [13] , and influenza virus NS1 inhibits TRIM 25-mediated ubiquitination of RIG-I , thereby attenuating interferon production [14] . The anaphase-promoting complex ( APC ) or cyclosome is a macromolecular complex that contains cullin-ring E3 ubiquitin ligase activity and is conserved across all eukaryotes ( for a review , see [15] ) . It has at least eleven subunits and two co-activator proteins ( CDC20 ( cell-division cycle protein 20 ) and Cdh1 ( CDC20 homologue 1 ) ) , which are separated into three sub-complexes . These include the cullin-ring ligase domain ( composed of APC2 , 10 , and 11 ) , the specificity arm ( composed of APC3 , 6 , 7 , and 8 ) , and the bridge ( composed of APC1 , 4 , and 5 ) . Cdh1 and CDC20 activate APC activity to prevent premature entry into S phase and to promote progression through mitosis , respectively . The APC complex ubiquitinates more than 40 proteins , including A- and B- type cyclins , to regulate their stability . It also regulates degradation of its own coactivator proteins , Cdh1 and CDC20 , as a form of feedback regulation . Due to its central role in cell cycle progression , the APC is also a promising target for anti-cancer therapeutics [16] . Many viruses modulate the host cell cycle to establish optimal conditions for their replication . Several viral proteins have been reported to target the APC , possibly to force the cell into an S phase-like biochemical environment to promote efficient viral replication . Proteins from adenovirus , chicken anemia virus , human papillomavirus , human T-lymphotropic virus , hepatitis B virus , parapoxvirus , and HCMV have been reported to regulate the function of the APC [17]–[24] . However , the mechanisms used by these viruses during infection to subvert the APC are largely unknown . HCMV is a globally important opportunistic pathogen that causes severe diseases in immunocompromised individuals and is the leading viral cause of congenital diseases . This virus stimulates cell cycle progression of quiescent cells into an S phase-like environment but concurrently blocks host DNA synthesis [25] . HCMV promotes cell cycle progression likely in part by inactivating Rb [6] , [26] and regulating the APC [24] , [27] , [28] . It has been reported that HCMV has two means to regulate the APC . The multifunctional viral kinase pUL97 phosphorylates the APC coactivator Cdh1 , thus likely inhibiting its activity [24] . Nonetheless , abrogation of UL97 alone only results in a modest increase in APC activity during infection [24] . Independent of UL97-mediated Cdh1 regulation , HCMV also induces degradation of two APC subunits , APC4 and APC5 , leading to the dissociation of the complex during infection [24] . The viral factor and associated mechanism responsible for regulating degradation of the APC subunits have not been identified . In this study , we demonstrate that the HCMV protein pUL21a interacts with the APC , resulting in proteasome-dependent degradation of APC4 and APC5 . Expression of pUL21a dissociates the APC cullin-ring ligase subcomplex from its specificity arm . This regulation alters APC activity and increases levels of a subset of APC-regulated cell cycle proteins . We have identified residues proline-arginine ( PR109-110 ) in pUL21a to be critical for its ability to bind and regulate the APC . A mutant virus in which the viral ability to regulate the APC is abrogated by both alanine substitution of proline-arginine residues in pUL21a and UL97 deletion is markedly more defective compared to the pUL97 deletion virus alone . This suggests that HCMV has evolved an invasive strategy of using both viral factors to regulate the APC to facilitate its infection . Our study has identified the HCMV protein pUL21a as a novel APC regulator and elucidated a unique mechanism to subvert APC activity .
HCMV pUL21a is a 15 kDa , highly unstable protein that is expressed with early kinetics [29] . One identified function of this protein is to facilitate efficient viral DNA synthesis [30] . However , this protein shares no significant homology with any known protein . To provide mechanistic insight into its activity , we used a proteomics approach to identify interacting partners of pUL21a during infection . We created a recombinant virus ( ADgfpUL21a ) in which the UL21a coding sequence was tagged with the green fluorescent protein ( GFP ) coding sequence . This virus grew with wild-type kinetics , and the tagged protein was fortuitously much more stable than native pUL21a [29] . A GFP tag can stabilize certain fusion proteins [31] , and made it possible to detect interacting proteins in our study . We infected fibroblasts with either ADgfpUL21a or control HCMV ( ADgfp ) that expressed free GFP only . At 48 hours post infection ( hpi ) , we isolated the protein complexes from infected cells by a rapid one-step immunoaffinity purification on magnetic beads coated with GFP antibody-coupled protein A . Electrophoresis analysis revealed multiple protein bands that were specific to the pUL21a-containing sample ( Figure 1A ) . We analyzed pUL21a-specific protein bands by mass spectrometry and identified the proteins depicted with arrows as APC specificity arm subunits , APC3 , APC7 , and APC8 ( Table S1 ) . We validated these interactions in HCMV infected cells by co-immunoprecipitation followed by immunoblot analysis . Here we used APC3 and APC8 as the marker for the APC complex . Pull-down of pGFP-UL21a , but not the GFP control , isolated both APC3 and APC8 ( Figure 1B ) . The lower band detected by APC8 antibody was nonspecific as it neither co-immunoprecipitated with APC3 antibody ( Figure 1B ) nor was affected by shRNA knockdown of APC8 ( Figure S1 ) . In the reciprocal experiment , APC3 antibody co-immunoprecipitated APC8 and pGFP-UL21a but not GFP . Neither GFP nor APC3 antibody co-immunoprecipitated cellular PCNA ( Figure 1B ) , and an antibody against HA did not co-immunoprecipitate any of the proteins detected here ( data not shown ) , thus providing additional evidence for the specificity of these interactions . As pGFP-UL21a is co-immunoprecipitated with multiple APC subunits , we interpret the result to suggest that pUL21a binds to the APC complex , even though the precise subunit where pUL21a directly interacts with remains unknown . To determine if this interaction also occurred with native pUL21a , we performed co-immunoprecipitation assays on lysates from cells infected with wild-type virus ( ADgfp ) , and we also included lysate from cells infected with UL21a deletion virus ( ADsubUL21a ) as a negative control ( Figure 1C ) . Infected cells were treated with proteasome inhibitor , MG132 , as pUL21a was highly unstable and otherwise could not accumulate to levels allowing reproducible detection of this interaction [29] . In the presence of MG132 , the level of native pUL21a was markedly increased and could be co-immunoprecipitated with APC3 antibody . This interaction was specific as the antibody did not co-immunoprecipitate PCNA or the viral DNA polymerase accessory factor UL44 . To test if pUL21a was able to bind to the APC in the absence of other HCMV proteins , we performed co-immunoprecipitation assay on lysates from 293T cells transfected with constructs expressing the GFP-amino terminal tagged UL21a ( gfpUL21awt ) or UL21a carrying two stop codons at its amino terminus to abrogate pUL21a expression ( gfpUL21astop ) ( Figure 1D ) . Both gfpUL21awt and gfpUL21astop were expressed at equal levels but only gfpUL21awt associated with APC3 or APC8 . Additionally , APC3 antibody co-immunoprecipitated gfpUL21awt but not gfpUL21astop . We conclude that pUL21a interacts with the APC and does not require other HCMV proteins for this interaction to occur . To begin understanding the nature of this interaction , we identified the APC-binding domain of pUL21a . Sequence alignment of pUL21a with its homologues in chimpanzee CMV ( CCMV ) and Rhesus CMV ( RhCMV ) revealed a highly conserved N-terminus ( residues 1–47 ) , divergent middle region ( residues 48–83 ) , and C-terminus that contained several conserved residues ( residues 84–123 ) , including a proline-arginine ( PR ) pair at residues 109–110 ( Figure 2A ) . We created a series of truncation mutations targeting each region in the GFP-tagged pUL21a , and tested the ability of mutant UL21a proteins to interact with the APC in 293T cells ( Figure 2B ) . All mutants were expressed at similar levels and were efficiently immunoprecipitated by the GFP antibody ( Figure 2C , and data not shown ) . As expected , full-length gfpUL21awt co-immunoprecipitated both APC3 and APC8 while the gfpUL21astop mutant did not . Importantly , while the carboxyl-terminal fragment of pUL21a consistently co-immunoprecipitated APC3 and APC8 , the amino-terminal and middle fragments were unable to do so . Thus the carboxyl-terminus of pUL21a contains the APC binding domain . To define the precise sequence of the APC binding site , we made gfpUL21a mutants in which each of five conserved residue clusters within its carboxyl terminus were individually substituted with alanine residues ( Figure 2A ) . As a control , we also made alanine substitutions for the non-conserved proline-histidine pair at residues 111–112 ( PH111-112AA ) ( Figure 2A ) . All mutants were stable and were efficiently pulled down by the GFP antibody ( Figure 2D , and data not shown ) . Among them , only the PR109-110AA mutant lost the ability to bind to the APC . Substitutions of the adjoining non-conserved residues ( PH111-112AA ) had no effect on APC binding . To validate the result in the context of infection , we constructed recombinant HCMV viruses expressing GFP-tagged or native forms of PR109-110AA or PH111-112AA pUL21a variants ( ADgfpUL21aPR-AA , ADgfpUL21aPH-AA , ADpmUL21aPR-AA , and ADpmUL21aPH-AA ) . During infection , a reciprocal interaction between gfpUL21aPH-AA and APC3 could be detected while gfpUL21aPR-AA and APC3 did not interact ( Figure 2E ) . Furthermore , untagged pUL21aPH-AA , but not pUL21aPR-AA , was co-immunoprecipitated with APC3 when stabilized by MG132 ( Figure S2 ) . Together , these results indicate that the carboxyl terminus of pUL21a contains the APC binding domain and the residues PR109–110 are critical for this binding . It has recently been reported that the APC bridge subunits APC4 and APC5 are degraded during HCMV infection and the complex dissociates [24] . To test if pUL21a was required for these events , we first examined APC subunit accumulation during infection with or without pUL21a . Levels of APC4 and APC5 proteins were markedly reduced during wild-type infection relative to mock-infected cells at 24 hpi ( Figure 3A ) . However , no reduction was observed in APC4 and APC5 levels during infection with the UL21a-deletion virus . The pUL21a-deficient virus fails to express late viral genes due to a defect in viral DNA synthesis [30] . To rule out any role of late genes in APC4 and APC5 degradation , we treated infected cells with phosphonoacetic acid ( PAA ) to block viral DNA synthesis and late gene expression . APC4 and APC5 levels were reduced during infection with wild-type virus but remained elevated during infection with the UL21a-deletion virus , even following PAA treatment . Furthermore , there was no appreciable difference in APC4 and APC5 transcript levels between wild-type and deletion virus infections ( Figure 3B ) . These data suggest that the changes in APC4 and APC5 protein levels occur at the level of protein stability . Consistent with this hypothesis , MG132 enhanced APC4 and APC5 protein levels during infection with wild-type but not deletion virus ( Figure 3C ) . Thus , pUL21a-mediated loss of APC4 and APC5 was due to proteasomal degradation . Moreover , the APC binding mutant virus ADpmUL21aPR-AA was unable to degrade APC4 and APC5 while the ADpmUL21aPH-AA virus was as efficient as the wild-type control virus . These data support the conclusion that pUL21a binding to the APC promotes proteasomal degradation of APC4 and APC5 . We next tested if the APC binding ability of pUL21a was also required for APC dissociation during infection . In this experiment , we used APC3 and APC10 as the marker for the specificity arm and cullin-ring ligase subcomplex of the APC , respectively . These two subcomplexes sit on opposite sides of the APC . APC10 has been proposed to bind APC substrates along with coactivator proteins , including Cdh1 [32] . APC10 associates with APC2 and APC11 of the ligase subcomplex , but its location in the inner cavity of the APC allows for contact with APC3 and APC6 of the specificity arm . In cells infected with ADpmUL21aPR-AA , total levels of APC3 and APC10 were similar to those in cells infected with ADpmUL21aPH-AA , allowing for a direct analysis of the efficiency of their association with the complex ( Figure 3D ) . APC3 could not co-immunoprecipitate APC10 in ADpmUL21aPH-AA-infected cells , consistent with dissociation of the complex in the presence of functional pUL21a . In cells infected with ADpmUL21aPR-AA , APC3 was able to pull down APC10 efficiently , indicating that the two subcomplexes remained associated . Finally , the integrity of the APC during ADpmUL21aPH-AA infection was largely restored upon addition of MG132 , even though total protein levels were reduced likely due to MG132-induced cell death ( Figure 3D , and data not shown ) . These data were recapitulated during infection of wild-type and UL21a deletion viruses ( Figure S3 ) . Our data provides strong evidence supporting the model that binding of pUL21a to the APC induces degradation of the APC bridge arm resulting in complex dissociation . As APC8 was co-immunoprecipitated with pUL21a in our original screen , it raised the possibility that pUL21a might require APC8 to target APC4 and APC5 . For instance , pUL21a might bind to APC8 to disrupt the structure of the APC leading to APC4 and APC5 degradation , or it might use APC8 as a docking site for recruiting protein degradation enzymes to target APC4 and APC5 . To test this , we depleted APC8 in these cells by shRNA knockdown ( Figure S4 ) . Following shRNA depletion of APC8 , the APC4 and APC5 levels remained reduced in cells infected with wild-type virus compared to those with UL21a-deletion virus , even though APC knockdown did seem to affect the overall stability of APC4 and APC5 in pUL21a-independent manner ( Figure S4 ) . This suggests that pUL21a-mediated degradation of APC4 and APC5 is independent of APC8 . To determine the functional consequence of pUL21a-dependent APC dissociation , we first analyzed the accumulation of APC substrates during wild-type or UL21a-deletion virus infection . The protein levels of APC substrates Cdh1 ( that is also an APC co-activator ) and geminin were markedly increased in wild-type virus infection as previously reported [27] , [33] ( Figure 4A ) . However , their levels were reduced during infection with the UL21a-deletion virus , suggesting increased APC activity . The geminin transcript accumulated to wild-type levels even without pUL21a , providing evidence that the difference in protein accumulation was not due to transcriptional regulation ( Figure 4B ) . PAA treatment had no effect on substrate accumulation , ruling out pUL21a-mediated late gene expression as the source of the observed phenotype ( Figure S5A ) . MG132 largely restored substrate levels during UL21a deletion viral infection , indicating that the difference is likely due to increased proteasome degradation ( Figure 4C ) . These results were also recapitulated during infection of APC binding mutant virus ADpmUL21aPR-AA and its control virus ADpmUL21aPH-AA ( Figure 4C ) . To confirm that decreased APC substrate accumulation during mutant virus infection was due to APC activity , we used shRNAs to knock down APC8 or the coactivator Cdh1 to deplete APC activity . Both APC8 and Cdh1 shRNAs efficiently reduced expression of their respective targets ( Figures 4D and S5B ) . Importantly , APC8 knockdown restored geminin and Cdh1 levels in cells infected with ADpmUL21aPR-AA or ADsubUL21a virus to those with ADpmUL21aPH-AA or ADgfp virus . Likewise , Cdh1 knockdown restored geminin levels in cells infected with the pUL21a-deficient viruses . Thus , our results indicate that pUL21a association with the APC allows it to target APC4 and APC5 subunits for degradation to alter APC activity during infection . It is noteworthy that not all APC substrates were subjected to pUL21a-mediated regulation . We did not observe significant difference in Cdc6 or a drastic reduction in thymidine kinase protein levels in the UL21a mutant relative to wild-type viral infection ( data not shown ) . It is possible that these APC substrates are regulated by multiple mechanisms , including APC-independent viral regulation , pUL21a-mediated alteration in APC substrate specificity , and pUL97-mediated phosphorylation of the APC coactivator Cdh1 . In fact , Cdh1 from both wild type and UL21a mutant virus infected cells migrated slower in an SDS-PAGE gel compared to that from mock cells , which was previously shown to be due to phosphorylation ( Figure 4E ) [28] . Therefore , virus-induced , Cdh1 phosphorylation-mediated APC regulation appears intact even without pUL21a during HCMV infection . As the APC prevents the premature entry of the cell cycle into S phase , we predicted that increased APC activity in the absence of pUL21a would not compromise the ability of HCMV to arrest infected cells at G1/S phase boundary . Consistent with this hypothesis , cells infected with wild type , ADpmUL21aPH-AA , or ADpmUL21aPR-AAvirus showed indistinguishable cell cycle profiles throughout infection , with the majority of cells phenotypically arrested in G1 phase ( Figure S6 ) . To test if pUL21a was sufficient to alter APC activity , we first analyzed 293T cells that over-expressed pUL21a by transient transfection . Expression of pUL21a alone was sufficient to markedly reduce the levels of APC4 and APC5 ( Figure S7A ) , and as expected , geminin and Cdh1 levels were elevated in these cells . These pUL21a-expressing cells were largely arrested in G2/M phase ( Figure S7B ) , failed to multiply , and ultimately died ( Figure S7C ) . The biological characteristics of pUL21a-expressing cells are therefore consistent with reduced APC activity . To more precisely test if pUL21a was able to regulate the APC in the absence of other HCMV proteins , we developed an inducible pUL21a expression system . We constructed a HeLa cell line stably expressing a GFP-tagged TetR ( tetracycline repressor ) gene . We then transduced this cell line with lentiviruses expressing pUL21astop , pUL21aPH-AA , or pUL21aPR-AA under a CMV-TetO ( tetracycline operator ) promoter . pUL21a protein accumulation was only detected in the presence of tetracycline , suggesting tight regulation of pUL21a expression ( Figure 5A ) , although its levels were significantly lower than those expressed in transiently transfected cells ( Figure S7A ) . Importantly , the addition of tetracycline significantly reduced APC4 and APC5 protein levels in cells expressing pUL21aPH-AA , but not pUL21astop or pUL21aPR-AA . To assess the consequence of pUL21a on APC activity , we synchronized cells expressing pUL21aPH-AA ( i . e . wild-type pUL21a ) in mitosis with nocodazole and then assayed their ability to progress out of mitosis after release from nocodazole treatment . In the absence of tetracycline and pUL21a , cells readily progressed through the mitotic phase following release . In the experiment shown in Figure 5B , 26% and 48% of cells entered the next G1 phase by 2 and 4 hours , respectively . In the presence of tetracycline where pUL21a was expressed , progression through the mitotic phase was clearly delayed . As the result , only 5% and 24% of cells reached G1 by 2 and 4 hours , even though by 8 hours most of pUL21a-expressing cells were able to enter G1 , likely due to low expression of pUL21a in these cells as compared to those in transiently transfected cells . Additionally , following nocodazole withdrawal , APC substrates geminin and cyclin B1 remained elevated in the presence of tetracycline while their levels were reduced in its absence ( Figure 5C ) . Our results provide strong evidence that pUL21a expression alone is sufficient to regulate APC activity . In the final experiments , we tested the consequence of pUL21a-mediated APC regulation on HCMV replication in fibroblasts . We first tested if the ability of pUL21a to regulate the APC would be responsible for its previously reported role in promoting viral DNA replication [30] . We compared the growth of ADpmUL21aPR-AA mutant virus ( i . e . pUL21a APC-binding deficient ) to that of wild-type , ADpmUL21aPH-AA ( i . e . pUL21a APC-binding competent ) , or UL21a deletion viruses in multi-step growth curve analysis . We found that ADpmUL21aPR-AA grew indistinguishably from wild-type and ADpmUL21aPH-AA viruses in both cycling and G0-synchronized fibroblasts , whereas the UL21a deletion virus had a 100-fold defect ( Figure 6A ) [29] . Furthermore , knockdown of Cdh1 and APC8 was unable to enhance UL21a-deletion virus replication ( data not shown ) . This suggests that pUL21a has at least two independent activities . One is to facilitate viral DNA replication via an unknown mechanism and is responsible for the growth defect of UL21a deletion virus . The second activity is to regulate the APC , whose impact on virus replication is not apparent under the aforementioned experimental conditions . As two HCMV proteins , pUL97 and pUL21a , are capable of regulating the APC , we hypothesized that one of these two proteins acted to compensate for the loss of the other during infection . Consistent with this hypothesis , HCMV appeared to retain the ability , at least to some extent , to regulate the APC even when pUL21a or pUL97 is absent ( Figure 4E , and data not shown ) [24] . To test this hypothesis more directly , we created recombinant HCMV viruses ADpmUL21aPH-AA/subUL97 and ADpmUL21aPR-AA/subUL97 . The two viruses were derived from ADpmUL21aPH-AA and ADpmUL21aPR-AA , respectively , and both contained an additional deletion in UL97 . Both recombinant viruses grew slower than wild-type virus due to lack of the multifunctional pUL97 protein ( Figure 6C ) . However , reconstitution of ADpmUL21aPR-AA/subUL97 that lacked pUL21a APC-binding activity following BAC transfection was markedly slower than that of ADpmUL21aPH-AA/subUL97 ( Figure 6B ) . At day 25 post transfection , while cells transfected with the BAC clone of ADpmUL21aPH-AA/subUL97 showed nearly 100% of CPE indicated by virus-driven GFP expression , GFP-positive foci in cells transfected with the BAC clone of ADpmUL21aPR-AA/subUL97 were distinctly smaller . Furthermore , multi-step growth curve analysis showed that titers of ADpmUL21aPR-AA/subUL97 were 13- and 14- fold lower than that of ADpmUL21aPH-AA/subUL97 at 14 and 21 days post infection ( dpi ) , respectively ( Figure 6C ) . As a control to show that this phenotype was not due to general viral attenuation resulting from the UL97 deletion , we also constructed double mutant viruses ADpmUL21aPH-AA/subUL117 and ADpmUL21aPR-AA/subUL117 . These two viruses were derived similarly from ADpmUL21aPH-AA and ADpmUL21aPR-AA , but also contained a deletion in viral gene UL117 . We chose UL117 as the control because its mutation attenuated virus growth but not viral early or early-late gene expression so UL97 expression was unlikely affected [34] . BAC transfection reconstituted both mutant viruses at similar efficiency and produced viruses with similar titers ( data not shown ) . Multi-step growth analysis demonstrated that ADpmUL21aPH-AA/subUL117 and ADpmUL21aPR-AA/subUL117 replicated at similar kinetics ( Figure 6D ) . At 14 dpi , the titer of ADpmUL21aPR-AA/subUL117 was slightly lower ( e . g . 1 . 5-fold ) than that of ADpmUL21aPH-AA/subUL117 . However , growth of mutant virus carrying only the UL117 deletion tracked with ADpmUL21aPR-AA/subUL117 , suggesting that the difference between the PH and PR mutants at 14 dpi , if any , is minimal . Together , our data provide evidence that disruptions of both pUL97 and the APC regulatory activity of pUL21a are synthetically lethal to HCMV replication . This is consistent with a working model that these two functions enable HCMV to cope with APC activity to promote virus replication ( Figure 7 ) . In sum , we have shown that the HCMV protein pUL21a antagonizes the APC by promoting proteasome-mediated disruption of this prominent cellular E3 ubiquitin ligase .
HCMV has been shown to have two different means to regulate the anaphase-promoting complex ( APC ) during infection [24] , [27] , [28] . It can induce phosphorylation of APC co-activator Cdh1 , and it induces dissociation of the complex by promoting proteasomal degradation of two components of the bridge subcomplex , APC4 and APC5 . The viral protein pUL97 appears to be responsible for Cdh1 phosphorylation [24] . However , pUL97 is an HCMV-encoded kinase that has many reported roles [26] , [35] . How this particular pUL97 activity impacts HCMV infection remains elusive . Importantly , the viral factor or precise molecular mechanism mediating APC4 and APC5 degradation has not been identified , and how APC disruption contributes to HCMV replication is not known . Here , we have identified the HCMV protein pUL21a as the viral factor that mediates APC disruption . It does so by interacting with the APC and inducing proteasome-dependent degradation of APC4 and APC5 , which results in complex dissociation . This is the first identified viral protein that modulates the APC in this manner . We also show , for the first time , the impact of viral modulation of the APC , particularly by pUL21a , on HCMV replication . Loss of pUL21a-mediated APC regulation has minimal impact on virus replication but the combined loss of both pUL97- and pUL21a-mediated regulation markedly attenuates growth of the virus relative to single loss of pUL21a- or pUL97- mediated regulation . Our studies support a working model in which HCMV uses pUL97-mediated Cdh1 phosphorylation and pUL21a-mediated complex disruption to control APC activity for efficient virus infection ( Figure 7 ) . Why has HCMV developed these two distinct mechanisms that seemingly lead to a similar biological consequence ? It is possible that these two mechanisms have differential roles in HCMV infection under different conditions or in particular cell types , even though either one seems sufficient and can compensate for loss of the other in fibroblasts . Alternatively , it is possible that these two mechanisms serve as the fallback for one another or act synergistically to maximize the ability of the virus to acquire a complete control of the APC during infection . In any event , the fact that HCMV uses multiple means to subvert the APC underlines its critical role in HCMV infection . This is particularly true for large DNA viruses such as HCMV , which often encode multiple viral factors to regulate the same or related cellular targets central to their infection [36] . However , it is often challenging to dissect these intertwined viral mechanisms during infection because of the presence of other factors targeting the same process . The regulation of the APC represents one such critical but complex viral regulatory strategy , and our studies shed light into its role and mechanism during HCMV infection . Several viral factors from different viral families have been reported to use diverse mechanisms to regulate the APC . For instance , the human papillomavirus E2 protein binds to and inhibits the Cdh1 activator protein [20] , while the parapoxvirus virus protein PACR ( poxviral APC regulator ) functions as an enzymatically inactive APC11 mimic [23] , [37] . The chicken anemia virus ( CAV ) protein apoptin can bind to the APC at the bridge and cause its dissociation using an unknown mechanism [19] . The fact that proteins from both HCMV and CAV target the APC bridge subcomplex suggests that viruses have evolved regulatory strategies converging on this sub-complex as an efficient means to disable APC activity . It is intriguing to speculate that modulating the APC complex by dissolving the bridge may allow viruses to alter substrate specificity of the APC instead of completely abolishing its activity , as the enzymatic portion of APC is known to have activity in vitro [23] , [38] . HCMV does not appear to directly destroy the enzymatic subcomplex of APC , so it is of interest to determine if the APC retains some activity or is directed to target different substrates during virus infection . Several viral proteins have now been reported to regulate the APC in overexpression , and evidence correlating the role of these factors and viral replication is emerging . Deletion of the parapoxvirus PACR or CAV protein apoptin markedly attenuated virus growth in tissue culture even though their ability and role in inhibiting the APC during infection has not been clarified [23] , [39] . Recently , the UL97 kinase of HCMV has been shown to phosphorylate Cdh1 and partially inhibit the APC during infection but with unknown consequences for viral replication [24] . Our study elucidates the mechanism by which pUL21a regulates APC in the context of virus infection and indicates a role of this pUL21a activity in viral replication . Mutation abolishing the APC binding activity of pUL21a had no impact on viral growth in tissue culture , but the loss of both pUL21a-APC binding and pUL97 markedly attenuated viral replication relative to the loss of pUL97 alone . Our data suggest that HCMV has evolved a sophisticated strategy by encoding both pUL97 and pUL21a to overcome APC activity . However , further experiments are needed to unequivocally demonstrate the vital role of APC regulation in HCMV replication and provide mechanistic insight into how this regulation impacts its biology . How does pUL21a target APC4 and APC5 for proteasome degradation ? pUL21a does not contain a sequence domain that would suggest it as an E3 ligase , thus likely ruling out this possibility . Currently , we also do not know which subunit of the APC complex that pUL21a directly binds to so the precise mechanism that it uses to degrade APC4 and APC5 remains elusive . It is certainly possible that pUL21a may bind to a subunit neighboring to APC4 and APC5 so it can disrupt the APC structure leading to APC4 and APC5 degradation , or recruit a protein degradation enzyme ( e . g . E3 ubiquitin ligase ) to destabilize the subunits . However , knockdown of APC8 does not abrogate the ability of pUL21a to degrade APC4 and APC5 , suggesting that APC8 is not involved and the presence of the entire complex is not required . Intriguingly , pUL21a itself is a highly unstable protein and likely degraded in a ubiquitin-independent manner [29] , [40] . It is tempting to speculate that pUL21a may directly bind APC4 and APC5 and target them for degradation in a ubiquitin-independent manner . One focus of future work is to identify the APC component that pUL21a directly binds to and elucidate the mechanism of how pUL21a targets APC4 and APC5 to the proteasome . What would be the benefit for the virus to alter APC activity ? The APC may restrict HCMV replication via several mechanisms . The APC not only promotes cell cycle progression through M phase , it also prevents cells from prematurely entering S phase . Thus virus-mediated APC regulation may help HCMV maintain an S phase-like cellular environment for viral replication . The APC targets more than 40 proteins for degradation , so it may deplete host factors critical to viral replication . Consequently , viruses may need to alter the substrate specificity of the APC or allow accumulation of APC substrates critical for viral replication . Interestingly , the only viruses within the poxvirus and herpesvirus families that are known to modulate the APC ( e . g . parapoxviruses and HCMV ) are those that do not encode viral thymidine kinase ( TK ) and ribonucleotide reductase subunit M2 ( RRM2 ) . Both enzymes are APC substrates and critical for the production of deoxyribonucleotides . It is tempting to speculate that this viral regulation of the APC may provide viruses a means to produce sufficient nucleotides to replicate their genome [23] , [27] . Nonetheless , the APC also targets proteins involved in cellular DNA synthesis , glycolysis and glutaminolysis , and cell cycle progression , all of which could impact viral replication [41] . Moreover , the APC may also promote ubiquitination and degradation of viral proteins to restrict infection [42] . Several HCMV proteins contain a putative destruction Box ( D-box ) motif , an APC recognition signal commonly found in its substrates [24] . Future work is needed to differentiate these possibilities and unravel the APC substrates that may be critical for viral replication . Insight into the mechanism of pUL21a-mediated APC regulation may also have broad impact on cancer and neuronal disease . Due to its essential role in cell cycle progression , the APC is a promising target for novel anti-cancer therapeutics [16] , [43] . In fact , we found in this study that overexpression of pUL21a essentially prevented the proliferation of a transformed cell line ( Figure S7 ) , suggesting that pUL21a regulation of the APC could inhibit cancer cell growth . Furthermore , several recent studies have also highlighted a vital role of the APC in neuronal development ( for a review , see [44] ) . HCMV infects neuronal cells and congenital HCMV infection leads to neuronal disease and severe complications such as blindness , hearing loss , and mental retardation . It is reasonable to speculate that inhibition of the APC by pUL21a may play a role in promoting neuronal disease in congenitally infected infants . Therefore , an understanding of pUL21a-APC interaction may reveal novel mechanisms of APC assembly and regulation , give further impetus to target the APC for anti-cancer therapies , and uncover new insights into the molecular basis of HCMV pathogenesis .
Primary embryonic lung fibroblasts ( MRC-5 ) , human newborn foreskin fibroblasts ( HFFs ) , 293T , and Hela cells were propagated in Dulbecco's modified Eagle medium ( DMEM ) supplemented with 10% fetal bovine serum , non-essential amino acids , and penicillin-streptomycin . Transient transfection of expression constructs were carried out using lipofectamine according to the manufacturers' instructions . pYD-C235 is a pLPCX-derived retroviral vector ( Clontech ) that expresses a DsRed gene driven by an internal ribosome entry site 2 ( IRES2 ) [45] . pYD-C474 was created by PCR amplifying the coding sequence of the pGFP-UL21a fusion protein from pADgfpUL21a ( see below ) and ligating it into the multiple cloning site of pYD-C235 . pYD-C580 was created by replacing the coding sequence of wild-type UL21a in pYD-C474 with that of mutant UL21a carrying two stop-codon mutations at the N-terminus ( i . e . UL21astop ) [30] . Vectors expressing pGFP-UL21a truncation mutants were derived from pYD-C235 while vectors expressing point mutants were derived from pYD-C474 . Truncation mutants were made by PCR amplifying the targeted UL21a coding sequences and point mutants were created using a QuickChange XL kit ( Stratagene ) . Primers used to create these mutants are listed in Table S2 . pYD-C160 , pYD-C175 , and pYD-C682 are pRetro-EBNA derived retroviral expression vectors that expressed GFP , UL21a , and UL21astop , respectively . pYD-C648 and pYD-C649 are pLKO-based lentiviral vectors expressing GFP-TetR and carrying the CMV-TetO2 promoter , respectively ( generous gifts from Roger Everett , University of Glasgow Centre for Viral Research ) [46] . YD-C665 , YD-C667 , and YD-C669 are lentiviral expression vectors created by cloning the UL21astop , UL21aPH-AA , and UL21aPR-AA sequences into the multiple cloning site of YD-C649 . To produce pLKO-based lentiviruses , 293T cells were transfected with corresponding pLKO vectors along with packaging plasmids . Lentivirus was collected at 48 and 72 hours and used to transduce MRC-5 cells . To create GFP-TetR expressing stable cells , Hela cells were transduced with pYD-C648 derived lentivirus and sorted for GFP expression 48 hours later . GFP-positive cells were collected , grown in the presence of G418 ( 500 µg/ml ) , and frozen as cells stably expressing GFP-TetR . These stable cells were then transduced with lentivirus derived from YD-C665 , YD-C667 , and YD-C669 , selected with puromycin ( 2 µg/ml ) , and tested for tetracycline ( 1 µg/ml ) -regulated expression of targeted genes . For shRNA knockdown , MRC-5 cells were transduced with pLKO-based lentivirus expressing shRNA against the targeted gene for 48 hours . The shRNA sequence for Cdh1 knockdown was 5′CCAGTCAGAACCGGAAAGCCA3′ and the shRNA sequence for APC8 knockdown was 5′GCAGGAGGTAATATGCTATAA3′ . All pLKO-based shRNA lentiviral vectors were purchased from the Washington University Children's Discovery Institute/Genome Center . The primary antibodies used in this study included anti-β actin ( AC-15 , Abcam ) ; anti-HA ( HA . 11 , Covance ) ; anti-GFP ( 3E6 and A6455 , Invitrogen ) ; anti-APC3 ( AF3 . 1 , Santa Cruz and 610454 , BD ) ; anti-APC8 ( 6114 , Biolegend ) ; anti-APC4 ( A301-176A , Bethyl laboratories ) ; anti-APC5 ( A301-026A , Bethyl laboratories ) ; anti-geminin ( sc-13015 , Santa Cruz ) ; anti-Cdh1 ( DH01 , Calbiochem ) ; anti-cyclin B1 ( ms868 P1 , Thermo-Scientific ) ; anti-UL21a [29]; anti-IE2 ( mAB8140 , Chemicon ) ; and anti-IE1 and anti-pp28 ( generous gifts from Thomas Shenk , Princeton University ) [45] . Phosphonoacetic acid ( PAA ) , MG132 , tetracycline , gancyclovir ( GCV ) , and propidium iodide ( PI ) were purchased from Sigma-Aldrich . Lipofectamine 2000 and Protein A-conjugated Dynabeads were purchased from Invitrogen . Recombinant HCMV AD169 viruses were reconstituted from transfection of corresponding BAC-HCMV clones as previously described [34] . Viral stocks were prepared by ultra-centrifugation of infected culture supernatant through 20% D-sorbitol cushion and re-suspending pelleted virus in serum-free medium . The following BAC-HCMV clones were used in the present study , and were constructed using PCR-based linear recombination as previously reported [29] , unless indicated otherwise . pAD-GFP , which carried the GFP-tagged genome of the HCMV AD169 strain , was used to produce wild-type virus ADgfp [45] . pADgfpUL21a , which carried an N-terminally GFP-tagged version of pUL21a , was used to produce ADgfpUL21a virus [29] . pADsubUL21a , which carried a GalK/kanamycin dual mutagenic cassette in place of the UL21a coding sequence , was used to produce UL21a-deletion virus ADsubUL21a [29] . pADgfpUL21aPR-AA , pADgfpUL21aPH-AA , pADpmUL21aPR-AA , or pADpmUL21aPH-AA carried point mutation PR109-110AA or PH111-112AA in the GFP tagged or native UL21a gene , respectively . These recombinant BAC clones were used to produce corresponding point mutant viruses . pADpmUL21aPH-AA/subUL97 and pADpmUL21aPR-AA/subUL97 carried the GalK/kanamycin mutagenic cassette in place of UL97 on the background of pADpmUL21aPR-AA and pADpmUL21aPH-AA BAC clones . Similarly , pADpmUL21aPH-AA/subUL117 , pADpmUL21aPR-AA/subUL117 , and pADsubUL117 carried the GalK/kanamycin mutagenic cassette in place of UL117 on the background of pADpmUL21aPR-AA , pADpmUL21aPH-AA , and pAD-GFP BAC clones , respectively . All BACs were confirmed by restriction digestion , PCR , and sequencing . HCMV virus titers were determined in duplicate in HFFs by tissue culture infectious dose 50 ( TCID50 ) assay or plaque assay . Relative viral genome numbers were determined by real-time quantitative PCR ( qPCR ) as described previously [29] . For most infections , subconfluent MRC-5 cells in serum-containing medium were inoculated with recombinant HCMV virus at an input genome number equivalent to that of 3–5 infectious units of wild type virus/cell for 1 hour , unless otherwise indicated . Inoculum was removed and fresh medium was replenished . For infection of G0-synchronized cells , MRC-5 cells were incubated in serum-free medium for 72 hours , infected as described above , and maintained in serum-free media throughout the infection . For shRNA knockdown experiments , subconfluent MRC-5 cells were transduced with lentivirus for 24 hours , incubated in fresh medium for additional 48 hours , and infected as described above . When necessary , PAA ( 100 µg/ml ) was added immediately following infection , and MG132 ( 10 µM ) was added 12–14 hours prior to harvest . For viral growth analysis , virus production in the media of infected cultures was determined by TCID50 , plaque assay , or qPCR . For qPCR analysis , virion DNA was prepared as previously described [29] . Briefly , cell-free supernatants were treated with DNase I to remove contaminating DNA , and virions were lysed with proteinase K and SDS . DNA was extracted with phenol/chloroform/isoamyl alcohol and precipitated with ethanol . The DNA was subjected to qPCR using primers and a taqman probe specific for UL54 . For immunoprecipitation , frozen cell pellets were lysed in lysis buffer ( 0 . 5% NP-40 , 50 mM Tris-Cl pH 8 . 0 , 125 mM NaCl , supplemented with protease and phosphatase inhibitors ) using an end-over-end rotator at 4°C for 30 minutes . Cell extracts were cleared by centrifugation at 16 , 000× g for 15 minutes . Supernatants were incubated with protein A-coated Dynabeads that were coupled to 1 µg anti-HA ( HA . 11 , Covance ) , 1 µg anti-GFP ( 3E6 , Invitrogen ) or 2 µg anti-APC3 ( AF3 . 1 , Santa Cruz ) mouse monoclonal antibodies at 4°C for 1–2 hours . Beads were washed with PBS and immunoprecipitated protein complexes were eluted by boiling beads in reducing sample buffer for 5 minutes . Cell extracts ( pre-IP ) were also collected and boiled in reducing sample buffer . For mass spectrometry analysis , protein complexes were resolved by SDS-polyacrylamide gel electrophoresis ( Invitrogen ) followed by staining with a silver stain kit ( Sigma-Aldrich ) . Protein bands specific to immunoprecipitated pUL21a complex were excised for identification by MS/MS mass spectrometry [47] . For immunoblotting , total cell or pre-IP extracts were lysed in sample buffer containing SDS and protease and phosphatase inhibitors . Proteins were resolved on a SDS polyacrylamide gel , transferred to a polyvinylidene difluoride ( PVDF ) membrane , hybridized with a primary antibody , reacted with the horseradish peroxidase-conjugated secondary antibody , and visualized using chemiluminescent substrate ( Thermo Scientific ) . Total RNA was extracted with TRIzol ( Invitrogen ) and treated with Turbo DNA-free reagent ( Ambion ) to remove genomic DNA contaminants . cDNA was reverse transcribed from total RNA with random hexamer primers using the High Capacity cDNA reverse transcription kit ( Applied Biosystems ) . cDNA was quantified using SYBR Advantage qPCR Premix ( Clontech ) and primers for the cellular genes geminin , APC4 , APC5 , and GAPDH ( glyceraldehyde-3-phosphate dehydrogenase ) as an internal control ( see below ) . cDNA from infected cells was used to generate a standard curve for each gene examined . The standard curve was then used to calculate the relative amount of specific RNA present in a sample . Primers used for RT-qPCR are as follows: geminin , forward 5′GCCTTCTGCATCTGGATCTCTT3′ and reverse 5′CGATGTTTCCTTTTGGACAAGC3′ [24]; APC4 , forward 5′ATTCTCGTCCTTGGAGGAAGCTCT3′ and reverse 5′TTCTGGCCATCCGAGTTACTTCAG3′ [24]; APC5 , forward 5′GTGCCATGTTCTTAGTGGCCAAGT3′ and reverse 5′GATGCGCTCTTTGCAGTCAACCTT-3′ [24]; GAPDH , forward 5′CTGTTGCTGTAGCCAAATTCGT3′ and reverse 5′ACCCACTCCTCCACCTTTGAC3′ [30] . To determine cellular DNA content , cells were trypsinized , collected by low-speed centrifugation , fixed , and permeabilized in ice-cold 70% ethanol overnight . Cells were stained with propidium iodide only , or double-stained with propidium iodide and anti-pUL44 antibody to identify HCMV-infected cells . Total or pUL44-positive cells were determined for their DNA content by cell-cycle analysis with flow-cytometry . Percentages of cells in each cell cycle compartment were calculated using CellQuest or FlowJo software .
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In this study , we report an intriguing mechanism used by human cytomegalovirus ( HCMV ) to regulate a cellular E3 ubiquitin ligase , the anaphase promoting complex ( APC ) . The ability to hijack the ubiquitin-proteasome system for regulating protein degradation and to manipulate the cell cycle for viral genome synthesis is critical in many viral infections . The APC is a master cell cycle modulator that targets a number of regulatory proteins for proteasomal degradation . It can prevent cells from entry into S-phase , thus creating a hindrance for viruses needing to coerce cells into a cellular environment favorable for viral DNA synthesis . We have identified an HCMV protein , pUL21a , which uses a seemingly counterintuitive mechanism to regulate the APC . It interacts with the APC to target the subunits of this ubiquitin ligase for proteasomal degradation . This causes disruption of the complex and reduces its activity . Furthermore , a virus lacking pUL21a and pUL97 , which is another HCMV-encoded APC regulator , was highly attenuated when compared to loss of UL97 alone , suggesting that HCMV uses two proteins to fully disarm the APC . This study identifies a herpesviral protein that uses a unique , proteasome-dependent mechanism to regulate the activity of this prominent cellular E3 ubiquitin ligase .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"infectious",
"diseases",
"virology",
"cytomegalovirus",
"infection",
"biology",
"microbiology",
"viral",
"diseases"
] |
2012
|
Proteasome-Dependent Disruption of the E3 Ubiquitin Ligase Anaphase-Promoting Complex by HCMV Protein pUL21a
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Hashimoto's thyroiditis ( HT ) is the most common of all thyroid diseases and is characterized by abundant lymphocyte infiltrate and thyroid impairment , caused by various cell- and antibody-mediated immune processes . Viral infections have been suggested as possible environmental triggers , but conclusive data are not available . We analyzed the presence and transcriptional state of human herpesvirus 6 ( HHV-6 ) in thyroid fine needle aspirates ( FNA ) and peripheral blood mononuclear cells ( PBMCs ) from 34 HT patients and 28 controls , showing that HHV-6 DNA prevalence ( 82% vs . 10% , p≤0 . 001 ) and viral load were significantly increased in FNA from HT patients , and thyrocytes from HT FNA displayed a 100-fold higher HHV-6 DNA load compared to infiltrating lymphocytes . In addition , while HHV-6 was strictly latent in positive samples from controls , a low grade acute infection was detected in HT samples . HHV-6 variant characterization was carried out in 10 HT FNA samples , determining that all specimens harbored HHV-6 Variant A . The tropism of HHV-6 for thyroid cells was verified by infection of Nthy-ori3-1 , a thyroid follicular epithelial cell line , showing that thyrocytes are permissive to HHV-6 replication , which induces de novo expression of HLA class II antigens . Furthermore , HHV-6-infected Nthy-ori3-1 cells become targets for NK-mediated killing , NK cells from HT patients show a significantly more efficient killing of HHV-6 infected thyroid cells than healthy controls , and HT patients have increased T-cell responses to HHV-6 U94 protein , associated to viral latency . These observations suggest a potential role for HHV-6 ( possibly variant A ) in the development or triggering of HT .
Hashimoto's thyroiditis ( HT ) , or chronic lymphocytic thyroiditis , is a common autoimmune disease with unknown etiology and its prevalence has been increasing over the past 50 years [1] , [2] , [3] . Together with genetic factors , environmental factors are thought to be important in triggering autoimmune thyroid diseases ( AITD ) , and viral infections have been suggested as possible environmental triggers [4] , yet no conclusive evidence is available . Also herpesviruses have been suggested as potential cofactors , and have occasionally been detected in AITD [5] , [6] . Thyroid cells infected with human cytomegalovirus were shown to act as antigen presenting cells and therefore might be involved in autoimmunity [7] , patients with Graves' disease display a higher frequency of EBV-infected B cells secreting antibody to TSH-R [8] , and AITD patients have elevated antibody titers against EBV antigens [9] . Human herpesvirus 6 ( HHV-6 ) DNA has been detected in HT tissue specimens , but not in tissues from Graves' disease or multi nodular goiter [6] . HHV-6 infection is common and has a worldwide distribution [10] . Viral strains cluster in two variants: HHV-6A , with still unknown disease association , and HHV-6B , the etiologic agent of roseola ( exanthem subitum ) , a childhood benign febrile disease . HHV-6 in vitro replicates most efficiently in primary T-cells and in selected T-cell lines . However , the in vivo tropism of HHV-6 is considerably broader , including macrophages , endothelial cells , salivary glands , and brain [6] , [11] , [12] . After primary infection , HHV-6 establishes a latent infection and resides mainly in peripheral blood mononuclear cells ( PBMCs ) and in macrophages [11] , [13] . During latency , HHV-6 expresses specific viral transcripts . In particular , expression of U94 , in the absence of other viral lytic transcripts , is considered a molecular marker of viral latency [14] , [15] . HHV-6 has been tentatively associated to several chronic autoimmune inflammatory processes [5] , including Sjogren syndrome [16] , [17] , multiple sclerosis [18] , [19] , [20] , rheumatoid arthritis and systemic lupus erythematosus [21] , [22] . In addition , recent case reports suggested that HHV-6 infection might be related to the onset of autoimmune disorders , including purpura fulminans , severe autoimmune acquired protein S deficiency [23] , autoimmune connective tissue diseases [24] , and severe autoimmune hepatitis [25] . However , HHV-6 involvement in autoimmune diseases is still not supported by convincing data . In this report , we show that HHV-6 establishes a productive in vivo infection of thyroid cells from HT patients , that infected thyrocytes become a target for innate NK killing , and that HT patients have increased CD4+ and CD8+ T-cell responses to HHV-6 U94 protein . These findings strongly argue for a pathogenic association between HHV-6 and autoimmune HT .
Thyroid fine needle aspirates ( FNAs ) derived from 34 HT patients and 28 patients with benign follicular epithelial lesions ( controls ) were analyzed for the presence of HHV-6 DNA by real time quantitative PCR ( qPCR ) specific for HHV-6 U94 and U42 genes . All clinical samples were analyzed in double blind tests . In addition , when there was sufficient amount all samples were reanalyzed in a randomized and blinded fashion at a distant time from the first analysis , yielding superimposable data . All samples contained amplification-grade DNA , as shown by qPCR of RNase P , a cell reference gene that was also used to normalize viral loads to number of cells . The results showed that HHV-6 was significantly more prevalent in HT FNAs ( 28/34 , 82% ) than in FNAs derived from controls ( 3/28 , 10% ) ( p≤0 . 001 ) ( Table 1 ) . Furthermore , viral load was higher in HT specimens ( mean 8 . 1×103 copies/µg of cellular DNA , range 7 . 9×102–4 . 2×104 copies/µg DNA ) than in the few controls which resulted positive for HHV-6 ( mean 7 . 1×102 copies/µg DNA , range 5 . 7×102–8 . 2×102 copies/µg DNA ) ( p≤0 . 05 ) . In 10 specimens the amount of DNA was sufficient to characterize the viral variant by restriction enzyme pattern analysis of U31 nested PCR amplification product [26] . The results showed the presence of variant A in all tested specimens ( data not shown ) . Due to the lymphotropic nature of HHV-6 and to the presence of significant lymphocyte infiltrates in thyroids of HT patients , we assessed whether HHV-6 was harbored in thyroid epithelial cells or rather in infiltrating lymphocytes . To address this question , FNA specimens from two additional HT patients were separated into epithelial and non-epithelial fractions by immunomagnetic selection with EpCAM ( Ber-EP4 ) antibodies , and then analyzed for HHV-6 presence . Effective separation of fractions was checked by RT-PCR amplification of specific leukocytes transcripts ( CD45 , CD3 ) , showing efficient depletion of lymphoid cells in the epithelial-enriched fraction ( Figure 1A ) . The analysis of viral presence showed that HHV-6 was mainly harbored in the enriched epithelial fraction ( i . e . thyroid cells ) , than in non-epithelial cells ( i . e . lymphocytes ) , as shown by the observation that virus load was 100-fold higher in epithelial ( mean value 4 . 1×104 copies/µg DNA ) than in non-epithelial ( mean value 5×102 copies/µg DNA ) fraction ( Figure 1B ) . Since HHV-6 DNA was detected with high frequency in HT thyroids , we determined whether the virus was sustaining active or latent infection . To this purpose , we analyzed viral transcription in positive FNAs , by specific qPCR after retrotranscription ( RT-qPCR ) for U42 ( indicative of productive replication ) and U94 ( expressed in both productive and latent phase ) . Therefore , the detection of U94 transcript in the absence of other viral mRNAs indicates latent infection . The analysis of the HHV-6 replicative status was performed in 21 out of the 28 HT patients that resulted positive for HHV-6 . The analysis could not be performed in 7 specimens , since there was not enough material left for RNA studies . The results showed that 71% ( 15/21 ) of HT FNAs harbored transcriptionally active virus , whereas HHV-6 was strictly latent in the few positive controls , as shown by the detection of only U94 mRNA ( Table 1 ) . In addition , in the two samples which were separated into epithelial and non-epithelial fractions , lytic HHV-6 U42 transcripts were detected only in the epithelial-enriched fraction , while the lymphocyte fraction showed the presence of only latent U94 transcripts ( Figure 1C ) , thus confirming that virus replication in thyrocytes and not in infiltrating lymphocytes . HHV-6 establishes latent infection in peripheral T lymphocytes from the general population , thus HHV-6 presence and transcriptional state were also analyzed in peripheral blood mononuclear cells ( PBMC ) derived from HT patient ( n = 20 ) and control ( n = 8 ) groups . Viral DNA was detected in 19/20 ( 95% ) HT patients and in 3/8 ( 37% ) controls ( Table 1 ) . However , although viral loads were higher in HT patients ( mean viral load of 1 . 2×104 copies/µg DNA , range 7 . 5×102–3 . 9×104 ) compared to controls ( mean viral load of 3 . 7×102 copies/µg DNA , range 2 . 8×102–4 . 9×102 ) ( p≤0 . 05 ) , HHV-6 was exclusively latent in all PBMC samples , as revealed by the presence of U94 mRNA in the absence of U42 transcripts . A similar situation was found in healthy blood donors , where 6/20 ( 30% ) collected PBMCs were found positive for HHV-6 presence ( mean viral load of 3 . 6×102 copies/µg DNA , range 1 . 2×102–5 . 1×102 ) , and none of them harbored active virus , as revealed by the detection of only U94 transcript . The tropism of HHV-6 for thyroid cells has not been described before . Therefore , to assess whether thyrocytes are permissive to HHV-6 , we performed in vitro infection of thyroid Nthy-ori3-1 with a HHV-6 cell-free inoculum , at a m . o . i . of 10 genome equivalents per cell . Viral replication was analyzed from 0 to 28 days post infection ( d . p . i . ) , evaluating virus presence , transcription and antigen expression in infected cells , respectively by PCR , qPCR , RT-PCR and IFA . As shown in Figure 2A , HHV-6 DNA was present at all times p . i . . Virus load decreased from about 5×104 copies/µg DNA at 1 d . p . i . ( corresponding to 1 viral genome in 3 cells ) to 5×102 copies/µg DNA ( 1 viral genome in 300 cells ) at 14 and 28 d . p . i . , when the experiment was discontinued , with no substantial difference between HHV-6 variants ( Figure 2B ) . Productive infection lasted for the first 7 d . p . i . , as shown by the presence of U42 ( immediate-early ) and U22 ( late ) lytic transcripts . At later time points , only a strictly latent HHV-6 infection was observed , as shown by disappearance of lytic transcripts and the persistence of only U94 mRNA ( Figure 2C ) . It is relevant to mention that U94 transcripts in lytically infected cells are expressed at lower levels relative to HHV-6 lytic genes , being approximately 100-fold less abundant than the transcripts of U42 gene , as previously reported by us [27] and in agreement with the original description by Rapp et al . [28] . Therefore the detection of only U94 mRNA , in the absence of lytic transcripts , is indicative of HHV-6 latency [14] . Likewise , the late viral antigen gp116 was detected for the first week p . i . , and disappeared concomitantly to the establishment of latency ( Figure 2D ) . HLA class II antigens are not expressed by normal thyrocytes , but their expression can be induced by different stimuli , including IFN-γ [15] and virus infection [7] . Surface expression of HLA-II on thyroid follicular cells might induce an APC behavior , promoting the autoimmune responses underlying HT development . Thus we analyzed whether HHV-6 lytic or latent infection might induce HLA expression in thyroid cells . Nthy-ori3-1 cells were infected with HHV-6 and analyzed by flow cytometry at 24 , 48 , and 72 hours p . i . . In parallel , cells were also infected with HHV-7 , as a control , or transfected with a U94-expressing plasmid , encoding the full length U94 gene , which is unique to HHV-6 and absent in the closely related HHV-7 . The results showed that HHV-6 infection induced cell-surface expression of DR antigenic determinants at a level comparable to that of IFN-γ treatment ( 17 . 62 and 15 . 54 MFI , respectively ) ( Figure 3A ) . By contrast , induction of HLA-II was not detected in HHV-7-infected cells ( 4 . 87 MFI ) , suggesting that the phenomenon is specific for HHV-6 . HLA-II induction was instead observed in U94-transfected thyroid cells ( 18 . 62 MFI ) , whereas it was not detected in control cells receiving the same plasmid with no insert , suggesting that it might be related to the expression of this specific HHV-6 U94 gene . By contrast , no change was observed in HLA class I expression , neither in HHV-6 or U94 treated , nor in HHV-7 infected cells ( 25 . 03 , 16 . 7 , 17 . 7 MFI respectively , compared to the control 23 . 08 MFI value ) ( Figure 3B ) . These findings are consistent with the possibility that HHV-6 infection may induce thyrocytes to present immunogenic epitopes to CD4+ T cells through HLA-class II up-regulation . Due to the high prevalence of HHV-6 active infection in HT patients , we searched for the presence of HHV-6-specific T cell responses in those subjects , using a dual color ELISPOT assay . Assays were performed on CD8+ and CD4+T cells isolated from PBMCs derived from 20 HT patients harboring active HHV-6 in their thyroid , and 20 healthy blood donors , used as controls . T-cell responses were enumerated and characterized for the type of cytokine produced , using either total virus lysate or purified recombinant HHV-6 U94 protein as antigens . The results showed that HT patients carried higher numbers of virus-specific circulating CD8+ and CD4+ T cells , particularly those recognizing the U94 protein ( Figure 4 ) . Characterization of cytokine-secreting cells in response to HHV-6 lysate and U94 protein disclosed relevant differences between HT patients and donors . HT patients showed that enhanced virus-specific CD8+ T cell responses were limited to IFN-γ-producing cells to U94 ( Figure 4 , Upper panel ) . With regard to CD4+ T cells , HT patients showed significantly higher responses of IL-2 and IFN-γ/IL-2 secreting effectors to HHV-6 lysate and U94 protein , and also to control tetanus toxoid , consistently with an underlying over-activation of this compartment ( Figure 4 , Lower panel ) . Notably , however , patients displayed a selective and significant increase in CD4+ T cells secreting IFN-γ only in response to U94 protein . ( Figure 4 , Lower panel ) . These findings indicate that HT patients have significantly higher numbers of circulating HHV-6-specific CD4+ and CD8+ T cells , mainly secreting only IFN-γ and directed to the U94 protein . To ascertain whether HHV-6 infection increases the susceptibility of thyroid cells to destruction by innate immune cells , a specific in vitro cytotoxicity assay was used . Innate responses against HHV-6-infected Nthy-ori3-1 thyrocytes were investigated in PBMCs from 3 HT patients , previously shown to harbor active HHV-6 in their thyroids , and 3 healthy controls . Thyroid cells were infected with HHV-6 and used as target cells for the killing assay at 24 , 48 and 72 h . p . i . , using an effector∶target ( E∶T ) ratio of 2∶1 . Co-cultures were incubated for 4 hours , a time chosen to detect mainly NK activation . Figure 5 illustrates the results obtained in one HT patient and one control , which are representative of all the subjects analyzed . Cell mortality associated with virus infection per se was very low ( necrosis<2% ) ( Figure 5A , C ) , but significant apoptosis occurred in infected thyrocytes exposed to PBMCs from healthy donors ( 42±6% , average of three controls ± SD ) ( Figure 5A ) . Cytotoxic activity was associated with NK cell activation , as demonstrated by CD107a exposure on CD3−/CD56+ cells ( Figure 5B ) . Notably , PBMCs from HT patients showed enhanced cytotoxic activity , both when cultured with uninfected ( 40±5% vs 0 . 1±0 . 08% in controls ) and with HHV-6 infected thyrocytes ( 80±7% vs 40±3% in controls ) ( Figure 5C ) . Moreover , NK cells of HT patients displayed high levels of basal degranulation , even when cultured with uninfected thyrocytes ( 67±6% vs 0% ) ( Figure 5D ) . In parallel , CD107a expression by NK cells of HT patients increased substantially in the presence of HHV-6 infected cells , with more than 90% ( 91±4% ) of CD3−/CD56+ NK cells activated . By contrast , degranulation of NK cells in a standard cytotoxic assay vs K562 showed similar levels in HT patients and controls , suggesting that the anti-HHV-6 NK activation observed in HT patients was not due to a general increase of NK cells reactivity ( Figure 5B , D ) . However , due to the high basal level of apoptosis induced by HT NK cells on uninfected thyrocytes , an alternative explanation might be that HHV-6 infection sensitizes these cells to apoptosis-inducing effects .
Viral infections have been frequently cited as important environmental factors implicated in AITD [29] , [30] , but no specific virus has yet been conclusively associated to the disease . In particular , herpesviruses have been implicated , with conflicting evidence . Case reports suggested a potential association between herpesvirus infection and AITD [6] , [9] , [31] , [32] but when thyroid FNAB specimens were analyzed no EBV , CMV or HSV-1 DNA was detected [33] . A recent study analyzed the presence of herpesvirus DNA in post-operative thyroid specimens from tissue blocks [6] , and HHV-6 was detected by single round PCR in 2 out of 15 ( 13 . 3% ) HT tissue specimens , whereas no HHV-6 DNA was isolated in Grave's Disease or Multi Nodular Goiter tissues . In our report , employing a sensitive real time qPCR , 82% of FNAs from HT patients were found positive for HHV-6 DNA , whereas only 10% of control FNAs ( derived from patients with benign thyroid lesions ) harbored the virus . Furthermore , the majority of HT specimens harbored viral loads almost 2 logs higher compared to those of the few control thyroid tissues which resulted positive ( p≤0 . 001 ) . Interestingly , variant analysis performed in 10 HT samples showed that all samples harbored HHV-6A , suggesting that HT might be specifically associated to this variant . Also the pattern of infection established by HHV-6 in HT patients is substantially different than that observed in healthy individuals , being characterized by: i ) higher viral frequency and load in HT biopsies compared to controls; ii ) active HHV-6 transcription in HT thyrocytes , consistent with virus replication , compared to latent infection in the few HHV-6-infected control thyroids; iii ) presence of HHV-6 infection mainly in thyrocytes , rather than in lymphocytes infiltrating the lesion; iv ) increased prevalence of latent HHV-6 infection in PBMCs . These findings are consistent with the possibility that the thyroid of HT patients may constitute a site of active HHV-6 infection/replication . Considering that HT patients have increased HHV-6 prevalence in their PBMCs , where the virus is strictly latent , still unidentified microenvironmental factors are probably required to allow HT thyrocytes to be infected by HHV-6 . Permissiveness to HHV-6 infection of thyrocytes was confirmed by our in vitro experiments showing that Nthy-ori3-1 cells support HHV-6 replication and antigen expression for approximately one week . Subsequently , viral latency is established , with a pattern similar to that recently described in endothelial cells [27] . Therefore , HHV-6 is capable of long-term persistence in thyrocytes , a relevant pre-requisite to exert possible pathogenic effects locally . Here we provide evidence indicating that HHV-6 may induce a de novo expression of HLA class II molecules in thyrocytes , which may thus behave as functional antigen presenting cells for CD4+ T lymphocytes . A similar observation has already been reported for human cytomegalovirus infection of thyroid cells [7] , [34] . Intriguingly , enhanced HHV-6-specific T cell responses were observed in all HT patients , with a marked increase in the number of CD4+ T lymphocytes recognizing HHV-6 antigens , particularly the subset of polyfunctional CD4+ T cells secreting both IFN-γ and IL-2 . These findings are consistent with an abnormal , probably persistent , immune response to HHV-6 antigens in HT patients , possibly favored by the local up-regulation of HLA class II molecules on thyrocytes induced by HHV-6 infection . These HHV-6-specific responses are likely embedded in a context of global over-activation of the CD4+ T cell compartment , as suggested by the increased responses of CD4+ T cells producing IL-2 or IFN-γ/IL-2 to the TT antigen . Nevertheless , HT patients showed significantly higher numbers of CD4+ and CD8+ T cells secreting IFN-γ only in response to the U94 antigen , suggesting a possible role of these effectors in mediating the killing of U94-expressing thyrocytes . The finding that the HHV6 U94 antigen elicited higher responses than the whole HHV6 lysate could be explained by the fact that HHV-6 infection is ubiquitous and highly prevalent , with almost 100% individuals having memory T cells recognizing lytic viral antigens [35] , [36] , [37] . Instead , high expression of the regulatory , non-constitutive U94 protein of the virus and the consequent development of a specific immune response against U94 , seems to be limited to specific pathologic conditions , as previously described also in multiple sclerosis patients , likely related to multiple virus reactivations [38] . Interestingly , autoimmune thyroid disease is significantly frequent in multiple sclerosis patients [39] , [40] , and it could be hypothesized that HHV-6 replication in HT patients might potentially be induced by autoimmune inflammation , as has been suggested for multiple sclerosis [41] , [42] . Therefore , the occurrence of selective U94-specific CD4+ and CD8+ T cell responses in HT patients , suggests a specific role of this viral product as a potential trigger of autoimmunity . Alternatively , HT patients might experience variations in U94 production , or frequent switches between latency and active replication , leading to an increased sensitization to this viral antigen . In addition to abnormal HHV-6-specific T cell responses , innate immunity triggered by HHV-6 may also contribute to HT development . In fact , PBMCs from HT patients showed a markedly enhanced cytotoxic activity to HHV-6-infected thyrocytes compared to control PBMCs derived from healthy donors . These findings , together with the observation that NK cells of HT patients show high levels of basal degranulation even when cultured with uninfected thyrocytes , suggest that these patients might suffer from an inherent NK cell alteration . However , further studies are required to fully elucidate this association and the mechanisms underlying the possible role of HHV-6 as a trigger of HT . Indeed , there are several potential mechanisms by which HHV-6 might induce autoimmune responses . Viral infections might trigger autoimmunity by exposing high amounts of normally sequestered cell antigens , through lysis of infected cells . Another potential trigger is represented by molecular mimicry , with the synthesis of viral proteins that resemble cellular molecules , as a mechanism of immune escape . The virus could also induce aberrant expression of histocompatibility molecules thereby promoting the presentation of auto-antigens . Notably , HHV-6 has the ability to trigger all the above mentioned mechanisms and in the recent years , several reports have suggested a potential role of HHV-6 in autoimmunity [21] , [24] , [43] , [44] , [45] , [46] , [47] . Overall , our study indicates that HHV-6 infection might be an important factor in HT development .
The samples were obtained as part of routine clinical work from patients undergoing fine needle aspiration biopsy ( FNAB ) for diagnostic purposes , and were used after receiving approval from the Local Ethical Committee of the University of Ferrara and S . Anna Hospital of Ferrara . The patients provided written informed consent for both FNAB procedure ( which is part of the clinical practice ) and for biomolecular analyses , to which purpose the samples were anonymized . A total of 62 subjects participated in the study . None of them had other concomitant diseases or was taking drugs that could possibly affect thyroid function . All patients and controls were euthyroid at the moment of biopsy . Serum TSH , free T4 , thyroperoxidase antibodies ( TPO Ab; normal value<35 IU/ml ) and thyroglobulin antibodies ( Tg Ab; normal value<115 IU/ml ) were measured in all patients by using an immuno-electrochemiluminescence technique ( Modular E 170 , Roche Diagnostics GmbH ) . All patients underwent ultrasound guided thyroid fine needle aspiration , as previously described [48] . Fine needle thyroid aspirates ( FNAs ) were used for both cytology and molecular analysis . The diagnosis of HT was based on the criteria described by Kini [49] . On the basis of these characteristics , 34 FNAs were considered as consistent with HT . The other 28 samples were FNAs derived from the normal tissue surrounding thyroid nodules in patients with hyperplastic follicular lesions ( eg . Multi Nodular Goiter , MNG ) , and were considered as controls . The 34 HT patients included 7 males and 27 females , with a mean age of 49 . 8±2 . 5 years ( range 32–75 years ) , with TPO Ab>35 IU/ml ( mean value = 1312 IU/ml , range 146–8229 IU/ml ) , and Tg Ab>115 IU/ml ( mean value = 751 IU/ml , range 280–3500 IU/ml ) . The 28 FNA control patients included 10 males and 18 females with a mean age of 53 . 3±5 . 6 years ( range 33–88 years ) ( there was no statistically significant difference between the two groups ) , and showed TPO Ab<35 IU/ml ( mean value = 9 IU/ml , range 7–11 IU/ml ) , and Tg Ab<115 IU/ml ( mean value = 12 IU/ml , range 10–16 IU/ml ) . In two HT cases , FNA amount allowed isolation of epithelial and lymphoid cells by immunomagnetic beads coated with an anti-EpCAM ( Ber-EP4 ) antibody ( CELLection Epithelial Enrich ( Dynal AS , Oslo , Norway ) , following manufacturer instructions . Effective separation of fractions was checked by semiquantitative PCR amplification of specific leukocytes ( CD45 , CD3 ) transcripts . Peripheral blood mononuclear cells ( PBMCs ) were isolated by Ficoll-Hypaque gradients . Aliquots of 106 PBMCs were stored −80°C for DNA and RNA analyses , whereas aliquots of 107 PBMCs were stored viably for ELISPOT analyses . Nthy-ori3-1 cells , a thyroid follicular epithelial cell line [50] , were maintained in RPMI medium with 10% FBS and infected with 10 genome equivalents per 1 cell . JJhan and SupT1 T cells were grown as already described [15] . Transfection of U94 expression plasmid was performed by nucleofection ( Amaxa , Lonza ) , upon standard conditions , as described [27] . Control cells received the same amount of empty vector alone . Efficiency of transfection , determined in parallel samples by transfection with pmax-GFP plasmid was approximately 70% in all experiments . Cell free virus inocula and UV-inactivated viral preparations were obtained as described [15]: HHV-6 variant A ( strain U1102 ) was grown and analyzed in the JJhan cell line [15]; HHV-6 variant B ( strain Z29 ) and HHV-7 ( strain CZ ) [51] were grown in the Sup-T1 cell line [52] . In vitro virus infection was performed in Nthy-ori3-1 cells seeded at optimal density as previously described [15] , [27] . DNA and RNA were isolated from clinical samples and Nthy-ori3-1 cells as described [27] . All RNA preparations were devoid of DNA , as assured by multiple DNase digestions and lack of amplification in PCR reactions where retrotranscription ( RT ) had been omitted [14] . HHV-6 DNA presence and load were analyzed by PCR and real time quantitative ( qPCR ) specific for the U94 and U42 genes [27] , and samples were considered positive when 1 µg of cell DNA harbored more than 100 copies of viral DNA [27] . Amplification of the house-keeping human RNase P gene was used as a control . All clinical samples were analyzed in a randomized and blinded fashion . In addition , 15/28 control and 21/34 HT FNAs , when there was enough material to repeat the analysis , were tested again in a randomized and blinded fashion at a distant time from the first analyses . HHV-6 variant A or B identification was obtained by restriction enzyme digestion with HindIII enzyme of the U31 nested PCR amplification product , as reported previously [26] . Digestion products were then visualized on ethidium bromide stained agarose gel after electrophoresis migration . Virus transcription was assessed by PCR or qPCR after retrotranscription ( RT-PCR , RT-qPCR ) , determining the presence of lytic ( U42 , U22 ) or latent ( U94 in the absence of U42 ) mRNAs , as previously reported [27] . The sensitivity of the used PCRs was similar for all genes , detecting as few as 100 copies of target sequence . Cell fractions derived by immunomagnetic separation of FNAs were characterized by RT-PCR specific for leukocytes transcripts ( respectively CD45 , CD3 ) , using serial dilutions of cDNA template , corresponding to amounts of total extracted RNA ranging from 100 ng to 1 pg . Primers and PCR conditions for CD3 and CD45 were previously reported [53] , [54] , and amplification reactions were carried out for 30 cycles . In each assay the cDNAs obtained from JJhan T cells or Nthy-ori3-1 thyroid cells were also included as positive and negative controls respectively . Amplification of the house-keeping β-actin gene was used as a control . Immunofluorescence for HHV-6 antigen expression was performed with a mouse monoclonal antibodies ( mAb ) directed against glycoprotein gp116 ( late antigen ) of HHV-6 A and B ( ABI , Columbia , MD , USA ) , as previously described [15] . Expression of HLA class I and II ( DR ) antigens was investigated in Nthy-ori3-1 cells infected with HHV-6 or HHV-7 , transfected with U94 expression plasmid or treated with IFN-γ ( 10 U/ml ) [15] . Staining was performed with anti-HLA-I ( W6/32 , IgG2a , FITC ) , anti-HLA-DR mAb ( L243 , IgG2a , PECy5 . 5 ) ( Caltag Laboratories , CA , USA ) , and isotypic controls ( Exbio , Praha , Czech Republic ) . The analysis was carried out with a FACSCount cytometer and the CellQuest software ( Becton Dickinson , San Jose , CA , USA ) . Results were expressed as MFI ( mean fluorescence intensity ) . The CD107a mobilization assay was performed using infected or non-infected Nthy-ori3-1 cells as target cells and PBMC from controls or HT patients as effector cells , with an effector∶target ratio of 2∶1 . K562 cells were used as positive control of NK activation . Degranulation was assessed in triplicate after 4 hours of co-culture by staining with PE-Cy5-conjugated anti-CD107a mAb ( e-Bioscience , Frankfurt , DE ) and gating on CD3−/CD56+ NK cells [55] , [56] . Induction of apoptosis was evaluated by flow cytometry after staining with FITC-labeled Annexin-V ( Bender MedSystem , Vienna , AU ) . Cell viability was assessed by Propidium Iodide staining . Results were expressed as percentage of gated cells . IFN-γ and IL-2 secretion by HHV6-specific CD4+ and CD8+ T cells was quantified using a dual color ELISPOT assay in 20 HT patients and 20 controls . Assays were performed using either total virus lysate or purified recombinant HHV-6 U94 protein as antigens . Briefly , an aliquot of purified HHV-6A containing 1010 virus genomes/ml was lysed with 0 . 25% Triton X-100 followed by sonication . Stock solutions were used at a 1∶1 , 000 dilution in the final assay . HHV-6 recombinant U94 protein , obtained as described [15] , was used at 2 µg/ml . CD4+ and CD8+T cells were isolated from PBMCs by immunomagnetic separation ( Miltenyi Biotec , Calderara di Reno , Italy ) . Aliquots of 5×104 CD8+ T cells , seeded in anti-IFNγ and -IL-2 antibody coated wells , were stimulated with the antigens . Mock lysates , obtained from uninfected JJhan cells , or Tetanus toxoid ( TT , 5 µg/ml , Calbiochem , San Diego , CA ) were also used , respectively as negative and positive controls . Results are expressed as spot forming cells ( SFC ) per 5×104 CD4+ or CD8+ T lymphocytes . Statistical significance of results was analyzed by independent Student t-test .
|
Hashimoto's thyroiditis ( HT ) is a very common autoimmune disease of the thyroid . In addition to genetic background , several viruses , including herpesviruses , have been suggested to play a role as possible environmental triggers of disease , but conclusive data are still lacking . The anecdotal presence of human herpesvirus 6 ( HHV-6 ) in HT specimens prompted us to study a possible association between HHV-6 and HT . Our analysis of fine needle thyroid aspirates and blood from HT patients and controls shows that HHV-6 prevalence and load are highly increased in HT patients . Furthermore , HT-derived thyrocytes harbor active virus , whereas HHV-6 is strictly latent in the few virus-positive controls . We also report that HHV-6 infects thyroid cells , inducing de novo expression of HLA-II surface antigens . Consequently , thyrocytes might behave as antigen presenting cells . Interestingly , immune cells from HT patients kill HHV-6-infected thyrocytes more efficiently than controls . Also , HT patients , but not controls , have specific T-cell responses to HHV-6 U94 protein . It is difficult to prove etiologic links between viral infections and diseases , especially in the case of a ubiquitous agent such as HHV -6 . Nevertheless , our findings indicate that HHV-6 might contribute to HT development , and argue for a pathogenic association between HHV-6 and HT .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"infectious",
"diseases",
"clinical",
"immunology",
"virology",
"autoimmune",
"diseases",
"immunology",
"biology",
"microbiology",
"viral",
"diseases"
] |
2012
|
Virologic and Immunologic Evidence Supporting an Association between HHV-6 and Hashimoto's Thyroiditis
|
Experiencing certain events triggers the acquisition of new memories . Although necessary , however , actual experience is not sufficient for memory formation . One-trial learning is also gated by knowledge of appropriate background information to make sense of the experienced occurrence . Strong neurobiological evidence suggests that long-term memory storage involves formation of new synapses . On the short time scale , this form of structural plasticity requires that the axon of the pre-synaptic neuron be physically proximal to the dendrite of the post-synaptic neuron . We surmise that such “axonal-dendritic overlap” ( ADO ) constitutes the neural correlate of background information-gated ( BIG ) learning . The hypothesis is based on a fundamental neuroanatomical constraint: an axon must pass close to the dendrites that are near other neurons it contacts . The topographic organization of the mammalian cortex ensures that nearby neurons encode related information . Using neural network simulations , we demonstrate that ADO is a suitable mechanism for BIG learning . We model knowledge as associations between terms , concepts or indivisible units of thought via directed graphs . The simplest instantiation encodes each concept by single neurons . Results are then generalized to cell assemblies . The proposed mechanism results in learning real associations better than spurious co-occurrences , providing definitive cognitive advantages .
Reading about a newly discovered insect species , an entomologist can rapidly learn various details of their development , communication , and mating . Studying the same material , it is much harder for someone with different expertise to learn the same facts . While it is commonsense that new information is easier to memorize if it relates to prior knowledge , the cognitive and neural mechanisms underlying this familiar phenomenon are not established . More specifically , one-trial learning of “neutral” events , as opposed to emotionally charged or surprising experiences [1] , is gated by knowledge of appropriate background information to make sense of the experienced occurrence [2 , 3] . Consider experiencing for the first time the co-occurrence of a buzzing sound with the sight of a beetle ( Fig . 1A ) . Learning that “beetles can buzz” may depend on background information that renders the “buzzing beetle” association sensible . Prior knowledge might include that wasps , flies , and bees also buzz . Such facts are relevant because they involve related concepts: these insects share several common associations with beetles ( e . g . small size , crawling , flying , erratic trajectories ) . The remainder of this paper refers to this cognitive phenomenon as “background information gating” or BIG learning . Mounting neurobiological evidence implicates formation of new synapses in long-term memory storage [4 , 5 , 6] . Building on those ideas , we propose a possible neuroanatomical correlate of BIG learning . The hypothesized mechanism is initially best illustrated under the over-simplifying assumption that associations are stored by connecting “grandmother” neurons , each corresponding to individual concepts ( Fig . 1B ) . The computational simulations presented in this work , however , demonstrate that this same concept also seamlessly works with distributed neuronal representations . In order to establish a synapse , according to Hebbian theory , the axon and dendrites of the two co-activated neurons must be juxtaposed [7] . We henceforth refer to this “potential synapse” configuration [8] as axonal-dendritic overlap or ADO . Intuitively , the reason the axon passes near the dendrite is because it is connected to other dendrites in that vicinity . Why then is the potential post-synaptic dendrite close to other dendrites contacted by the potential pre-synaptic axon ? Wiring cost considerations suggest that neurons should be placed nearby if they receive synapses from the same axons [9] . If knowledge representation is stored in pairwise neural connections [10] , this particular topology should correspond to relevant background information . Here we formulate this notion quantitatively with a new neural network learning rule , demonstrating by construction that ADO is a suitable mechanism for BIG learning . In our model , neural activation reflects associations sampled from various graphs taken as a simplified representation of everyday experience . Specifically , every instant of experience is represented as a subset of co-occurring elementary observables , each corresponding to a node of a “reality graph , ” in which edges denote probability of co-occurrence ( see S1 Text 1 . 1 for a more extended description ) . We study networks pre-trained with an initial connectivity by comparing their ability to learn new information that is related or unrelated to prior knowledge . Such pre-existing background information may derive from repetition learning [11] or from experience earlier in life: if the BIG ADO were enforced from the start in a fully disconnected network , no new synapses could ever form . The simplest instantiation encodes each concept by single neurons; results are then shown to generalize robustly to realistic cell assemblies . Noticeably , the proposed mechanism results in learning real associations better than spurious co-occurrences , providing definitive cognitive advantages .
This work assumes the classic model of neural networks as directed graphs in which nodes represent neurons and each directional edge represents a connection between the axon of the pre-synaptic neuron and the dendrite of the post-synaptic neuron . The network only contains excitatory neurons . In this model , formation of new binary connections ( a form of structural plasticity ) underlies associative learning , and knowledge is encoded by the connectivity of the network [10] . Activity-dependent plasticity is traditionally framed in terms of the Hebbian rule: “When an axon of cell a is near enough to excite cell b and repeatedly or persistently takes part in firing it , some growth process or metabolic change takes place in one or both cells such that a’s efficiency , as one of the cells firing b , is increased” [7] . Many variants of Hebbian synaptic modification exist [12] , often summarized as ‘neurons that fire together wire together’ . This popular quip , however , misses the essential requirement , clearly stressed in Hebb’s original formulation , that the axon of the pre-synaptic neuron must be sufficiently close to its post-synaptic target for plasticity to take place . The learning rule introduced in this work implements a form of structural plasticity in neural networks that incorporates the constraint of proximity between pre- and post-synaptic partners or axonal-dendritic overlap ( ADO ) : if two neurons a and b fire together , a connection from a to b is only formed if the axon of a comes within a threshold distance from a dendrite of b . In mathematical terms , this condition can be defined as a non-symmetric real-valued function between neurons corresponding to the distance from the axon of the candidate pre-synaptic neuron to the dendrite of the post-synaptic neuron . Now we introduce an approximation to express the axonal-dendritic overlap between neurons in terms of the connectivity of the rest of the network on the basis of two assumptions . The first assumption is that the axon of a passes near the dendrite of neuron b because it connects to another neuron c that is near neuron b . This assumption corresponds to a principle of parsimony in the use of axonal wiring: since the goal of axons is to carry signals to other neurons , the locations of axonal branches are part of trajectories towards synaptic contacts . The second assumption is that if neurons b and c are near each other , it is because they are both contacted by the same set of axons , which we generically call d ( Fig . 1 ) . This assumption presumes optimal neuronal placement once again to minimize axonal wiring , consistent with the existence of topographic maps e . g . in the mammalian cortex [13] , but also in invertebrate nervous systems [14] . These two assumptions can be combined into the assertion that the tendency of the axon of neuron a to overlap with a dendrite of neuron b increases with the number of neurons c and d such that a is connected to c and d is connected to both b and c . This idea is quantified by the following proximity ( π ) function: π ( a , b ) =Σc , d ( ωa , c×ωd , c×ωd , b ) , where ωa , c equals 1 if and only if the axon of a connects to the dendrite of c , and 0 otherwise ( likewise for ωd , c and ωd , b ) , and the indices c and d run over all neurons in the network ( see also Fig . 1A ) . The above formula can be elegantly expressed as the product of three matrices: ∏=ΩΩ×ΩΩt×ΩΩ , where Ω = {ωm , n} is the ( binary ) network connectivity ( also called adjacency matrix ) , with the number of rows and columns equal to the number of neurons in the network , and each row and column representing a neuron’s pre- and post-synaptic contacts , respectively , with all other neurons; Ωt is the transpose matrix in which every row is substituted with the corresponding column and vice versa ( this operation is equivalent to switching axons and dendrites for each neuron ) ; and Π = {π ( m , n ) } is the proximity matrix , which ( like Ω ) is square and non-symmetric . The results presented in the main text are obtained by choosing a value for the proximity threshold θ in order to discriminate between proximal and distant pairs of neurons: a is deemed proximal to b , that is there is a potential synapse between a and b , whenever π ( a , b ) > θ . The proximity threshold is one of several parameters that have to be fixed when running simulations of an actual system; robustness of the mechanism is discussed in S1 Text 3 . 2 . As an alternative to such a discontinuous threshold , we also implemented a probabilistic criterion for relating potential connectivity to proximity . In this case , the probability of a and b being proximal was not a binary function of proximity but it instead followed a sigmoid curve . This probabilistic variant , while introducing an additional source of noise in the simulations , yielded results ( also described in S1 Text 3 . 2 ) that confirmed the main results of this work . However , this more general approach also increases the complexity of the model , by requiring the specification of an additional parameter to define the slope of the sigmoid . Note , in a similar vein , that the above proximity formula seamlessly extends to non-binary connectivity matrices . For instance , network connectivity could be expressed as a matrix Ω recording not just the existence of a connection between two neurons , but the number of their physical contacts or other relevant measures , such as the stability of the synapses [15] . In the simple formulation used in this work , which presumes optimal neuronal placement to minimize axonal wiring , high proximity values make axonal-dendritic overlap likely , but not absolutely warranted . The learning rule described above relates closely to earlier works proposing similar learning mechanisms to explain generalization and grammatical rule extraction . Most strikingly , a learning procedure with a very similar structure was described [16] to explain a generalization of a novel sequence ( b-d ) based on experienced sequences ( a-c ) , ( a-d ) , and ( b-c ) . Despite this similarity ( which we discovered during peer-review ) , the formulation introduced in the current work was derived independently , starting from the interpretation in terms of axonal-dendritic overlaps and structural plasticity . More generally , circuit connectivity , synaptic plasticity , and neuronal placement are interrelated in a broad class of other common neural network approaches , including Kohonen-type self-organizing maps [17] . In our model , the ADO constraint on structural plasticity is reduced to simple topological proximity rather than physical distance between neurons . Moreover , the application to background information-gated learning , the neural network implementation , and the analyses presented here are all novel . To explain why axonal-dendritic overlap ( and the approximation captured by the above proximity formula ) constitutes the neural correlate of background information gating ( BIG ) , we revert to the ( admittedly simplistic ) “grandmother cell” interpretation in which each individual neuron represents a corresponding observable ( Fig . 1B ) . With such a one-to-one mapping in place , existing synapses reflect learned associations between previously co-occurred observables ( solid arrows in Fig . 1A ) , altogether constituting already acquired knowledge . When witnessing a new co-occurrence between the two observables a and b , the association of their internal representations will only be allowed if consistent with prior relevant knowledge , ultimately corresponding to background information . This work investigates the computational characteristics of the BIG ADO learning rule starting from well-defined reality-generating graphs ( described in the next sub-section of these Materials and Methods ) . In the general simulation design , the network of the agent’s internal representation is created by copying the set of nodes from the reality-generating graph , but connecting them by sampling only a subset of edges . This process produces a network effectively encoding a certain amount of knowledge of reality consistent with prior experience . The same result would be obtained by “pre-training” a ( n initially ) fully disconnected network with the common “firing together , wiring together” rule ( without BIG ADO filter ) and sequentially activating pairs of neurons corresponding to the sampled subset of the reality-generating graph . This design models the agent’s representation of background information related to previously experienced aspects of reality . Such a set-up allows investigation of the effect of the BIG ADO filter on subsequent learning . In the testing phase , further experience is sampled from not-yet learned edges of the reality-generating graph . These can be chosen so as to represent co-occurrences of observables more or less closely related to the pre-trained knowledge ( mimicking expert or novice agents , respectively ) . Specifically , when initially connecting the neural network , we select the pre-training subset of edges non-uniformly from the reality-generating graph , such that distinct groups of nodes are differentially represented . For example , if the neural network is pre-trained with 50% of the edges from the reality-generating graph , three quarters of these edges can be sampled from half of the nodes , and one quarter of the edges from the other half . The resulting neural network is an “expert” on half of the reality-generating graph ( because it knows a majority of the corresponding structure ) , and a “novice” on the other half ( where it only knows a minority of the structure ) . In the “learning test” phase , the network is presented with new edges selected either from within the domain of expertise ( that is , from the one quarter of edges not used in pre-training ) or from the outside ( from the three quarters of unused edges in the other half of nodes ) . The network learns new edges only if the proximity of the corresponding nodes is above threshold . Moreover , two ( or more ) edges of the reality-generating graph can be presented at once ( e . g . x-y and w-z ) to allow measurement of differential learning between the “real” and “spurious” associations . The former types reflect actual edges in the reality-generating graph ( i . e . x-y and w-z ) , while the latter correspond to “random” co-occurrences ( x-w , x-z , y-w , and y-z ) . The requirement of axonal-dendritic overlap for the formation of new connections is implemented by ways of the proximity function , which itself depends on pre-acquired connectivity . Thus , if the BIG ADO filter were in place from the beginning , no synapses would ever form in the network . The above pre-training design , which circumvents this impasse , can be justified by a two-stage developmental model [18] . Early in development , neurons are still optimizing their placements , and axonal branches undergo frequent rearrangements; in the subsequent mature stage , experience-dependent synapse formation and pruning are still common , but neuronal wiring is much more stable . Nevertheless , the “pre-training” model adopted here is also consistent with non-developmental scenarios . Even in adulthood , growth processes can be triggered by continuous repetition or by neuromodulation reflecting emotionally salience ( e . g . shock , pleasure , etc . ) . These conditions can explain the acquisition of prior knowledge ( background information ) . The BIG ADO filter , in contrast , constitutes a neuroanatomically-inspired model of one-trial , emotionally neutral learning . The dataset of word associations used in the first test of the BIG ADO learning rule ( Fig . 2A-B ) was derived from a compilation of noun/adjective pairings in Wikipedia . In its original form , it consisted of 32 million adjective-modified nouns ( http://wiki . ims . uni-stuttgart . de/extern/WordGraph ) . After identifying nouns corresponding to animals and household objects , we skimmed infrequent adjectives and removed ambiguous terms ( see S1 Text 2 . 1 for exact protocol ) . The resulting bipartite graph consisted of 50 animal nouns , 50 household object nouns , 285 adjectives and 2 , 682 edges ( 1 , 324 for animals and 1 , 358 for objects ) . Next , two networks were pre-trained by connecting half of the noun-adjective pairs from the graph . One of the networks associated more edges pertaining to animal nodes ( becoming an animal expert and object novice ) , while the other associated more edges pertaining to object nodes ( object expert , animal novice ) . Moreover , the amount of specialization was also varied to mimic different levels of specialization . This was achieved by varying the ratio between animals and objects learned in pre-training . Learning was then tested on the other half of the noun-adjective pairs using the BIG ADO rule with a proximity threshold ( θ in equation 1 ) of 6 . In the random equivalent graphs , edges between 100 “noun” nodes and 285 “adjective” nodes were generated stochastically by preserving both the overall noun and adjective degree distributions of the word graph . In this “control” condition , networks were pre-trained with expertise on one arbitrary subset of nodes . The “intrinsic background information” of a noun class can be quantified from the bipartite graph with the Proximity function and Pearson’s product-moment correlation coefficients ( S1 Text 3 . 1 ) . Specifically , consider the proximities of a noun with the set of all adjectives: the correlation of these values can be then computed between any two nouns . The intrinsic background information of a noun class will be reflected by a statistically larger mean correlation coefficient over all pairs of nouns within that class than over all pairs of nouns from two different classes . The mean correlation was significantly greater for animal-animal than the animal-object pairs ( 0 . 69 vs . 0 . 47 , p<10-4 ) , while there was no statistical difference ( p>0 . 1 ) between the mean correlations of the object-object ( 0 . 48 ) and object-animal ( 0 . 46 ) pairs ( see S1 Text 3 . 1 for details ) . To test the BIG ADO learning rule in more broadly applicable cases than noun-adjective associations , we generated small-world graphs adapting the algorithm of Watts and Strogatz [19] . Specifically , unless otherwise noted , Watts-Strogatz graphs were initially produced with degree 20 and 10% rewiring probability . Next , a random direction was selected for 90% of the edges , while the remaining 10% was made bidirectional . A random 20% of the nodes , along with all their incoming edges , were then labeled as belonging to the agent’s area of expertise . In the pre-training phase , networks were wired with a random set of edges of the graph , with the constraint that half of them must belong to the area of expertise , unless otherwise specified . The resulting connectivity consisted of a sub-graph of the initial graph , whose nodes in the area of expertise had higher average degree than those outside the agent’s expertise . In the “grandmother cell” implementation ( Fig . 3 ) , the BIG ADO threshold was set at 1 . When the size of the graph ( N ) was varied to assess the robustness of the BIG ADO findings with respect to the parameter space , the degree ( d ) and the number of associations ( edges ) used to pre-train the network ( T ) also varied as d = N/50 and T = N×d/4 , in order to keep the fraction of associations learned during pre-training constant . Neural network simulations with realistic cell assemblies ( Fig . 4 ) implemented the Zip Net model [20] , a computational enhancement of classic Associative Nets [21] that ensures optimal Bayesian learning [22] . Briefly , learning the association between two concepts A and B represented respectively by neurons a1 , a2 , … , as and b1 , b2 , … , bs , entails strengthening ( or forming ) synapses between co-active neurons and weakening or eliminating those between active and inactive neurons . Specifically , in the “incidence” matrix M with rows and columns respectively representing pre- and post-synaptic neurons , the entries in columns bj’s of all ai’s rows are increased while the remaining entries are decreasing by an appropriate amount to keep the total synaptic input constant ( S1 Text 2 . 3 ) . In the pre-training phase , the connectivity matrix is generated from the incidence matrix simply by keeping a fixed number of synapses per neuron ( those with highest weight ) , and setting the rest to zero . During BIG ADO testing , two neurons a and b can only form a new synapse upon co-activation if they have an axonal-dendritic overlap , which is expressed as the triple matrix product ΩΩtΩ computed from the positive values of the incidence matrix ( S1 Text 2 . 4 ) . Lastly , retrieval works as a classic dendritic sum: given a stimulus A’ represented by neurons a’1 , a’2 , … , a’s , all the entries in the rows corresponding to the ai’s are added up for each column , and those sums exceeding a given firing threshold correspond to activated ( post-synaptic ) neurons . If enough neurons belonging to the same cell assembly B’ fire , concept B’ gets activated .
We tested the BIG ADO paradigm on a bipartite association graph derived from a compilation of 32 million noun/adjective co-occurrences in Wikipedia . We identified two classes of nouns ( animals and household objects ) and pre-trained two networks to learn a subset of the noun/adjective associations , each with “expertise” mostly in one of the two noun classes ( Fig . 2A ) . Specifically , one network was pre-trained with a greater proportion of animal/adjective associations than of object/adjective associations ( and vice versa for the other network ) . BIG learning facilitated networks to acquire new information that was related to the information already stored . Moreover , the magnitude of this phenomenon increased with the level of specialization between animals and objects ( Fig . 2B ) . Note that , even in their “novice” domain of knowledge , networks cannot be completely “naïve . ” Even if the pre-trained proportion of “novice” edges is lower than in the domain of expertise , it must still be non-zero or else no subsequent associations could be learned . Interestingly , the effect was greater for animal expertise than for object expertise . Furthermore , more animal associations were learned when the network was pre-trained with the same number of animal and object edges . Both of these differences can be explained by two independent forms of background information: one intrinsic in the source data , and another dependent on the sample used to pre-train the network . The former was eliminated by repeating the simulations on random equivalent graphs ( Fig . 2B: right bar pairs ) . Direct analysis of Pearson’s coefficients of the bipartite graph Proximity function ( see Materials and Methods ) confirmed that the noun/adjective association is more specific for animals than for objects ( 0 . 69 vs . 0 . 48 , p<10-4 ) . To validate the above results against broadly applicable cases besides word associations , we tested the BIG ADO learning rule in a general class of random small-world graphs [19] resembling real-world architectures , organizations , and interactions ( Fig . 3A ) . Networks were pre-trained with samples of associations biased towards an arbitrary subset of nodes . As in the bipartite graph , the ADO filter gated subsequent learning of new associations by favoring those pertaining to this background information ( Fig . 3B ) . Next we investigated the ability of BIG to differentiate between “real” and “spurious” associations . Most co-occurrences experienced in everyday life do not reflect real associations , but rather events that happened together by chance . For example , suppose you were eating a grapefruit while experiencing the buzzing beetle described in the Introduction . Why should buzzing be associated with beetle and not with grapefruit ? Hebbian models form both associations , relying on later experience to reinforce those that reoccur and eliminating the others [12] , e . g . upon repeatedly dissociated experiences of eating a grapefruit without buzz and vice versa . Strikingly , the BIG ADO filter distinguished real from spurious associations ( Fig . 3C ) , facilitating the ability to learn relevant co-occurrences over “occasional” ones the first time around . In a simple protocol , each experience consisted of the co-activation of two independent pairs of connected nodes in the Watts-Strogatz graph . The resulting six co-occurrences correspond to two real associations ( between the two connected nodes in each of the pair ) and four spurious associations ( between neurons across the pairs ) . Inspection of the simulation outcomes confirmed that spurious “buzzing grapefruit” co-occurrences were not remembered because they lacked relevant background information . In the pre-trained network , the axon of buzzing overlaps with the dendrite of beetle ( high ADO ) thanks to the already acquired buzzing-wasp , flying erratically-wasp , and flying erratically-beetle associations . Thus , the potential association buzzing-beetle ‘passes’ the BIG ADO filter . In contrast , buzzing and grapefruit have little if any axonal-dendritic overlap; thus , the corresponding association is not formed according to the BIG ADO mechanism . The learning differentials of both expert-over-novice networks and real-over-spurious associations increased with the bias towards a subset of nodes in the Watts-Strogatz graph , and were observed over a broad range of model parameters ( see S1 Text 3 . 2 for additional results ) . The notion of representing mental states or elementary concepts in single ( “grandmother” ) neurons is appealing [23] but unrealistic [24] . Theories and experiments estimate that at least 50–200 cells take part in encoding each unit of thought [25 , 26 , 27] . Cell assemblies provide for redundancy , error-correction , and larger storage capacity . We thus extended the BIG ADO paradigm to cell assemblies . In cell assembly models , acquiring a new association between two co-occurring events entails formation of new synapses between the neurons representing one event and the neurons representing the other event . With the BIG ADO filter , forming synapse between a pair of co-active neurons requires appropriate pre-existing connections similarly to Fig . 1B , with the notable difference that the same neuron typically belongs to several cell assemblies . Among the first ( and simplest ) neural network models employing cell assemblies are Willshaw’s Associative Nets [21] . Simulations with the Willshaw model confirmed the BIG ADO results with the word association graph ( see S1 Text 2 . 3 for implementation detail and S1 Text 3 . 2 for analysis ) . However , the original Associative Nets achieve maximal storage capacity when cell assembly size is log-proportional to the number of neurons [20] . Such limitation on cell assembly size makes this approach unsuitable for learning realistic Watts-Strogatz graphs . A more sophisticated variant of this model , which achieves optimal Bayesian learning [22] , attains excellent performance for cell assembly sizes compatible with those estimated for real brains . This latter model ( Zip Nets ) enabled cell assembly implementation of the BIG ADO mechanism with generic Watts-Strogatz graphs . In a typical configuration , the network learned 50% of novel associations within its domain of expertise , but only 9% unrelated to prior knowledge . When two node pairs ( sampled randomly within and outside domain of expertise ) were co-activated at once , 30% of real associations were learned vs . 7% of the spurious ones . Sampling only within or outside the domain of expertise , the learning proportions for real and spurious pairs were 50% and 12% or 9% and 3% , respectively . Similar outcomes were consistently observed across a broad range of connectivity parameters in the small-world graphs . In particular , a substantially higher proportion of associations were learned within the domain of expertise than outside for any graph degree d ( the average number of edges per node ) from 8 to 24 and rewiring probability up to 80% ( Fig . 4A ) . The rewiring probability R defines by construction Watts-Strogatz graphs as hybrids between regular ( R = 0% ) and random graphs ( R = 100% ) . The fraction of spurious associations learned was substantially lower than that of real associations for degrees above 5 and rewiring probability below 50% ( Fig . 4A ) . This suggests that prior connectivity ( ADO ) provides a biologically realistic neural correlate of background information and its ability to gate learning in any highly clustered networks . In clustered networks , two nodes are more likely to be interconnected if they are both connected to a third node . This is a common property of many types of graphs that extends beyond Watts-Strogatz networks [28] . Although the adopted connectionist framework is an over-simplified model of nervous systems , this simplicity also reflects the foundational applicability of the BIG ADO learning rule . Specifically , the described mechanism does not depend on specific choices of parameters such as graph dimension , number of associations presented , learning threshold , and others . In particular , the main effect of axonal-dendritic overlap to selectively gate learning by background information was consistently reproduced in every combination of parameters conducive to adequate memory storage ( Fig . 4B ) . Moreover , the discrimination between real and spurious associations with cell assemblies in small-world graphs was also largely unaffected by the choice of numerical values . Importantly , however , this latter effect varied quantitatively as a function of selected model parameters ( Fig . 4C ) , such as the proximity load , which determines how topologically close an axon and a dendrite must be to constitute a potential synapse ( see section 2 . 4 of S1 Text ) . This is the key parameter distinguishing BIG ADO from traditional Hebbian learning: a new synapse is formed between two neurons when they fire together if and only if a potential synapse is already present . Thus , certain circuits might be better designed than others to support efficient one-trial learning depending on their specific plasticity and excitability ( see S1 Text 3 . 2 for additional results ) .
This report introduced a new biologically-motivated learning rule for neural networks that explains why it is easier to acquire knowledge when it relates to known background information than when it is completely novel [11] . The key idea is that this “background information-gated” ( BIG ) learning emerges from the necessity of neuronal axons and dendrites to be adjacent to each other in order to establish new synapses . Such basic geometric requirement was explicitly recognized in Hebb’s original formulation of synaptic plasticity , yet is not usually accounted for in neural network learning rules . The claim that existing structure matters for learning is not new [29] . However , the level of abstraction of current computational models of brain function fails to capture the details of axonal and dendritic shape . The critical breakthrough of this work consisted of parsimoniously relating “axonal-dendritic overlap” ( ADO ) to circuit connectivity by assuming optimal neuronal placement to minimize axonal wiring . This corresponds to a fundamental neuroanatomical constraint: an axon must pass close to the dendrites that are near other neurons it contacts . The topographic organization of the mammalian cortex ensures that nearby neurons on average encode related information [30] . Incorporating this new relationship into classic connectionist learning algorithms , we found that networks trained in a given domain more easily acquire further knowledge in the same domain than in others . If the proximity threshold is set to zero , the model reverts to a traditional neural network unconditionally learning all associations . From this perspective , the BIG ADO rule could be considered as a biological constraint on learning . However , to our initial surprise , the morphologically-motivated constraint on structural plasticity also endows neural nets with the powerful computational ability to discriminate real associations of events , like the sight of a lightning and the sound of the thunder , from spurious co-occurrences , such as between the thunder and the beetle that flew by during the storm . Thus , we surmise that the selectivity of synaptic formation implied by the ADO requirement provides a fundamental cognitive advantage over the unconstrained “fire together , wire together” plasticity rule of classic artificial neural networks . Of course the ability to associate completely unrelated facts or events may also be useful in many circumstances . Several different models have proposed that the hippocampus might be specialized for precisely that function , possibly leveraging its superior plasticity rate [31] or adult neurogenesis [32] . Our model suggests that this ability might also derive from the lack of topographic mapping in this structure ( e . g . hippocampal area CA3 ) . Moreover , the profuse axonal arbors of cortical neurons may enable access to a surprisingly large pool of intertwining dendrites through neurite outgrowth [33] , perhaps providing a counter-mechanism to balance the BIG ADO rule . The computational advantage of the BIG ADO algorithm over alternative learning rules can be quantified in terms of discrimination between real associations and spurious co-occurrences . If k pairs of real associations ( A1-B1 , A2-B2 , … , Ak-Bk ) are presented at the same time , BIG ADO selectively learns the correctly paired events over spuriously co-occurring ones ( e . g . A1-B2 , A2-B1 , etc . ) . A “fire-together , wire-together” rule without ADO constraint can achieve similar selectivity by repetition . In this case , each association must be presented multiple times in order to attain the same discrimination power displayed by BIG ADO in one-trial learning . The number of required repetitions grows with the number k of real associations presented together and also depends on the structure of the association graph . For example , in the conditions of Fig . 3 , BIG ADO learns real associations at a rate of 6:1 relative to spurious co-occurrences upon the first presentation . To obtain the same ratio in the absence of ADO if just five pairs are presented together , each association has to be repeated on average four times . Mammalian brains display greatest plasticity during development , but certain cortical regions remain plastic throughout adulthood [34 , 35] . Our research design is consistent with an initial phase of maximal plasticity , followed by a ‘mature’ state of conditional plasticity . Specifically , during pre-training , all witnessed associations are learned . Clearly , the anatomical constraint of axonal-dendritic overlap holds in all phases of development . However , the more prominent neuronal and axonal movements in earlier developmental stages would largely circumvent or alleviate the ADO filter . In practice , we pre-load the network directly with synaptic connectivity equivalent to that resulting from such an initial developmental phase ( representing ‘background knowledge’ ) . Afterword , the model preferentially learns associations related to previously acquired information . The resulting mature network not only avoids associating the ( most numerous ) spurious co-occurrences , but is also optimally structured to learn the associations most relevant to the environment in which it developed . Besides providing clear evolutionary advantages , these key features could also be applied in artificial intelligence and search engines . Background information gating explains the familiar ability to form stable memories based on single experiences ( as opposed to repetition ) . This process is complementary to ( and as fundamental as ) other factors known to control learning , such as valence and novelty . The proposed mechanism of axonal-dendritic overlap , based on the elementary anatomical organization of neuronal circuits , is also independent of neuromodulatory pathways likely to underlie alternative or parallel regulation of one-trial learning . This framework can also be useful to describe how semantic knowledge can be incorporated into existing knowledge . Moreover , the model offers a possible neural network correlate for the rapid memory consolidation occurring when new information is assimilated into a pre-existing associative “schema” or mental representation [36] . Other recent models have been proposed to explain the dependence of learning on prior knowledge [37] . The proposed BIG ADO learning rule is only conceptually related to axonal-dendritic overlap , as the anatomical data necessary to generate a complete model of all axons and dendrites in a network is still unavailable ( see e . g . [38] ) . Realistically , potential synapses might work in synergy with additional mechanisms conducive to the same learning rule . For example , presentation of individual elemental associations ( buzzing wasp , flying wasp , and flying beetle ) may lead to the formation of cell assemblies representing associations between higher-order concepts and their properties ( “flying insect” ) , as previously hypothesized [39] , possibly supported by ongoing structural plasticity [40] . Moreover , axonal-dendritic overlap may provide powerful constraints for the recruitment of individual neurons into cell assemblies . While cell assembly selection has been proposed as the core of knowledge representation in neural systems [41] , the underlying anatomical mechanisms have so far remained elusive [26] . Thus , the proposed link between neuronal structure and function may constitute an essential foundation for brain-based theories of cognition .
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We introduce and evaluate a new biologically-motivated learning rule for neural networks . The proposed mechanism explains why it is easier to acquire knowledge when it relates to known background information than when it is completely novel . We posit that this “background information-gated” ( BIG ) learning emerges from the necessity of neuronal axons and dendrites to be adjacent to each other in order to establish new synapses . Such basic geometric requirement , which was explicitly recognized in Donald Hebb’s original formulation of synaptic plasticity , is not usually accounted for in neural network learning rules . More generally , the level of abstraction of current computational models is insufficient to capture the details of axonal and dendritic shape . Here we show that “axonal-dendritic overlap” ( ADO ) can be parsimoniously related to connectivity by assuming optimal neuronal placement to minimize axonal wiring . Incorporating this new relationship into classic connectionist learning algorithms , we show that networks trained in a given domain more easily acquire further knowledge in the same domain than in others . Surprisingly , the morphologically-motivated constraint on structural plasticity also endows neural nets with the powerful computational ability to discriminate real associations of events , like the sight of a lightning and the sound of the thunder , from spurious co-occurrences , such as between the thunder and the beetle that flew by during the storm . Thus , the selectivity of synaptic formation implied by the ADO requirement is shown to provide a fundamental cognitive advantage over classic artificial neural networks .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
A Neural Mechanism for Background Information-Gated Learning Based on Axonal-Dendritic Overlaps
|
Brazil remains the country in the Americas with the highest prevalence of schistosomiasis . A combination of control efforts and development , however , has sharply reduced its intensity and distribution . The acquisition of specific schistosome populations may be dependent on host characteristics such as sex , age , geography , work , habits and culture . How these and other host characteristics align with parasite subpopulations may guide approaches to improve control . A cohort of more than 90% of the residents in two rural communities in Brazil participated in an epidemiologic survey of demographic , socio-economic and behavioral characteristics . The variables sex , age , intensity of infection , socio-economic index , % lifetime spent on site , previous infection , and trips outside the district were used to group parasites infecting individuals . Schistosoma mansoni infection status was determined by examination of stools submitted on 3 different days . The aggregate of eggs collected from the whole stool was used to determine degree of population differentiation from allele frequencies for 15 microsatellites . Infection prevalence was 41% for these communities , and the epidemiologic characteristics were similar to many of the endemic areas of Brazil and the world . Parasite population structuring was observed between the two communities ( Jost's D 0 . 046 , CI95% 0 . 042–0 . 051 ) , although separated by only 8 km and connected by a highway . No structuring was observed when infected individuals were stratified by host's biologic , demographic or epidemiologic characteristics . Those most heavily infected best reflected the communities' overall parasite diversity . The lack of differentiation within villages suggests that individuals are likely to get infected at the same sites or that the same parasite multilocus genotypes can be found at most sites . The geographic structuring between villages and the lack of structuring by age of the host further supports the impression of a population little affected by migration or drift .
The transmission of schistosomiasis is influenced by human culture , occupations and demographics among other factors . Also , our group and others have demonstrated that each individual host carries only a portion of the total available parasite genetic variability [1] , [2] , [3] , [4] , [5] , [6] , [7] , and thus host-to-host structuring may exist due to each individual's personal characteristics , such as age , sex , social status or residence . These are factors that may bring them into contact with genetically distinct populations of parasites or even influence their susceptibility . While these epidemiologic relationships are usually explored by associating human demographics with infection prevalence or intensity , by using genetic markers we can also determine if these host characteristics are associated with acquiring different parasite subpopulations . An immediate problem for any such analysis is how to sample the parasite population . Due to the biology and local distribution of the parasite Schistosoma mansoni , sampling for genetic analysis is not straightforward . The snail host , where asexual reproduction takes place , lives an average of 3 months , and cercariae collected at one point in time do not represent the whole genetic diversity found in humans [7] . In addition to differences in behavioral factors and biological susceptibility of the human host , the intermittent presence in the snail host increases the potential for differential acquisition of parasite genotypes . Sexual reproduction takes place in the human host where the adult worms are inaccessibly located in mesenteric veins . A portion of the hundreds of eggs produced daily by worm pairs remains trapped in tissues and will not contribute to the succeeding generation , whereas the majority of these progeny is shed in stool . New individuals enter the host only by infection , a form of migration . Since the adult parasites are long-lived , humans can accumulate a variety of individuals over time . Our approach to the population genetics of S . mansoni has been to analyze allele frequencies obtained by extracting DNA from the aggregate of eggs isolated from single stools . In this way the reproducing population of schistosomes from many individuals ( e . g . , most of the residents of small communities ) can be analyzed with a large sample size and a minimum of selection bias . An important problem for all genetic studies is determining appropriate sample size and avoiding selection bias . The population structure of most organisms is studied by collecting a sample of discrete genotypes and then aggregating or pooling these into allele frequencies for the whole population . This approach is dependent on the quality of the sampling performed . Depending on the organism and the specific population , sample sizes of 30 [8] or hundreds [9] , [10] have been deemed necessary to provide an adequate sample . Parasite populations add unique challenges to the problem of sampling since they are not simply structured as discrete organisms scattered or clustered across a landscape . They exist as populations within individual hosts ( infrapopulations ) as well as the collection of parasites within one host species ( component populations ) [11] . The latter represents the full genetic potential of which the infrapopulations are each a small sample . For the individual human infection with S . mansoni , a typical 200 g stool with a light infection of 40 eggs/g will have a total of 8 , 000 eggs . The miracidial stage can be hatched from eggs and collected for study . Samples of 10 , 20 , 30 individual miracidia may be small when diversity is high , and there may be bias for which eggs will hatch into miracidia and which can be collected . Further , the process of hatching and collecting individual parasites limits the number of infected people that can be examined . How to sample , what to sample and how much to sample has never been defined for schistosomes . Our approach to the population genetics of S . mansoni has been to analyze allele frequencies obtained by extracting DNA from an aggregate of eggs isolated from the whole stool of infected individuals . In this way the transmitted population of schistosomes from many or even all individuals ( in the case of a small community ) can be analyzed with a large sample size from many infrapopulations and a minimum of selection bias . Sampling larger numbers of infrapopulations also allows for stratifying hosts for comparisons . Finally , using this approach we have shown that the stool egg population has a similar genetic composition to the adult worm population [12] , [13] . In this paper we assessed risk factors for infection and differentiation of parasite infrapopulations by genotyping the aggregate of eggs obtained from infected individuals in two small rural villages . We divided parasites into “component” populations based on host geography as well as host biology , demography and epidemiology . We then estimated differentiation between these groups from their infrapopulation or component population allele frequencies . Although we previously observed structure based on geographic distance between these two nearby communities [3] , we found little population structuring within the villages or between hosts . Finally , we explore the implications of these findings for the nature of schistosome populations in rural communities .
The Committee on Ethics in Research of the Oswaldo Cruz Foundation of Salvador , Bahia , the Brazilian National Committee on Ethics in Research and the Institutional Review Board for Human Investigation of University Hospitals Case Medical Center , Cleveland , Ohio approved the study design . All subjects provided written informed consent or in the case of minors , consent was obtained from their guardians . All aspects of the study have been conducted according to the principles expressed in the Declaration of Helsinki . Two rural Brazilian communities – Jenipapo ( population 482 ) and Volta do Rio ( population 367 ) – were studied because of their high prevalence of schistosomiasis , their size and their relative isolation . They are administered by the municipality of Ubaíra ( roughly equivalent to a county in the USA ) and are located in the Jiquiriçá River valley in the state of Bahia . By road they are 270 km SE of the State capitol and principal city , Salvador . Each was at least 12 km from a major town and 8 km distant from each other . Volta do Rio is also divided geographically into an upper and lower section with a 40 m difference in height above the river ( Figure 1 ) . The major sources of livelihood are planting cacao , bananas cassava , cattle raising and other animal production . There is a Federal Family Health Program clinic in Jenipapo with a permanent staff consisting of a nurse , dentist and part-time physician . Volta do Rio has a simpler health post that employs only a group of nurses . Jenipapo also has primary and secondary schools attended by all of the nearby small communities , including Volta do Rio . As previously described [3] , an epidemiologic and parasitologic survey was conducted for all inhabitants ≥1 year old who agreed to participate . Questions concerning housing , sanitary habits , socio-economic conditions and water contact were asked as part of the epidemiologic survey . For water contact , individuals or guardians for minors <10 years of age were asked if they frequently used any of the 8–9 previously identified major water contact sites and what activities they tended to perform there . The socio-economic evaluation was based on the Criteria for Economic Classification of Brazil ( http://www . abep . org/novo/Content . aspx ? ContentID=139 ) . These criteria with revisions have been used nationally for more than a decade to characterize the purchasing power of the Brazilian population using possessions ( color TV , radio , bathroom , car , washing machine , videocassette/DVD , refrigerator , freezer ) , services ( maid/housekeeper ) and degree of education of the head of household . The index places households within 8 categories ranging from minimum monthly wage to 13X minimum monthly wage . The interpretation of these categories is weighted for metropolitan regions of the country including Salvador , Bahia . Three stool samples each on different days were requested from each resident over a period of 1 week for quantitative examination by the Kato-Katz method . Individuals who tested positive for S . mansoni infection were treated with a single oral dose of praziquantel according to Brazilian Ministry of Health guidelines [14] . Those found to have intestinal nematodes were treated with mebendazole . All stools were weighed to the nearest 0 . 01 g on a digital balance upon arrival in the laboratory . Whole stools from single individuals that were positive for S . mansoni were homogenized in a blender containing 200 ml of 2% saline followed by selective sieving [15] through two mesh nylon filter bags ( FSI , Michigan City , Indiana , USA ) with 300 and 55 µm pore sizes , respectively . The retained material was then sedimented in 2% saline . Since eggs were among the densest elements in the stool [16] , the bottom 5 ml of sediment was collected and kept frozen at −20°C until used for DNA isolation . The 5 ml frozen stool sediment was mixed with 5 ml 2X extraction buffer ( 50 mM NaCl , 100 mM Tris–HCl , pH 7 . 5 , 10 mM EDTA , 1 . 0% SDS ) and 10 ml H2O-saturated and Tris-buffered phenol , pH 7 . 5 . This was followed by two chloroform/Isoamyl extractions [3] . The DNA was then ethanol precipitated and suspended in 10 mM Tris , pH 7 . 5 , 1 mM EDTA . Finally , the sample was treated with cetyl trimethylammonium bromide ( CTAB ) to remove PCR inhibitors [17] . To genotype S . mansoni eggs , 15 microsatellite markers were used as described previously [2] , [3] . For each marker a duplicate PCR reaction using 2 µL of extracted DNA from stool was performed , totaling 30 reactions per sample . PCR products from each sample were combined into groups of three or four markers and processed on an Applied Biosystems 3730xl DNA Analyzer . PeakScanner software ( Applied Biosystems , Carlsbad , CA ) was used to determine peak heights from which allele frequencies were calculated . Successful PCR reactions were defined as those in which there was at least one peak >500 pixels in the size range expected for a given marker . All peaks less than 100 pixels were excluded . We attempted to genotype all samples , and if multiple samples from the same individual amplified , their mean allele frequency was used . Subsequent population analyses were limited to those samples where a minimum of 12 out of 15 markers genotyped successfully . Information collected during the study was double-entered into the program Epi Info version 3 . 5 . 3 [18] . Pearson's chi-square and Student's t-test were used to compare categorical and continuous data , respectively , and a p-value of 0 . 05 was used as the criterion for statistical significance . Multivariable analyses were carried out using logistic or linear regression in SPSS ( Version 17 ) . Individuals with missing data were dropped for the analysis of that variable . For population genetic analyses , allele counts for each sample were calculated by multiplying the allele frequencies at a microsatellite locus by the total egg counts found on the Kato-Katz assay . Infrapopulations were stratified by the host's residence , sex , age , intensity of infection , household , travel history , number and location of water contacts and socio-economic condition . Genetic differentiation between populations was expressed as the index Jost's D [19] calculated using the program SPADE ( http://chao . stat . nthu . edu . tw ) . D is a true differentiation index and does not rely on assumptions of Hardy-Weinberg equilibrium , which do not apply to infrapopulations . After grouping , each pair of infrapopulations can be compared within a group or the combined allele numbers and allele frequencies can be used to form a component population . We make the following differentiation and diversity comparisons: Egg counts were recorded as eggs per gram of stool ( epg ) and log-transformed to approximate a normal distribution for analyses . Arithmetic means were calculated for group Di , Dic and AE . For the Di and the Dic group means were compared by bootstrapped Student's t-test with 1000 resamples , since the distribution of these measures is unknown . There is no standard for effect size for these new types of comparison . For the Dc , we follow the convention used for interpreting FST values [21] . Dc values from 0–0 . 05 indicate little differentiation; from 0 . 05–0 . 15 , moderate differentiation; and above 0 . 15 , great differentiation [22] . Changes in D rather than the absolute value below the 0 . 05 range , however , may still indicate a significant obstacle to gene flow .
The study group consisted of 814 of the 849 ( 96% ) inhabitants residing in the 243 households of the two villages . The mean age was 31 . 5 years ( ±22 . 2 ) , and slightly more women than men were enrolled ( 53 . 7% ) . Most subjects were born in their current municipality ( 83 . 7% ) , and the average percent of lifetime spent in the municipality of Ubaíra was 93 . 5% . Considering the history of travel outside of the district , 25 . 3% reported any travel , and a minority ( 19 . 5% ) of those who traveled reported contact with surface water . There were some differences for the two geographically distinct areas of Volta do Rio ( VdR ) . The percent of those traveling outside of the district was greater for individuals from lower VdR than upper VdR ( 34 . 2 vs . 22 . 2% , p = 0 . 02 ) , but they remained outside of the area for similar lengths of time ( 61 . 92 vs . 57 . 82 days , p = 0 . 51 ) . There were significantly more individuals in upper VdR who had at least one family member infected ( 36 . 9 vs . 25 . 2% , p = 0 . 02 ) . Most demographic and epidemiologic characteristics were similar for both villages ( Table 1 ) , with the exception of the socio-economic index and sanitation . Jenipapo had a somewhat greater purchasing power for ( 12 . 0 vs . 10 . 7 , p = 0 . 017 ) . The mean socio-economic index for the two localities was 11 . 4±4 . 3 , which corresponds to the second lowest of the 8 income categories used nationwide . A socio-economic index of 11 points translated to a family income of approximately $330/month in 2009 . Nearly all homes in both villages have piped water and indoor flush toilets . The 2 most common destinations for these toilets was either a septic tank or the river . Despite a lower socio-economic index , the disposal of human waste was more adequate in VdR than Jenipapo , and upper VdR had better waste disposal than lower VdR . In VdR the Jiquiriçá River is shallow , sluggish and seasonal , while at Jenipapo the Jiquiriçá is joined by a major stream that maintains flow in the river throughout the year . This may explain the different approaches to sanitation . Drinking water in both communities comes from sources several km away from the river . The prevalence of S . mansoni infection was higher in Jenipapo ( 45 . 8% ) than VdR ( 35 . 1% ) , but the mean intensity of infection was similar ( Table 1 ) . The lower limit of detection was 8 epg and the highest mean intensity observed was 3 , 792 epg . Some 31 . 3% of residents knew someone with current or past infection with S . mansoni , and 34 . 2% had one or more relatives with schistosomiasis . Two hundred and ninety seven individuals ( 37% ) reported past infection with S . mansoni , and 93 . 6% of those reporting infection also reported being treated , most often with oxamniquine ( 64 . 0% ) . None had been treated with praziquantel , which was newly approved in Brazil for treatment of schistosomiasis at the time of the study . No variables or contact points were correlated with intensity of infection . Characteristics that were associated with a higher risk for S . mansoni infection were living in Jenipapo , age ( 2nd , 3rd and 4th decades compared to 1st , Figure 2 ) and male sex ( Table 2 ) . Traveling outside of the municipality of Ubaíra in the past year was not associated with an increased risk for infection , but water contact while traveling was ( OR of 2 . 3 , p = 0 . 012 ) compared to those reporting no contact . A self-reported history of past infection overall had no correlation with risk , but reporting past treatment for S . mansoni did ( OR 3 . 07 , p = 0 . 02 ) . Eight water contact points in Jenipapo and nine in VdR were identified as those most commonly visited by villagers . The number of visits and nature of activities at each site were asked during the epidemiologic survey . The risk of being infected with S . mansoni increased substantially as the individual had contact with an increasing number of sites ( Table 2 ) . After adjusting for age and sex , people who reported contact with one point in Jenipapo and two in VdR were significantly more likely to be infected ( Table 3 ) . All of these points were common crossings to reach from one side of the river to the highway . A log was used as a temporary bridge at one point each in the two villages . However , at contact point 5 in Jenipapo ( Figure 1C ) the activity most associated with infection was fishing ( OR = 2 . 96 , p = 0 . 012 ) , which is usually performed while wading in the river . In VdR , at the point not used for crossing the river ( P3 , Figure 1B ) formed a pool , and bathing here was most associated with infection ( OR = 3 . 55 , p<0 . 001 ) . Working and walking at this site were protective ( OR = 0 . 1 , p = 0 . 012 and OR = 0 . 043 , p = 0 . 036 , respectively ) , while fishing and playing in the water were also associated with a risk for infection ( OR = 4 . 18 , p = 0 . 048 and OR = 4 . 93 , p = 0 . 026 , respectively ) . The only significant activity associated with those who used the site and were uninfected was collecting water ( OR = 5 . 1 , p<0 . 048 ) . While individuals younger than 15 years old did not report more water contact than those older ( p = 0 . 540 ) , the type of contact may have involved more or longer exposure . Water contact for children tended to involved leisure activities such as walking ( p = 0 . 02 ) , swimming ( p<0 . 001 ) and playing ( p = 0 . 002 ) compared to older individuals who contacted water primarily through activities associated with labor , such as working ( p = 0 . 02 ) and obtaining water ( p = 0 . 05 ) . Fishing was equally frequent between both age groups . Males did report visiting 1 . 5 times as many water contact points as females ( p = 0 . 001 ) . Our previous study [3] used only samples that were positive by Kato-Katz in all 3 stools ( n = 116 ) . For the analysis here we included all samples regardless of the number of stools positive for S . mansoni , thus , genotypes from 226 of the 335 infected individuals ( 67 . 5% ) were included for analysis . Of those genotyped , 51 . 8% were genotyped for 3 samples , 14 . 6% for 2 samples and 33 . 6% for only 1 sample . The differentiation between the two geographic component populations of the two villages ( D = 0 . 046 , CI95% 0 . 042–0 . 051 ) was similar to that previously reported [3] . To determine whether related parasites clustered with host characteristics , component populations were formed by grouping infrapopulations based on host epidemiologic characteristics of sex , age , household , economic status , place of birth , frequency of travel , previous infection and number and location of water contacts . Differentiation between these component populations was analyzed for the Di , Dc , Dic and effective allele number . Di was significantly different for the individual villages and both villages combined when infections were grouped by sex , age , infection intensity and certain water contact sites ( Table 4 ) . We also tested similarity of infrapopulations within households . Only 11% and 5% of households in Jenipapo and VdR , respectively , had more than one member infected . The mean Di for household members in Jenipapo was 0 . 065±0 . 040 and 0 . 086±0 . 047 in VdR compared to 0 . 095±0 . 033 and 0 . 123±0 . 066 for all those infected in Jenipapo and VdR , respectively . The bootstrapped t-tests for the mean Di of household clusters versus all individuals in the village were significantly smaller ( p = 0 . 004 , p = 0 . 030; Jenipapo and VdR , respectively ) . The Dc indicates that the composition of the populations based on host characteristics differ little in their genetic composition . The Dic was significant for age overall , but this was mainly due to a difference in VdR where children ≤15 acquired parasites that were more genetically differentiated from the whole community of parasites than those infecting adults . Overall and in both communities , infrapopulations from heavy infections were less differentiated from the community's component population than lighter ones . The AE was only significantly different for intensity of infection for the two villages combined as well as separately . This is a measure of diversity , and higher intensity infections averaged higher effective allele numbers . AE was also associated with the socio-economic index in Jenipapo . Since different age groups may be exposed to different subpopulations of S . mansoni , we further stratified age into 4 groups: 0–7 , 8–15 , 16–40 and >40 . We found that the youngest age group gave the highest Dc in pairwise comparisons and the highest mean Dic of any group , but this age group was also the smallest ( n = 12 ) , had the lowest prevalence and the lowest intensity of infection . When 12 individuals with similar intensities of infection ( sample sizes ) were compared from each age group , these differences resolved .
The communities of Jenipapo and Volta do Rio are typical of the region in their level of development and access to sanitation . They also are similar to many other areas endemic for schistosomiasis in their age-specific prevalence and intensity of infection [23] . Differences in the prevalence of infection between these otherwise similar communities may be due to differences in how human waste is handled . In part these choices may be the result of the presence of constant flow in the river in Jenipapo and seasonal flow in VdR . Consistent with this , upper VdR which is much further from the river had higher use of septic tanks and fewer homes reporting using the river . In Brazil , economic development and control efforts using education and the drug oxamniquine ( used prior to praziquantel ) have greatly reduced the amount of hepatosplenic disease , but the infection prevalence in many areas has not changed . In these two villages , the current prevalence when based on a single stool examination is no different from the 15–20% prevalence observed for the state of Bahia in the 1950's [24] , [25] and at the start of control programs in 1976 [24] , [26] , [27] . When multiple stool samples are examined , the true prevalence of infection is even two to threefold higher . Some common risk factors found in other communities can be identified in this study . Age between 10 and 20 was associated with the highest prevalence and intensity . In Brazil , male sex is associated with increased risk [28] , but in other parts of the world infection can be more prevalent in females [29] . This difference is likely due to differing sexual roles in work , play and ultimately water contact . Water contact is an essential step in transmission of schistosomiasis . While this variable would seem to be a strong risk factor with high correlation with infection , it has been difficult to measure and then associate with intensity [30] . Even when water contact is directly observed [29] , the frequency of contacts is not always predictive . Questionnaires have been the simplest and least expensive way to assess risk factors . In Brazil , questionnaires have been shown to produce reliable responses that correlate with risk of infection [31] , [32] , but even here there can be significant place-to-place variation [33] requiring questions tailored to the specific location . Water contact has been solicited in multiple ways in terms of location , type of activity , time of contact and percent body exposure . We asked only which sites were visited and what activities were commonly performed there . The questionnaire was administered prior to all stool examinations and thus not biased by knowledge of the infection status of respondents . We found that simply counting the number of sites visited was most associated with prevalence of infection . Further , while travel away from the area was not associated with infection , self-reported travel combined with surface water contact was associated . These associations tend to validate the responses given by the residents . In addition to risk for prevalence and intensity of infection , we sought to identify risk factors for acquiring specific parasite populations . The moderate differentiation between infrapopulations indicates that each individual collects a limited portion of the total genetic variability from the component population . This non-homogeneous distribution together with differences in water contact , occupation , habits , sex , years of exposure , etc are all reasons for structuring of the parasite population within different demographic categories of the human host . No population structuring , however , was observed . In another human population with a different intensity of transmission or different economic , cultural or geographic organization the distribution of parasites might be different . Geographic structuring showed that over a short distance schistosome gene flow is limited in this region ( Dc Jenipapo/VdR = 0 . 046 ) . By contrast , within the two villages , when we assessed differentiation based on individual water contact sites , we found no difference in Dc among the sites or for number of sites visited . In VdR in particular , where there is a significant geographic difference in the height of the two parts of the city relative to the Jiquiriçá River and a highway between them , we were unable to demonstrate geographic differentiation . This indicates that , within the resolution of our methodology , local gene flow is high within the villages , but not between them . Individuals tend to be infected at the same sites or the same parasite multilocus genotypes can be found at most sites . They do not tend to contaminate the waters in nearby villages , and the school sanitation system ( serving children from both villages ) is unlikely to contribute to the local parasite population . The marked difference in age-specific prevalence , intensity and perhaps increased exposure during water contact suggest that children are likely to be more exposed than adults to the current component population present in the resident snails . However , the lack of differentiation by age suggests that current and past populations are largely undifferentiated , and that over at least the last 5 years ( the 95% CI for parasite life-span is 5 . 7–10 . 5 years [34] ) there has not been a large degree of migration or selection , also supported by the geographic structuring between the villages . The amount of differentiation within the groups of infrapopulations ( Di ) defined by host characteristics was often significantly different for multiple host factors , but we have no basis for comparison to say if this is biologically meaningful . By contrast the index Dic was significantly different for only intensity of infection in both villages . The socio-economic index and age are variably associated , but these may be secondarily related to intensity . The Dic is a measure of how differentiated an individual infrapopulation is from the whole adult worm/egg parasite community . It serves as a useful measure of how effectively individual hosts within a group sample the component population . The higher the intensity of infection , the more samples are present , which results in a better representation of the component population . This will reduce D for the infrapopulation relative to the component population . For the mean effective allele number , a measure of diversity , only intensity of infection ( <400 or >400 epg ) showed significant difference for both villages . This is consistent with expectation . In this area , the sampling of the most heavily infected , who are usually between 7 and 15 years of age , might be the best way of estimating the composition of the component population without sampling everyone . This limited sample would still lack precision , and age alone was not significantly associated with the Dc or Dic for Jenipapo . An important issue for these conclusions is the sensitivity of the methods employed for differentiating parasite subpopulations . We do know that our approach is sensitive enough to differentiate the component population for the two villages , and that in the laboratory , different laboratory-maintained S . mansoni populations from the same laboratory and different lots of parasites from the same life cycle can be distinguished [12] . We were able to genotype at least 1 sample from 67% of those infected . Since we have shown infrapopulation allele frequencies are stable over at least the span of a week , obtaining a single stool is unlikely to be a source of error . Most of those we were unable to genotype had low egg counts and low DNA concentrations [3] . Most of the cryptic infections we failed to detect are also likely to have been of low intensity . They were , therefore , less likely to contribute significantly to the genetic composition of their component populations . The relative relationship of the genetic composition of eggs to adult worms is unknown in natural infections , but in laboratory infections in mice , allele frequencies between these two stages were very similar [12] . There is no one approach that address all problems in population genetics , but the approach taken here is well suited to measure differentiation , since it allows for many large samples . Certain population genetic indices , such as the FIS , cannot be well estimated from aggregated data , however . In addition , we are unable to identify null alleles . This should not affect estimates of differentiation for populations in which the rate of null alleles is likely to be similar . Attempts to control S . mansoni infection in Brazil were successful in decreasing intensity of infection , and therefore , morbidity and mortality of the disease , but the infection has far from disappeared . An understanding of the dynamics of transmission and the distribution of the parasite at the population level can contribute to planning control measures . We show that there is little population sub-structure by host characteristics to influence how praziquantel therapy should be distributed . There are no special reservoirs of distinct parasite populations within the community , and much of transmission is local with good evidence for a barrier to gene flow with a nearby community . Future studies will examine how applicable the patterns seen in these communities are to others in Brazil and elsewhere . Until elimination has been achieved , surveillance and treatment will need to be continued and improvements in sanitation advanced .
|
Schistosomiasis is one of the world's most important parasitic infections . Its elimination has proved difficult even in countries such as Brazil where access to treatment is readily available . Infection is the result of human contact with surface water where there are infected snails , so that human biology and habits may bring different individuals in contact with different groups of parasites . Identification of schistosome subpopulations may assist understanding transmission patterns and guide control efforts . We compared microsatellite allele frequencies from all of the infections in 2 small villages and determined that the movement of parasites between them was limited . Individual infections were distinct composites of parasites , but if infected humans were grouped by demographic and epidemiologic characteristics , there was no evidence that specific parasite subpopulations were being selected in these types of hosts . Infections were also not differentiated when stratified by host's age indicating that the populations were stable over time . Since the infection cycle requires human fecal contamination of water , local human behavior can to some degree be inferred from the patterns of schistosome subpopulation distribution .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2013
|
Characteristics of the Human Host Have Little Influence on Which Local Schistosoma mansoni Populations Are Acquired
|
Vaults are the largest known cytoplasmic ribonucleoprotein structures and may function in innate immunity . The vault shell self-assembles from 96 copies of major vault protein and encapsulates two other proteins and a small RNA . We crystallized rat liver vaults and several recombinant vaults , all among the largest non-icosahedral particles to have been crystallized . The best crystals thus far were formed from empty vaults built from a cysteine-tag construct of major vault protein ( termed cpMVP vaults ) , diffracting to about 9-Å resolution . The asymmetric unit contains a half vault of molecular mass 4 . 65 MDa . X-ray phasing was initiated by molecular replacement , using density from cryo-electron microscopy ( cryo-EM ) . Phases were improved by density modification , including concentric 24- and 48-fold rotational symmetry averaging . From this , the continuous cryo-EM electron density separated into domain-like blocks . A draft atomic model of cpMVP was fit to this improved density from 15 domain models . Three domains were adapted from a nuclear magnetic resonance substructure . Nine domain models originated in ab initio tertiary structure prediction . Three C-terminal domains were built by fitting poly-alanine to the electron density . Locations of loops in this model provide sites to test vault functions and to exploit vaults as nanocapsules .
Vault ribonucleoprotein particles are found in the cytoplasm of most eukaryotic cells [1] . Ninety-six copies of major vault protein ( MVP; 95 . 8 kDa ) form the thin , hollow vault shell with dimensions reported as 725 × 410 × 410 Å3 [2] . The MVP shell encapsulates a 50 × 106–Å3 interior volume that contains 2–4 copies of telomerase associated protein 1 ( TEP1; 290 kDa ) , about 12 copies of an enzyme , poly ( ADP-ribose ) polymerase ( VPARP; 193 kDa ) , and 8–16 copies of a small untranslated RNA . The mass of a rat liver vault is about 13 × 106 Da [3] . Most eukaryotic cells contain upwards of 10 , 000 copies of vaults [4] . MVP expressed in insect cells self-assembles into vault shells [5] . Vaults were recently shown to have a protective role in innate immunity [6] . MVP co-localized with Pseudomonas aeruginosa in lung epithelial cells at an early stage of infection , and MVP knockout mice [7] , which do not form vault particles , were shown to be more susceptible to bacterial lung infection . Vaults had previously been implicated in multidrug resistance [8] and cellular signaling [9–12]; however , their exact role in any of these pathways remains elusive . Vault structure has previously been probed by transmission electron microscopy , cryo-electron microscopy ( cryo-EM ) , and nuclear magnetic resonance ( NMR ) . Multi-image averaging greatly clarified the cryo-EM image of the MVP shell [1] . Vault anatomical terms , emerging from both earlier work and our own , are shown in Figure 1 . Internal contents of rat vaults and new features of modified recombinant vaults have been localized by cryo-EM difference mapping . The RNA and a portion of TEP1 reside inside the vault near the ends of its two caps [13] . The N termini of MVP form the waist and extend toward the vault interior , and VPARP localizes onto the inner surfaces of the vault [2] . During our work , an MVP substructure was determined by NMR ( residues 113–221 of human MVP [14] ) . Engineering of the vault by encapsulation of exogenous components has begun [15]; proteins can be targeted to the inside surface of the vault by expression as fusions with either the N terminus of MVP or a VPARP-derived targeting domain , and localization to the vault interior can be confirmed by cryo-EM difference mapping . Extending the cryo-EM vault structure via crystallography to derive an atomic model is of great potential value in designing modifications of the vault structure and to elucidate function . The crystallographic difference-Fourier technique applied to future cocrystals could precisely localize internal vault components , while indicating their shapes and thus orientations relative to the MVP shell .
Phasing was initiated by manual placement of cryo-EM electron density of a half vault at a crystal 2-fold axis ( see Methods ) . The phases , and thus the detail in the image of the vault , were initially improved by density modification using a single 48-fold rotational noncrystallographic symmetry ( NCS ) operator ( marked NCS in Figure 1 ) . NCS is symmetry of the vault that is not shared with the crystal . The results from testing parameters for averaging paralleled those reported for spherical viruses [16 , 17] , except that the phases “condensed” into two pseudo-Babinet-inverse sets ( see Methods and Figure S1 ) . One phase set was selected because the map derived from it contained double-disk C-terminal structures that could plausibly contain 24-fold symmetric MVP chains in each layer ( marked 24C in Figure 1 ) . The featureless cryo-EM electron density separated into globules in the more plausible 48-fold averaged electron density map ( Figure 2 ) , indicating more preferential cohesion within short segments of the MVP chain than between consecutive segments . This meant that the MVP monomer , at least below the C-terminal cap structure , folded into domains ( see Figure 1 for initial partitions ) . The first averaging was not biased by prior expectation of domains . The density globules were spaced as would be backbone atoms with side chains between . The barrel portion of the vault appeared built from vertical “staves” of stacked domains . Observation of stacked domains parallels one conclusion of the NMR spectroscopists [14] . This initial 48-fold averaging was later improved by “dot model refinement , ” applying concentric 24-fold ( density block 11 in Figure 1 ) and 48-fold ( density blocks 1–10 in Figure 1 ) NCS axes ( see Methods ) and domain-shaped “dot models” to re-initiate the phase sets . The enantiomer of the electron density map was assigned during model building . Each half vault consists of 24 identical pairs of MVP chains A and B . Chains A and B differ only near their C termini . The unique parts of the cpMVP model ( chain B and C terminus of chain A ) were built into the electron density map resulting from “dot model refinement” ( Figure 3 ) . Because of nonequivalence of the C termini , the unique part of the model was assembled from 15 models of 14 domains . The stack of 15 domain models is shown in Figure 4 ( see Table 1 for domain partitions; see Methods for construction details and for model validation ) . The cpMVP model contains 749 of the 873 residues expected for this construct , starting at residue 3T of the N-terminal cysteine tag inside the vault waist , and ending in nonequivalent residues 779 in the two C-terminal cap disks . C-terminal residues 780–861 appear to be located outside the vault , above the present model ( VAK , LHR , and P . Stewart , unpublished data ) . The 15 domain models from three sources are shown as panels of Figure 5 . Domains 3 , 4 , and 5 were derived from the NMR structure of domains 3 and 4 ( Protein Data Bank ( PDB ) [18 , 19] entry 1Y7X [14] ) . Domains 1 , 2 , and 6–12 originated in models predicted with the ROSETTA algorithm [20–22] operating on several MVP sequence segments ( see Table 1 and Methods ) . Domain 13 and the nonequivalent C-terminal domains 14a and 14b ( see next section ) were built by inserting poly-alanine segments into density , then iteratively shifting and modifying segments to pack the density with plausible topology and backbone geometry . The MVP sequence was applied to domains 13 , 14a , and 14b when the other cpMVP domain models were nearly complete . As discussed in the Validation section of Methods , most domain models appear correct by the available criteria: correlation of backbone to density , plausibility of backbone geometry , and by estimation of side-chain interactions . The MVP structure in the “crossover zone” ( Figures 1 , 4 , and 5K ) reduces the vault symmetry from 48-fold in the waist , barrel , and cap helices ( residues 3T–715 ) to 24-fold in the C-terminal cap disks ( residues 716–779 ) . cpMVP model chains A and B become nonequivalent in the crossover zone . Assuming that identical sequences in chains A and B would result in similar structures , the crossover model was built as short A and B segments adjacent to approximate local 2-folds . The model shown in Figure 5K , when repeated 24 times and viewed at low resolution , would form the two electron density rings in the crossover zone between the two symmetries . The electron density in the two C-terminal cap disks indicated that the pairs of MVP chains enter the disks in opposite directions ( Figure 5L ) . Reasoning as above , the C-terminal cap disk models were built upside down relative to each other . The asymmetric unit of the vault is thus a dimer of MVP molecules ( model chains A and B ) . To complete the AB dimer model from the unique parts of the cpMVP model ( Figure 4 ) , chain B residues 3T–715 were rotated by one leftward increment of 48-fold NCS rotation to produce chain A residues 3T–715 . The asymmetric unit of the vault crystal is a half vault built by 24-fold NCS rotation of the AB dimer ( blue-red pair in Figure 6A ) . The 417- × 417- × 675-Å3 whole-vault model ( Figure 6B; fills density of Figure 3 ) is generated from the half-vault model by 2-fold rotation around the crystal y-axis ( bottom of Figure 6A ) . The whole-vault model may be reconstructed from the cpMVP dimer model and rotation matrices contained in PDB entry 2QZV . Because the full model in Figure 6B ( 96 copies of 749 residues ) is cumbersome to examine , a partially assembled cpMVP model is provided as Model S1 .
Building an atomic model into 9-Å electron density represents crystallography at the edge of what is possible . Model building could only be attempted because the locations of the N and C termini had already been established by cryo-EM , and because the electron density of the vault shell is very thin . The “petal” shapes of collapsed vaults [3] indicated that the MVP domains stack vertically , thus limiting the volume of density to consider for each domain . That is , the sharp edges of the “petals” limit lateral excursions of the polypeptide chain , supporting the quasi-linear spoke structure that we find for MVP in the vault . In building the model , we assigned model shapes of domains into electron density shapes , resulting in what we term a draft model . We recognize the substantial uncertainties in this model , and discuss them in Text S1 . The construction of the draft model is motivated by two goals . The first is to lay a basis for further x-ray crystal studies of vaults . The next steps are crystal improvement of the vault shell and crystallization of substructures , partitioned at domain boundaries derived from our current model and sequence analysis . The substructures can be inserted into density derived from crystallography of the whole vault , as has been done for cryo-EM density of other large structures [23–25] . Such a cloning , expression , and crystallization effort could be hindered by the side-to-side interactions that build the vault ( Figure 6 ) , but these could be alleviated by residue replacements at the interaction points . The second reason to build the draft model is to guide projects of vault engineering , discussed in the following section . Identifying or engineering a specific property , such as metal binding , would require reasonably accurate juxtaposition of ligand atoms . We have tentatively identified some candidate metal-binding sites by the simplistic means of searching for adjacent aspartate and glutamate residues . At the local 2-fold axes between N-terminal domains ( yellow bars in Figure 5A ) , Glu 4 and Glu 5 face Glu 4 and Glu 5 of the nonequivalent MVP in the other vault half , backed by two copies of Met 1 side chains [26] . Asp 20 in one vault half faces Asp 20 in the equivalent chain in the other vault half ( across the black 2-fold bars in Figure 5A ) . Metal affinity at the N termini is consistent with observation of acid dissociation of vault halves [27] . The model of domain 12 ( Figure 5I ) reaches left to nearly bring together Asp 615 and Asp 566 ( or possibly Asp 570 ) in domain 11 from two positions left . Thus these aspartates may be a metal affinity site . The draft model offers ideas about the binding sites for the other vault components . Charge clusters could signify affinity sites for internal vault components . Negative charges clustered by sequence adjacency were found on the inside surface of the vault at domain 6 ( Glu 342 , Glu 344 , Glu 346 , and Glu 347 ) . Positive charges clustered by the fold were found on the inside surface of the vault at domain 10 ( Lys 506 , Arg 507 , His 509 , Arg 511 , and Arg 512 ) . Residues 102–112 and 277–305 could not be placed in density . The site that 277–305 would occupy is slightly above the location indicated by cryo-EM analysis as the site with most binding energy for the MVP interaction domain of VPARP [15] . Atoms of 277–305 could become ordered on contact with VPARP , and this loop could be a target for insertion of a binding motif in an engineered vault . The draft model provides a list of sequence positions likely to be loop structures where ligand-binding sequences may be inserted . Passenger proteins could then be targeted to the vault interior or exterior ( analogous to [15] ) . The estimated domain boundaries and preliminary model may be useful for further fold predictions and fold recognitions . The draft model of the vault shell offers new conjectures about vault function . It has been suggested that vaults may interact with lipid rafts [6] . A bulk property , such as membrane binding , would be enhanced by the geometric repeating vault structure . In domains 3 , 4 , and 5 ( as currently folded ) , side chains of Trp 143 , Trp 196 , and Trp 249 are located on an almost straight vertical line ( Figure 5C ) . The left-right rotational repeat generates a geometric belt of membrane anchor residues around the vault barrel . The cascading energy of immersing triples of Trp side chains in a membrane could be enough to bend the membrane , or to initiate a vertical split in the vault barrel . A split vault could better contact the membrane , and could release vault contents . An amphiphilic crevice that could bind lipid was found at the top of the vault shoulder . The inner surface of the crevice ( Figure 5H ) is formed by the top of domain 10 , surfaces of left and right copies of domain 11 , and the bottom of domain 12 ( Figure 5I ) . The electron density for domain 12 indicates disorder , suggesting that its beta-sheet could be mobile . The draft model hints at the origin of the striking eight-petal geometry of the collapsed vault structure [3] . How do 24 identical MVP dimers of the half vault break apart into eight identical petals ? The answer may be at the top of the shoulder region . Domain 12 of each cpMVP chain overhangs two copies of domain 11 to tie together groups of three cpMVP molecules ( see left panel of Figure 4 and top of shoulder in Figure 5I ) . This is at the base of the coiled-coil region previously thought to stabilize the vault [28] . Vaults may thus collapse into eight petals of six chains each ( see Figure 9 of [3] ) in part because the MVPs are tied together as threes at the top of the shoulder but twos in the barrel region . An MVP C-terminal structure very similar to the nonequivalent C termini of this model ( top of Figure 4 , and Figure 5L ) could be responsible for previous observations of TEP1 density [29] . The model contains two C-terminal disks built upside down relative to each other . According to this model , if TEP1 and its RNA localize to the internal surface of the inner disk , they would find similar contacts on the exterior of the outer disk . Cryo-EM analysis of various recombinant vaults containing the cpMVP construct used in this study were unable to identify a TEP1 site for lack of strong difference density [2] . However , as there are thought to be only 1–2 copies of TEP1 per vault half , it may be difficult to assign density to TEP1 in the absence of a higher-resolution structure . These few examples of new insights into vault engineering and vault function demonstrate the potential usefulness of the draft model of the vault shell described in this paper .
The vault construct most successful for crystallography thus far was cpMVP ( 96 copies of 96 . 8 kDa; [2] ) . The N-terminal 12-residue sequence of cpMVP ( MAGCGCPCGCGA ) originated in a metal-binding motif of metallothionein . The rest of the sequence ( 861 residues ) is the same as the rat liver MVP sequence ( GenBank accession code Q62667 GI:47606697 ) . The N-terminal tag was intended for heavy metal binding to help determine phases and thus the structure , but it instead forms disulfide links thought to rigidify the cpMVP vault and improve diffraction . cpMVP vault particles were purified as described elsewhere [5] . Further details are given in Text S2 . Crystals were grown by hanging-drop vapor diffusion . Separate reservoir and precipitant solutions decoupled the initial and destination drop conditions and were prepared as follows . The 1-ml reservoir solutions contained 0 . 64%–0 . 76% polyethylene glycol ( PEG ) 8000 , 3% glycerol , 0 . 05 M Na MOPS , pH 7 , 0 . 044 M MgCl2 , and 0 . 2% n-octyl-β-D-glucopyranoside ( β-OG ) . If a 1-mM dithiothreitol ( DTT ) solution was used instead of water to keep the volumes constant , the reservoir DTT concentration was about 0 . 8 mM . DTT seems to delay crystallization while encouraging growth of the favored C2 crystal form . The glycerol and detergent minimally affected crystallization , but they did facilitate later cryoprotection and reduce surface tension around the crystal . The volume of water ( or 1 mM DTT ) in the reservoir was critical to set the destination vapor pressure; one pipet was calibrated to deliver this volume . The precipitant solutions contained 0 . 27%–0 . 33% PEG 8000 , 1 . 5% glycerol , 0 . 025 M Na MOPS , pH 7 , 0 . 02 M MgCl2 , and 0 . 1% β-OG . The total volumes were completed with water ( or with 1 mM DTT to final concentration 0 . 9 mM ) . The precipitant mixtures were centrifuged at 10 , 000g for 3 min . The hanging drops were made by mixing 1 . 5-μl vault and 3-μl precipitant solutions . The air volume was initially saturated with cyclohexane ( see Text S2 for further details ) . Crystallizations were partially protected from room vibrations by low-cost isolator platforms ( Text S3 ) . Crystals were cryoprotected and annealed by floating microdialysis ( Text S4 and Figure S2 ) . Diffraction data were collected at Advanced Light Source Beamline 8 . 2 . 2 . The x-ray beam was focused at detector position ( Text S5 ) . Initial phases were generated by manually placing half of the cryo-EM vault electron density in the crystal lattice at a 2-fold as directed by the 13 . 68° β angle reported by the molecular replacement rotation function . This is the same as the tilt away from the orthogonal z-axis shown in the self-rotation function ( Figure S3 ) . Automated molecular replacement had been abandoned due to the inaccuracy of the translation function ( see Text S6 and Figure S4 ) . The placement and artefactual thinning operations are shown in Figure S5 , and the packed phasing model is shown in Figure S6 . The positive-only half-vault density map from cryo-EM ( prepared for automated molecular replacement; Text S6 ) was scaled smaller ( scale factor 0 . 96 applied with MAPMAN [30] ) , masked by MAPMASK [31] , and the whole-vault center was translated to ( 0 , 0 , 0 ) with MAPROT [31 , 32] . This simplified density modification ( Text S7 ) . The density was re-masked at its new location , and the density was rotated −13 . 68° around the y-axis ( and thinned , Figure S5 ) with MAPROT . The rotation function α and γ angles both coincided with the vault high-symmetry axis , and were ignored because the cryo-EM electron density varied little around that rotation . Phases were calculated from the density map ( plus symmetry mates ) with SFALL [31] . This initial near-featureless phasing model was almost centrosymmetric [33] . The phase set derived from the initial model was improved by density modification by simultaneous application of NCS averaging , solvent-flattening , and histogram matching , using DM [31 , 34] . The cross-section in Figure 1 shows the relative locations of the crystal and NCS axes . The center of symmetry was broken by application of 48-fold NCS averaging ( see Text S7; [33] ) . The enantiomer was assigned later during model building . The phases from the initial 48-fold average were further improved by iterative “dot model refinement” ( Text S8 ) , applying concentric 24- and 48-fold averaging to phase sets initiated from models of unassigned atoms ( “dots” ) . Electron density features revealed by crystallographic means could be indirectly validated ( see Text S8 ) . The N-terminal disk inside the waist and the 48 holes at the top of the shoulder were independently observed via cryo-EM [2] . The globules of electron density ( Figure 2 ) were spaced as though they represented backbone atoms , separated by side chains . Some of the predicted models ( see below ) and the NMR substructure [14] resembled shapes at their corresponding electron density . In the barrel region , a 3-fold repeat in the shape of the electron density paralleled expectation of sequence repeats ( Figure 5c ) . The accumulated evidence indicated that the electron density was meaningful . The amino acid sequence of MVP has yielded some useful structural expectations . Using fold-prediction and fold-recognition algorithms , we sought models to facilitate the interpretation of the electron density map . To initiate tertiary structure prediction for the first 400 residues of MVP , the sequence was divided at and near predicted domain boundaries . The seven N-terminal MVP repeats as represented in the PFAM protein domain database [35] were: residues 26–87 , 88–141 , 142–194 , 195–247 , 248–305 , 306–355 , and 356–404 . For residues thought to be in the vault shoulder ( approximately residues 404–650 ) , several putative domain segments were created with sizes varying from 40–80 residues . In this region , domain boundary selection was first aided by prediction of loops using PSIPRED [36] . Ab initio models for each putative domain were generated with the HMMSTR/ROSETTA web server [20–22] . The HMMSTR/ROSETTA server divided the input sequence into short segments , searched a database for plausible fragment structures , then attempted to reassemble the fragments into a compact structure model , ignoring the NCS neighbors . The server quickly returned results by using shorter conformational searches with fewer repetitions than were used in the original ROSETTA algorithm [37] , and by performing ab initio tertiary structure predictions on short segments of the chain , which are subsequently combined with a genetic algorithm [21] . The shapes and plausibilities of the ROSETTA models depended on the choices of input residue windows . Thus , we used the simplified web server version of ROSETTA for its speed in testing many residue ranges . The sequence segments chosen to construct the cpMVP model are listed in Table 1 . Both the HMMSTR/ROSETTA server and the 3-D-PSSM fold-recognition server [38] predicted several beta-sheet–rich domains in the N-terminal two-thirds of the MVP . The best 3-D-PSSM fold-recognition matches in this region included the seven-bladed beta propeller fold of Protein Data Bank ( PDB; [18 , 19] ) entry 2BBK , and beta-sheet–rich structures 1BQS and 1NLT . These fold-recognition matches did not fit well in the electron density . However , these calculations suggested that the N-terminal region contains several stacked beta-sheet–rich domains , in agreement with the observation of strong reflection intensities at 10-Å resolution , and in agreement with the NMR substructure [14] . We elaborated on the prior expectation of coiled-coil structure [28] in the 650–800 region of the MVP sequence . Residues 570–600 and 650–825 were predicted to be mostly helical using the PSIPRED secondary structure prediction method . Additionally , the 3-D-PSSM fold-recognition server predicted that these regions match well with long helices , such as those in PDB entries 1D7M , 1CUN , and 1KMI . The gapped alignment with PDB entry 1D7M , for instance , has 30% sequence identity to MVP residues 670–720 and 750–800 . A high probability of helical dimer or trimer in the range of residues 680–750 , was predicted using the MULTICOIL algorithm [39] . The cpMVP model was assembled from 15 domain models , shown as panels in Figure 5 , and stacked in Figure 4 . The origins and residue ranges of the individual models are listed in Table 1 . The model contains 749 of the 873 residues expected for the cpMVP construct . The domain models were manually fit to a 9-Å resolution Fobserved map calculated with enantiomer phases from slow reaveraging of Dot Model 6 ( see Text S8 ) , using XFIT of XtalView [40] . The map was contoured at 1 . 2σ and 2 . 6σ on a 2 . 6-Å grid . The domain models ( backbone and β-carbon atoms ) were manually bent to fit their density features . Segments were shifted to align backbone hydrogen bonds , to allow interdigitation of imagined sidechains , and to alleviate NCS collisions . Comments on specific domains are given in Text S9 . Each ROSETTA-predicted domain chosen for the cpMVP model contained a well-packed core structure , such as beta-sheets and helix , usually with dangling N and C termini . The shapes of the core features of each model were manually placed in electron-density shapes , and were arranged subject to the restraint that the dangling ends could later be manually reconnected to form a single covalent cpMVP chain . The most extreme manual interventions to ROSETTA models were applied to domain 2 ( see Figure 5b and Text S9 ) . Manual intervention at some proline residues is discussed in Text S1 . The vertical stacking of domain models was usually clear from the electron density and from the number of residues available for connections . In the shoulder region of the cpMVP model , boundaries between domains 8–11 are indistinct . The helix at the nominal boundary between domains 9 and 10 ( residues 494–503 , bottom foreground of Figure 5G ) could be flipped left or right , resulting in shifting the top of the cpMVP model left or right relative to the bottom of the model . The helix was flipped to its current location because the flipped structure relieved strain in the backbone geometry , and substantially increased contact area between domains 9 and 10 of the same MVP chain . Once the manually adjusted cpMVP model was complete , its backbone geometry was brought nearer to expectation values by torsion angle energy minimization using CNS [41] , which used a hydrogen-bonding energy term [42] . CNS added side chain atoms . Some automatic rotamer choices were manually altered , and some segments were manually shifted . After each round of manual intervention in a refinement model segment , energy minimization was performed on that segment maintaining covalent connections at symmetry junctions ( see Text S10 ) . Model validation , including a score based on the side chain atoms from CNS , is discussed in Text S1 .
The 9-Å resolution cpMVP dimer model , the structure factors , and the phases used to calculate electron density maps , have been deposited in the Protein Data Bank [18 , 19] ( http://www . rcsb . org/pdb ) with accession code 2QZV . The 96-mer vault nanocapsule ( Figure 6B ) may be reconstructed from the cpMVP dimer using rotation matrices contained in 2QZV , for example with graphics program CHIMERA [43] . The NMR structure of domains 3 and 4 is entry 1Y7X [14] . For convenience , a partially-assembled model is available as Model S1 . We again warn users of this model that its atom positions are approximate . The GenBank ( http://www . ncbi . nlm . nih . gov/Genbank ) accession number for rat liver MVP sequence is Q62667 .
|
Vaults are large barrel-shaped particles found in the cytoplasm in all mammalian cells , which may function in innate immunity . As naturally occurring nanoscale capsules , vaults may be useful objects to engineer as delivery vehicles . In this study , we propose an atomic structure for the thin outer shell of the vault . Using x-ray diffraction and computer modeling , we have inferred a draft atomic model for the major vault protein , which forms the shell-like enclosure of the vault . The shell is made up of 96 identical protein chains , each of 873 amino acid residues , folded into 14 domains . Each chain forms an elongated stave of half the vault , as well as the cap of the barrel-like shell . Our draft atomic model is essentially an atomic-level model for the entire 9 . 3-MDa vault shell , which offers a guide for protein engineering to test vault functions and to exploit vault particles as nanocapsules .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods",
"Supporting",
"Information"
] |
[
"molecular",
"biology"
] |
2007
|
Draft Crystal Structure of the Vault Shell at 9-Å Resolution
|
Cathepsin B ( CatB ) is a cysteine proteolytic enzyme widely expressed in various cells and mainly located in the lysosomes . It contributes to the pathogenesis and development of many diseases . However , the role of CatB in viral myocarditis ( VMC ) has never been elucidated . Here we generated the VMC model by intraperitoneal injection of coxsackievirus B3 ( CVB3 ) into mice . At day 7 and day 28 , we found CatB was significantly activated in hearts from VMC mice . Compared with the wild-type mice receiving equal amount of CVB3 , genetic ablation of CatB ( Ctsb-/- ) significantly improved survival , reduced inflammatory cell infiltration , decreased serum level of cardiac troponin I , and ameliorated cardiac dysfunction , without altering virus titers in hearts . Conversely , genetic deletion of cystatin C ( Cstc-/- ) , which markedly enhanced CatB levels in hearts , distinctly increased the severity of VMC . Furthermore , compared with the control , we found the inflammasome was activated in the hearts of wild-type mice with VMC , which was attenuated in the hearts of Ctsb-/- mice but was further enhanced in Cstc-/- mice . Consistently , the inflammasome-initiated pyroptosis was reduced in Ctsb-/- mice hearts and further increased in Cstc-/- mice . These results suggest that CatB aggravates CVB3-induced VMC probably through activating the inflammasome and promoting pyroptosis . This finding might provide a novel strategy for VMC treatment .
Myocarditis , defined as a nonspecific inflammatory disease of the myocardium , is most commonly caused by cardiotropic virus infection , especially for coxsackieviruses[1 , 2] . The clinical manifestations and severity vary among patients with viral myocarditis ( VMC ) [3] . Although some patients only present mild or even self-limited symptoms , VMC accounts for 8 . 6% to 12% of sudden cardiac deaths in young people due to its resultant acute heart failure or ventricular arrhythmias[4–6] . In addition , about 21% of patients with acute VMC may progress into dilated cardiomyopathy ( DCM ) , which may lead to repeated heart failure and is the major reason for heart transplantation at present[7] . However , except for supportive care , no other effective and specific therapies are proven effective for clinical use currently[8] . Cathepsin B ( CatB ) is an intracellular cysteine protease , mainly localized in the lysosome[9] . By its involvement in many pathophysiologic processes including apoptosis , autophagy , extracellular matrix turnover , inflammation and immune responses , CatB plays an important role in many diseases , such as cancer , rheumatoid arthritis , cardiovascular diseases , etc[9–11] . It has also been demonstrated that CatB is involved in viral infectious diseases because of its relations with virus entry , replication as well as virus-mediated cell apoptosis and immune responses[12–14] . Specifically , a recent study showed that CatB was significantly upregulated in muscle tissues of both patients with polymyositis and Guinea pigs with Coxsackievirus B1-induced polymyositis , and administration of the CatB inhibitor attenuated inflammation and apoptosis in muscle tissues of Guinea pigs with polymyositis[14] . Considering the similarity between the pathophysiology of polymyositis and myocarditis , with an important inflammatory part in a context of viral infection in both cases , cumulated with the previous demonstration of the role of CatB in the former , we hypothesized that CatB might also participate in the pathogenesis of Coxsackievirus B3 ( CVB3 ) -induced myocarditis . The inflammasome is an intracellular multiprotein complex consisting of three components: a cytosolic pattern recognition receptor , the adaptor protein ASC ( apoptosis-related speck-like protein containing a caspase recruitment domain ) and the cysteine protease procaspase-1[15] . The nucleotide-binding oligomerization domain ( NOD ) -like receptor family , pyrin domain-containing protein 3 ( NLRP3 ) is the most studied pattern recognition receptor[15] . Upon activation , NLRP3 recruits ASC , which further recruits procaspase-1 . Activation of procaspase-1 can cleave pro-interleukin ( IL ) -1β and pro-IL-18 into mature IL-1β and IL-18 , which are then released into circulation to amply the inflammatory responses . In addition , activated caspase-1 can also initiate a specific form of programmed cell death called pyroptosis[15] . Different from apoptosis , pyroptosis is a death pathway accompanied by release of a number of inflammatory cytokines , mainly including IL-1β and IL-18[16] . Specifically , the activated capase-1 cleaves gasdermin D , releasing its N-terminal domain , which oligomerizes in the membranes to form large pores causing subsequent membrane rupture and cell death[17] . The inflammasome has been implicated in many inflammation-related diseases , such as myocardial infarction and ischemia-reperfusion injury[18 , 19] . Recently , formation of the inflammasome has also been found in VMC both in patients and mice[20 , 21] . Besides , blockade of inflammasome activation by treating CVB3-inoculated mice with caspase-1 inhibitor Ac-YVAD-CHO significantly attenuated the severity of VMC[21] . According to previous data , CatB released from the lysosome is considered one of the upstream activators of the NLRP3 inflammasome[22] . Extracellular stimuli , including viral infection , could damage the lysosomes and release the lysosomal contents , including CatB , into the cytosol[23] . These findings suggest that CatB may exaggerate VMC via regulating the activation of the inflammasome . In this study , we built the murine VMC model by intraperitoneal injection of CVB3 , and investigated whether and how CatB contributed to VMC development , using genetically CatB knockout ( Ctsb-/- ) mice , as well as the mice deficient in cystatin C , which is an endogenous inhibitor of papain-like cysteine cathespins , particularly potent for CatB[24] .
To investigate the role of CatB in VMC , we first generated the VMC model by intraperitoneal injection of 1000 TCID50 of CVB3 into 4-week-old male C57BL/6 mice . Transthoracic echocardiography was conducted , and mice were sacrificed on day 7 and day 28 postinfection ( pi ) . At both time points , the hematoxylin and eosin ( HE ) staining showed apparent inflammatory infiltrates , and the echocardiography exhibited impaired cardiac function as evidenced by decreased ejection fraction ( EF ) and fractional shortening ( FS ) in the model group compared with the control ( Fig 1 , S1 and S2 Tables ) . These results suggest the successful establishment of CVB3-induced myocarditis . Next , we detected the expression and activity of CatB in the mice hearts . As shown in Fig 2 , the expression of activated CatB was significantly increased in CVB3-infected mice on both day 7 and day 28 pi . Besides , cardiac CatB activity was also significantly enhanced 7 days after virus inoculation ( S1B Fig ) . This implies that CatB is probably involved in the pathogenesis of VMC . To further explore the role of CatB in VMC , we used Ctsb-/- mice lacking CatB and cystatin C deficient ( Cstc-/- ) mice which overexpress cathepsins to directly investigate the impact of CatB on VMC . The deletion and overexpression of CatB in Ctsb-/- and Cstc-/- mice were verified by western blot ( Fig 3A ) and CatB enzymatic activity assay ( S1 Fig ) . Then , we compared cardiac structure , cardiac function and survival among the Ctsb-/- , Cstc-/- and wildtype ( WT ) mice , and found no significant differences in their baseline conditions ( S2 Fig ) . Compared with 57 . 14% in the WT+CVB3 group , the survival rate up to 28 days was significantly increased to 91 . 67% in the Ctsb-/-+CVB3 group but dramatically decreased to 18 . 18% in the Cstc-/-+CVB3 group ( Fig 3B ) . Moreover , Ctsb-/- mice showed less inflammatory cell infiltration and lower pathologic scores whereas the Cstc-/- mice exhibited the contrary results on day 7 pi , compared with the WT+CVB3 group ( Fig 4A and 4B ) . Serum cardiac troponin I ( cTnI ) level is a sensitive indicator of myocardial injury . As expected , on day 7 pi , the serum cTnI level was lower in Ctsb-/- group but higher in Cstc-/- group than that in WT+CVB3 group ( Fig 4C ) . We further assayed the effect of CatB on CVB3-mediated cardiac dysfunction . Deficiency of CatB significantly improved EF and FS compared with the WT+CVB3 group . However , accompanied with overexpressed CatB , Cstc-/- mice had reduced cardiac contractility compared with the WT+CVB3 group ( Fig 4D–4F , S3 Table ) . Together , these data indicate that CatB promotes VMC . It has been reported that CatB promotes entry and replication of several viruses[12 , 25] . Here we tested if CatB affects CVB3 titers in hearts . As depicted in S3 Fig , the cardiac virus titers exhibited no significant difference among the WT+CVB3 , Ctsb-/-+CVB3 and Cstc-/-+CVB3 groups on both day 7 and day 28 pi . These data imply that CatB promotes CVB3-induced VMC independently of altering viral replication . Recent studies have demonstrated that the NLRP3 inflammasome was activated in the myocardium of both patients and mice with VMC , and inhibiting caspase-1 activity significantly alleviated VMC[20 , 21] . As CatB releasing from damaged lysosomes could activate the NLRP3 inflammasome[26] , we hypothesized CatB might aggravate VMC through the NLRP3 inflammasome activation . The protein levels of NLRP3 , ASC , caspase-1 p20 , and serum IL-1β levels had no difference at baseline of the uninfected knockout mice and WT mice ( S4A and S4C Fig ) . Because only two mice of the Cstc-/- group survived till day 28 pi , we examined the levels of the components of the inflammasome in the mice hearts harvested on day 7 . The levels of these components of the NLRP3 inflammasome were markedly increased in the WT+CVB3 group compared with the WT group , suggesting the activation of the NLRP3 inflammasome in VMC . Moreover , compared with the WT+CVB3 group , their levels were significantly decreased in the Ctsb-/- group , but apparently enhanced in the Cstc-/- group ( Fig 5A and 5C ) . Pyroptosis is a newly discovered form of programmed cell death dependent on the activation of the inflammatory caspases , including caspase-1[27] . We found the enhanced caspase-1 activity in WT+CVB3 group was significantly decreased in the Ctsb-/- group but further increased in the Cstc-/- group ( Fig 5B ) . In addition , the caspase-1-induced pyroptosis was detected by the TUNEL staining , which showed less cell death in the Ctsb-/-+CVB3 group but more in the Cstc-/-+CVB3 group , compared with the WT+CVB3 group ( Fig 6 ) . The levels of caspase-1 activity and cell death detected by TUNEL showed no difference among the uninfected WT , Ctsb-/- and Cstc-/- mice ( S4B and S5 Figs ) .
The present study determined the role of CatB in CVB3-induced myocarditis . We found that CatB was activated in the hearts of CVB3-infected mice both in the acute and chronic phases , accompanied by the activation of the inflammasome . CatB deficiency markedly suppressed the activation of the inflammasome , reduced caspase-1-induced pyroptosis , attenuated cardiac inflammation , alleviated cardiomyocyte injury , prevented cardiac dysfunction and improved survival . In contrast , ablation of cystatin C significantly increased the expression of CatB , promoted the activation of the inflammasome , enhanced myocardial pyroptosis , and increased the severity of VMC . Based on these results , we concluded that CatB aggravated CVB3-induced myocarditis probably by activating the inflammasome and promoting pyroptosis . The lysosome is a ubiquitous intracellular organelle essential for cell homeostasis . It participates in degradation of macromolecules , endocytosis , autophagy , lysosomal exocytosis , and cell death signaling[28] . These functions of the lysosome largely depend on the hydrolases it contains[28] . CatB is one of the most important lysosomal cysteine proteases with highest activity in the acidic environment[29] . Besides the environmental pH value , CatB activity is mainly influenced by its inhibitors . Cystatin C is the most important endogenous inhibitor of CatB , both inhibiting its activity and synthesis[30] . Extensive studies have documented the crucial involvement of CatB in many diseases , including viral infection[31–33] . For example , Kartik Chandran et al . found that proteolysis of virus glycoprotein 1 by CatB was necessary for the entry of Ebola virus into the host cell[12] . In another study , expression of CatB was found dramatically increased in Dengue virus ( DENV ) -infected HepG2 cells , and both treating with CatB inhibitor and RNAi knockdown of CatB reduced the level of cleaved caspase-3 , suggesting a role of CatB in DENV-induced apoptosis[34] . Moreover , CatB level was also increased in both muscle and lung tissues of Guinea pigs with CVB1-induced polymyositis , and inhibition of CatB with CA-074Me exerted a protective effect by alleviating inflammation and apoptosis[14 , 35] . Our research demonstrates that myocardial activated CatB levels are enhanced both in the acute and chronic phases of CVB3-induced VMC . Absence of CatB significantly attenuates , while overexpression of CatB by ablation of cystatin C exacerbates CVB3-induced VMC . According to previous data , the possible mechanisms underlying CatB-mediated effects include: degradation of extracellular matrix ( ECM ) , induction of cell death , activation of the inflammasome , participation in autophagy etc[22 , 34 , 36–39] . Many pathogens , including viruses , can activate the inflammasome and induce pyroptosis[40] . Our study focused on the effect of CatB on the inflammasome activation during CVB3 infection . Consistent with a previous study[21] , our data showed that the inflammasome was activated and the inflammasome-induced pyroptosis was increased in the hearts of CVB3-inoculated mice . Furthermore , this phenomenon was blocked in Ctsb-/- mice but more apparent in Cstc-/- mice , suggesting that CatB promotes the activation of the inflammasome and pyroptosis in VMC . It has been verified that inhibiting inflammasome activation by treating mice with caspase-1 inhibitor significantly alleviated CVB3-induced myocarditis[21] . A combination of this fact and our results confirmed our hypothesis that CatB exaggerated VMC partially by regulating the activation of the inflammasome and its resultant pyroptosis . Cardiac viral load is one of the key factors that determine the severity and progress of VMC . The effect of CatB on virus replication is rather complicated . One study proved that , compared with the WT mice , Ctsb-/- mice showed no difference in virus replication and time to death when challenged with lethal mouse-adapted Zaire ebolavirus[41] . In our CVB3-induced VMC model , we demonstrate that deletion of CatB and cystatin C had no impact on cardiac virus titers of mice with VMC on both day 7 and day 28 pi . This suggests that the influence of CatB on VMC was independent of affecting CVB3 replication . Our results reveal the pathogenic role of CatB in CVB3-induced myocarditis , suggesting that inhibition of CatB could represent a promising treatment for VMC . In fact , CatB has been a hopeful target for pharmacological therapy of several kinds of diseases[42 , 43] . Treatment with the CatB selective inhibitor CA074 greatly suppressed bone metastasis of breast cancer in a 4T1 . 2 murine model[43] . The broad-spectrum cathepsin inhibitor E64d and the specific CatB inhibitor CA-074Me were both proved capable of reducing brain β-amyloid peptides and improving memory in the murine Alzheimer’s disease model[42] . The therapeutic effect of these inhibitors on VMC could also be investigated , and this may provide a new approach to treating VMC . However , there are still a few limitations of our study . First , cystatin C is the endogenous inhibitor of papain-like cysteine cathepsins , but not specific for CatB . Thus , the aggravation of the disease severity in infected cystatin C knockout mice is a comprehensive result of overexpression of several kinds of cathepsins , but not solely due to the increased levels of CatB . Second , deficiency of CatB and cystatin C had no effect on cardiac CVB3 replication 7 and 28 days after virus infection , and we only determined the cardiac function within 28 days pi in our study . It is still necessary to detect cardiac virus titers and cardiac function in a longer time . Third , as far as we know , in addition to caspase-1 activation , there is no any other specific strategy to detect pyroptosis . We used TUNEL staining , besides caspase-1 activation , to demonstrate pyroptosis which is in accordance with other reports[44 , 45] , but the specificity may be limited . To sum up , our study demonstrates that CatB aggravates CVB3-induced myocarditis and the one pathway , induction of the inflammasome and pyroptosis , has been shown to be a result of cathepsin B activity in this murine model of VMC . CatB may be a potential therapeutic target against VMC .
All animal experiments were approved by the ethical board of the Animal Care and Use Committee of Zhejiang University ( zju201308-1-01-085 ) , and were performed according to Guide for the Care and Use of Laboratory Animals of the U . S . National Institutes of Health . All efforts were made to minimize the number of animals and their suffering . The male C57BL/6 mice were purchased from Shanghai Slac Laboratory Animal Co . Ltd ( Shanghai , China ) . The breeding pairs of both Ctsb-/- mice and Cstc-/- mice in C57BL/6J background were provided by Professor Guo-ping Shi ( Harvard Medical School , MA , USA ) . The Ctsb-/- mice were generated in the laboratory of Professor Shi[46] , and the Cstc-/- mice were bought from the Jackson Laboratories[47] . Mice at 4 weeks of age were used in all experiments . HeLa cells were purchased from American Type Culture Collection ( ATCC ) and cultured in Dulbecco’s modified eagle medium ( DMEM ) with high glucose ( Shanghai Pufei Biotech Co . , Ltd , China ) . The CVB3 ( 3m strain , a mutant of Nancy strain ) was purchased from Wuhan Institute of Virology , Chinese Academy of Sciences , and preserved in Institute of Hypertension and Department of Internal Medicine , Tongji Hospital , Tongji medical college , Huazhong University of Science and Technology , China and stored at -80°C . The virus was amplified by infecting HeLa cells , subsequent freeze-thaw cycles and collection of the supernatants containing viruses . The virus titer was determined by a 50% tissue culture infectious dose ( TCID50 ) assay of HeLa cell monolayer as previously described[48] . To investigate the involvement of CatB in VMC , we randomly assigned the 4-week-old male C57BL/6 mice into two groups: control group ( n = 10 ) and CVB3 group ( n = 10 ) . Then , to further evaluate the effect of CatB on the severity of VMC , mice were divided into four groups: WT group ( n = 10 , 4-week-old male C57BL/6 mice ) , WT+CVB3 group ( n = 14 , 4-week-old male C57BL/6 mice ) , Ctsb-/-+CVB3 group ( n = 12 , 4-week-old male Ctsb-/- mice ) , Cstc-/-+CVB3 group ( n = 11 , 4-week-old male Cstc-/- mice ) . Each mouse of the CVB3 groups was intraperitoneally injected with 1000 TCID50 of CVB3 to induce VMC , while the other groups received an equal amount of DMEM . Transthoracic echocardiography was performed using the Vevo 2100 ultrasound imaging system ( VisualSonics , Toronto , Canada ) after anesthetization by isoflurane inhalation on day 7 , day 14 and day 28 following virus injection . The echocardiographic data such as left ventricular ejection fraction ( LVEF ) and left ventricular fractional shortening ( LVFS ) were then measured blindly according to the operator’s manual . Mice were sacrificed and their hearts were harvested on day 7 and day 28 after CVB3 infection . The hearts were fixed in 10% phosphate-buffered formalin , embedded in paraffin , sectioned and then stained with hematoxylin and eosin . As previously described , the severity of myocarditis was assessed using a 0–4 scale , in which 0 = no inflammation; 1 = one to five distinct mononuclear inflammatory foci with involvement of 5% or less of the cross-sectional area; 2 = more than five distinct mononuclear inflammatory foci , or involvement of between 5% and 20% of the cross-sectional area; 3 = diffuse mononuclear inflammation involving over 20% of the area , without necrosis; 4 = diffuse inflammation with necrosis[49] . Mouse heart tissues were aseptically obtained , weighed and homogenized in DMEM . After repeated freeze-thaw cycles , the samples were centrifuged at 300xg for 10 minutes , and the supernatants were collected . Then , the supernatants were used to determine the virus titers as previously described[48] . Levels of serum cTnI and IL-1β were determined using the commercial enzyme-linked immunosorbent assay ( ELISA ) kits ( Cloud-Clone Corporation , Houston , TX ) respectively , following the manufacturer’s instruction manuals . The frozen heart tissues were homogenized and then lysed using RIPA lysis buffer ( Beyotime Biotechnology , China ) added with the protease inhibitor ( Thermo Fisher Scientific , MA ) . Protein samples were separated by SDS-PAGE electrophoresis and transferred to polyvinylidene difluoride ( PVDF ) membranes . The membranes were incubated with primary antibodies overnight and then with secondary antibodies for another hour . Finally , the bands were visualized using ECL solution ( Merk Millipore , MA ) . The following antibodies were used: CatB antibody ( 1:200 dilution ) from Santa Cruz , NLRP3 antibody ( 1:1000 dilution ) from Abcam , ASC antibody ( 1:1000 dilution ) from Millipore , caspase-1 antibody ( 1:1000 dilution ) from Abcam , β-actin antibody ( 1:3000 dilution ) from Santa Cruz , Horseradish peroxidase ( HRP ) - conjugated anti-rabbit and anti-mouse IgG ( 1:3000 ) from Santa Cruz . Densitometric quantification of the bands were performed using ImagePro Plus ( Media Cybernetics , Warrendale , PA ) . The commercial Caspase-1 Activity Assay Kit ( Beyotime Biotechnology , China ) was applied to detect the caspase-1 activity in heart tissues . The tissue lysates were centrifuged at 16 , 000–20 , 000 g for 15 minutes , and the supernatants were collected . Meanwhile , an appropriate amount of the supernatants from each sample were incubated with the substrate Ac-YVAD-pNA in a 96-well plate for 60–120 minutes at 37°C . The absorbance values of the products pNA at 405 nm ( OD405 ) were measured by using the microplate reader ( Bio-Rad , Hercules , CA ) . Then , the activities of caspase-1 were calculated based on the above results and were finally shown as fold changes compared with control . Cardiac CatB activity was determined by using a commercial Cathepsin B Activity Assay Kit ( Fluorometric ) ( Abcam , England ) according to the instructions . The appropriate amount of heart tissues lysates was incubated with CatB substrate in the 96-well black plates with clear bottoms for 60–120 minutes at 37°C protected from light . The fluorometric absorbance was then measured at the Ex/Em of 400/505nm by using the microplate reader ( Bio-Rad , Hercules , CA ) . The results were shown as relative fluorescence units ( RFU ) per microgram of protein . Myocardial apoptosis was detected using the terminal deoxynucleotide transferase dUTP nick end labeling ( TUNEL ) kit ( Roche Life Science , Switzerland ) . The frozen sections were fixed in 4% paraformaldehyde and incubated with 0 . 2% TritonX-100 solution to break the cell membranes . The sections were then incubated with a mixture solution of enzyme solution and label solution for 1–1 . 5 hours shielded from light . The number of TUNEL-positive cells was counted under the fluorescence microscope . All values are shown as mean±SEM . When determining the statistical differences , unpaired student’s test was used between two groups , whereas ANOVA followed by Bonferroni multiple comparison test was applied among three or more groups . Kaplan-Meier curve was used to analyze the survival rates . P<0 . 05 was considered statistically significant . All the statistical analyses were performed using the GraphPad Prism ( version 6 . 0 ) .
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Severe VMC could lead to sudden cardiac death especially in youths , and is also the most common cause of secondary dilated cardiomyopathy . However , we still lack effective and specific clinical treatments currently . Therefore , further exploration of the pathogenesis and new therapeutic targets are urgently needed . Our results implied that CatB , a cysteine protease mainly located in the lysosome , is activated in the hearts of mice with VMC induced by intraperitoneal injection of CVB3 . Genetic deletion of CatB significantly improves survival , attenuates cardiac inflammation , decreases serum cardiac troponin I levels and alleviates cardiac dysfunction , without altering virus titers in hearts . However , ablation of its main endogenous inhibitor , cystatin C , distinctly exaggerates the disease severity . Mechanistically , we found that CatB influences VMC probably by activating the NLRP3 inflammasome and promoting caspase-1-induced pyroptosis . This may provide a potential new therapeutic strategy for VMC .
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2018
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Cathepsin B aggravates coxsackievirus B3-induced myocarditis through activating the inflammasome and promoting pyroptosis
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This paper presents a data analysis framework to uncover relationships between health conditions , age and sex for a large population of patients . We study a massive heterogeneous sample of 1 . 7 million patients in Brazil , containing 47 million of health records with detailed medical conditions for visits to medical facilities for a period of 17 months . The findings suggest that medical conditions can be grouped into clusters that share very distinctive densities in the ages of the patients . For each cluster , we further present the ICD-10 chapters within it . Finally , we relate the findings to comorbidity networks , uncovering the relation of the discovered clusters of age densities to comorbidity networks literature .
Studies of groups of diseases occurring together , or disease comorbidities , have traditionally focused on studies of small groups of diseases using techniques of hypothesis-testing [1–6] . The repeated existence of particular comorbidities is important to diagnoses and better index diseases [7 , 8] . Databases of electronic medical records contain phenotypic information for humans—namely , patient clinical histories . A novel method to analyze health records is to built the human phenotypic disease network , where nodes represent the diseases and edges indicate comorbidity relations [9] . More recent studies analyze databases on electronic health records to uncover systematic associations in the complete set of known diseases [6 , 10–12] . In this context , several methods of information sciences can be used to uncover patterns in electronic patient records . The main interest of these studies is to discover correlations between diseases that can help in prevention and can also inform systems biology frameworks [13] . More recently , computational methods are being used to reduce the costs of healthcare by helping to identify outliers in medical records [14] . Up to date , most of the samples of electronic patient records studied in the literature have used a narrow set of the general population of patients . For example , Hidalgo et al . covered 3 years of medical care claims of patients who were 65 years or older , this biased the information towards population of the elderly . Later , Roque et al . generated fine grained patient stratification and disease co-occurrence statistics of patients from the Sankt Hans Hospital , which is the largest Danish psychiatric facility [15] . Their results focus into phenotypes associated with mental and behavioral disorders or the chapter V of the ICD-10 standard classification catalog . Datasets with more complete sample of the population have become more recently available . Electronic records with time spans of decades allowed , for the first time , to uncover patterns centered on the number of key diagnoses that can detect diseases earlier in a patient’s life [16] . While Chmiel et al . [17] analyzed two years of medical claims of the entire population in Austria . They measured how the comorbidity network change its structure with the age of the patients . This information was used to build a diffusion model that explains a large percentage of the variance of all the disease incidents in a population . In that case , the comorbidity networks were built while pre-defining the age intervals of the patients analyzed . In this work , we present a clustering method by identifying the similarities in the age densities of the actual phenotypic records . We find groups of medical conditions that occur in the unsupervised age groups emerging from the data . These groups are in turn associated with a small set of chapters of the ICD-10 standard classification catalog . The wisdom of doctors when it comes to assessing susceptibility to diseases have been influenced by the years of practice and observation of many cases on daily basis . Doctors’ knowledge of the susceptibility to diseases at different ages/sexes serves as an essential prior to perform diagnostics of incoming patients . Similar symptoms for patients might lead to different diagnosis depending on the age and sex of the patient , a patient who is 70 years old is much more likely to suffer a heart attack than a 10 year-old even if both patients are suffering the symptom of chest pain . We show here that this common knowledge can be inferred from the data . Besides the symptoms a patient is having , the age and sex can aid the diagnostic process . We present a framework that automatically uncovers the relationship between health conditions and the age/sex of a patient . To that end , we group the health conditions based on their similarities in population age densities . Then , we construct the comorbidity graph in the same way found in the literature [9 , 17] to investigate the relationship of comorbidity coefficient values to the discovered clusters of conditions .
We further analyze the age densities of ICD-10 codes in the data to segment ICD-10 codes into groups of conditions with similar age densities . As a robustness measure , we consider the analysis by excluding all codes of chapters XVIII-XXII . The excluded chapters include symptoms ( e . g . R codes ) , procedural details ( such as complications or adverse drug effects ) and also personal factors ( general examinations and such ) . We represent the age distribution as a vector of 100 elements , each element has the probability of a patient of the corresponding age within the population of patients having the code . This is defined as probability p ( age|patient ∈ c ) where age is the age of the patients , c is a disease code and patient ∈ c is the set of patients that had a visit labeled as c . We cluster the densities for each ICD-10 code based on the vector representation of the age density p ( age|patient ∈ c ) . We use Hierarchical Agglomerative Clustering ( HAC ) to group the codes into clusters . The method is further discussed in the material and methods section . The age distribution of the codes clusters into six main groups as shown in Fig 4 . Clusters A and B show two clusters of codes having higher density towards the lower spectrum of ages . Cluster C shows a group of codes that have age densities concentrated in the ages 20 to 40 . Cluster D has diseases that are almost uniformly distributed across the ages . Cluster E has codes with densities concentrating in the range of ages over 60 and cluster F has codes with age densities concentrating over 70 . The kernel density estimation of the probability density of the clusters is included in ( S1 Fig ) . Fig 5 illustrates a few examples of the high prevalent ICD-10 codes from the clusters discovered in the data . For each cluster , Fig 5 shows the clustering dendrogram with a depth of six , branches in the dendrogram with a depth higher than six are represented by the disease that is most common in their respective branch . The branches are labeled by their clusters from A to F . Within cluster A , J21 acute bronchiolitis and H65 otitis media nonsuppurative were observed in 0 . 4 and 1 . 2 percents of the population respectively , both have a concentration towards the lower ages as shown previously . Cluster B has J06 acute infections of the upper airways with 8 percents of the population of patients . Furthermore , it has A09 diarrhea and J03 acute tonsillitis each with around 5 . 9 percents respectively . The noticeably increase of the percentage of patients is due to the population age distribution shown in Fig 1 . Cluster C with O82 Cesarean delivery has around 0 . 8 percents of the population of patients , the cluster is consistent with the defined age range between 20 and 40 . Cluster D has H52 disorders of refraction and accommodation with 10 . 6 percents and J01 acute sinusitis with 6 . 7 percents of the population of patients . As expected , as the clusters have more density around the peak of the age distribution of the population , the number of patients per code in the clusters becomes higher . Cluster E with age density towards the elderly has M54 back pain with 10 . 8 percents and M25 other joint disorders with 4 . 7 percents as the most common . Cluster F with age density in the oldest group has I10 essential hypertension ( primary ) with 10 . 4 percents and N39 other disorders of the urinary tract with around 3 . 5 percents . Pneumonia is third in around 1 . 8 percents . Fig 6 shows the decomposition of the clusters in terms of sex and age distribution of each cluster , which has the expected results . Further , we show the probability of association between clusters and the ICD-10 chapters agreed by the World Health Organization [21] , we use the Fisher exact test to measure the association between a chapter of codes to our identified clusters . Clusters have increasing mean age except for cluster C where the age range concentrated around 34 . Cluster C is dominated by female patients . This is explained by the high probability of association with ICD-10 codes in chapter XV pertaining to pregnancy and childbirth and postpartum . Interestingly , from A to D each cluster has their own signature of few associated chapters , while E and F are associated with more chapters proper of aging . This section sheds light on the age related characteristics of the edges in comorbidity networks [9 , 17] . We first construct the comorbidity network through the measure of relative risk between conditions . Further details about the measure of relative risk are included in the materials and methods . Fig 7 shows a sample of the comorbidity network . In the figure , we only show the edges with highest two thousand relative risk values in the quantified comorbidities . The figure is splitted into two parts A and B . Part A shows the intra-cluster edges and part B shows the inter-cluster edges . The sample selection of edges and nodes display are done for visualization purposes . To relate the clusters of diseases reported earlier to the study of comorbidity networks , we study the distribution of relative risk for inter-cluster versus intra-cluster comorbidities . Fig 8 shows the distributions of the relative risk of inter-cluster versus intra-cluster comorbidities . For each cluster in the data , we quantify the distribution of the relative risk of intra-cluster comorbidities ( in red ) and plot it against the distribution of the relative risk of inter-cluster comorbidities ( in gray ) . We find a clear variation in the divergence between the density of relative risk for inter-cluster comorbidities to the intra-cluster ones . The closer the distribution of age for patients in a cluster to a uniform distribution , the less the divergence in relative risk between inter and intra cluster edges . The divergence is highest in clusters A , C and F . They belong to the clusters that identify infants , women in reproductive age and the elderly . It is followed by clusters E and B . With E grouping age density towards the elderly with M54 back pain patients and M25 other joint disorders , while B groups conditions with concentration in teenage years and early adulthood . Cluster D is the closest in age distribution to a uniform , and has the minimal divergence to the distribution of inter-cluster comorbidity density , which has patients in H52 disorders of refraction and accommodation and J01 acute sinusitis .
This paper presents an approach towards investigating groups of diseases based on their relation to age and sex using the records of medical visits from a diverse population . We show that besides the symptoms , age and sex can rank the susceptibility to conditions in a diagnostic process . Using Hierarchical Agglomerative Clustering , we uncover 6 significant groups of medical conditions that present strong similarities on the age density of the patients . Each group of these medical conditions has meaningful associations with few of the 22 standard chapters used to categorize diseases . To find these associations we use the Fisher exact test . We relate the found groups of conditions to the study of comorbidity networks . Pairs of conditions tend to have higher relative risk with varying magnitudes when conditions are in the same group ( intra-cluster conditions ) compared to conditions that are not in the same group ( inter-cluster conditions ) . This in a sense means that the correlations of conditions in terms of sex and gender partially explain the higher relative risk values discovered in comorbidity networks [9 , 17] . Our findings build prior knowledge related to age and sex for automated diagnostics in a Bayesian setting to predict the condition of a patient given their symptoms . The code and data of the study are available for access at http://www . github . com/fha/brazil_health_study .
This paper studies a population of 1 . 7 million patients in Brazil , containing 47 million of health records with detailed medical conditions for visits to medical facilities for a period of 17 months . The data were analyzed anonymously for the privacy of patients’ data . To uncover common patterns of the age distribution of ICD-10 codes , we used a Hierarchical Agglomerative Clustering ( HAC ) approach to group the codes based on the similarities of age distributions . Each code is represented by a vector v of length 100 where each cell represents p ( age = i|patients ∈ c ) where patients ∈ c is the set of patients with the condition on their records . HAC cluster vectors , where each vector is a representation of the probability mass function of a code in the data . The vector representation of the probability mass function of the ages of a ICD-10 code is as follows: p ( a g e | p a t i e n t ∈ c o d e ) = [ p 1 , p 1 , . . . . , p 100 ] ( 1 ) Where pi = p ( age = i|patient ∈ code ) for a given code . At initialization , HAC assigns each vector object to a cluster , and sequentially merging them into clusters until all codes form one cluster . For measuring the distance d between two vector representations of age density , we use euclidean distance . The Ward distance criterion of clusters is dependent on the within cluster distances and the across clusters distances . Ward algorithm is conservative when merging clusters , thus it tends to find very compact clusters [22] . HAC provides a hierarchy structure of the clustered codes as illustrated in Fig 4 . To determine the number of clusters k that best divide the data , we calculate the total within-cluster distances for k from 1 to 20 . The total of distances drops as k increases until it does not decrease significantly . We select k that corresponds to the point where the total distances stops decreasing significantly . This method is known as the elbow curve method . To quantify the comorbidity between conditions , we employ a similar measure to what is used in the literature [9 , 17] . We used the relative risk measure to quantify the comorbidity between conditions in the dataset . The formula for quantifying the relative risk between two conditions is given by: R R i j = C i j N P i P j ( 2 ) Where Cij is the number of patients having both i and j diseases , N is the total number of patients in the data . Pi is the prevalence of condition i and Pj is the prevalence of condition j .
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Age and sex of a patient can be directly related to susceptibilities to certain medical conditions . We present a method to generate clusters of human phenotype , based on the age of the population . This method helps extract knowledge on age and sex from the data . The age and sex correlations with disease conditions can help in a task of predicting the susceptibility of incoming patients to conditions .
|
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"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
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2018
|
Age density patterns in patients medical conditions: A clustering approach
|
The precise anatomical location of gene expression is an essential component of the study of gene function . For most model organisms this task is usually undertaken via visual inspection of gene expression images by interested researchers . Computational analysis of gene expression has been developed in several model organisms , notably in Drosophila which exhibits a uniform shape and outline in the early stages of development . Here we address the challenge of computational analysis of gene expression in Xenopus , where the range of developmental stages of interest encompasses a wide range of embryo size and shape . Embryos may have different orientation across images , and , in addition , embryos have a pigmented epidermis that can mask or confuse underlying gene expression . Here we report the development of a set of computational tools capable of processing large image sets with variable characteristics . These tools efficiently separate the Xenopus embryo from the background , separately identify both histochemically stained and naturally pigmented regions within the embryo , and can sort images from the same gene and developmental stage according to similarity of gene expression patterns without information about relative orientation . We tested these methods on a large , but highly redundant , collection of 33 , 289 in situ hybridization images , allowing us to select representative images of expression patterns at different embryo orientations . This has allowed us to put a much smaller subset of these images into the public domain in an effective manner . The ‘isimage’ module and the scripts developed are implemented in Python and freely available on https://pypi . python . org/pypi/isimage/ .
A significant challenge for current bioinformatics is the computational analysis of large data sets . Recent developments in sequencing technologies have allowed , for example , the investigation of the time course of gene expression in early development of Xenopus tropicalis at high time resolution [1 , 2] . For a robust understanding of gene expression , the precise anatomical or cellular location of expression is as important as the timing of expression , yet this presents significant challenges for computational analysis . The most advanced work has been done in Drosophila , with the analysis of the time evolution of the spatial pattern of gene expression revealing genes with co-localised expression[3 , 4 , 5 , 6] . The spatial distribution of RNA within an embryo or tissue is typically obtained by in situ hybridisation ( WISH ) of a probe sequence to the endogenous RNA under study or by protein immunofluoresence , followed by photographic imaging of the required stages and views or sections . Preparation of reagents and optimisation of conditions for a specific protein/gene target may take time , but once done it is straightforward to generate images covering ( for example ) many different developmental stages . For studies on the localisation of small numbers of genes , analysis by inspection of the resultant images is likely to be feasible and may provide sufficient descriptive data to answer the biological question at hand . In larger scale screens the number of generated images can grow rapidly to tens of thousands [3] or more [7] , and at this level will either require computational analysis or significant commitment by members of the respective model organism community to manually annotate the images; for example with zebrafish [8] , Drosophila [6] or Xenopus [9 , 10] . However , although manual annotation is generally of high quality , it is slow and the required effort is not easily replicated . Computational analysis is clearly preferred for large numbers of images , although this is not a straightforward task , and may require significant investment of time and expertise to develop a suitable system . The goals of computational analysis are easily stated: to recognize the relevant physical anatomy of the organism in the image , locate the regions which show gene expression , and either label these regions with suitable anatomical terms or transfer them to a model coordinate system within which the expression patterns may be analysed and/or compared . These goals are usually achieved by two distinct processes described as segmentation ( recognising compartmentalisation in the image ) and registration ( fitting the embryo shape in the image to a model ) , as well as recognising which parts of the segmented image correspond to gene expression . Image analysis in Xenopus has several specific challenges: the embryos are not normally fully transparent; embryos may display distinct pigmented regions; in embryos that are cleared to make them transparent the outline of the embryo may merge into the image background; and experimental data frequently cover a wide range of development stages and concomitant variety of embryo shapes and sizes . In addition , the earlier development stages are quasi-spherical , and , unlike fly embryos , may present some difficulty in determining the axial orientation within the image . To date there are no published methods for computational image analysis developed for Xenopus . Here we report a first suite of tools developed for computational analysis of Xenopus in situ images . These tools are capable of cleanly separating the embryo from the image background over a wide range of developmental stages without requiring the background to be either uniform or any specific colour; in situ hybridisation stain and natural pigment are detected independently and can be marked up accordingly; and analysed images at the early quasi-spherical stages can be compared with each other to identify groups of images photographed at the same axial orientation . Application of this solution of the segmentation problem and partial solution of the registration problem has enabled us to analyse a large and highly redundant image collection , selecting a usefully condensed and representative set for public dissemination . Although it remains to provide the ability to register the images in a model coordinate system , we have laid some useful ground work for future progress . The reduced image set may also now be considered for manual image registration , expression pattern extraction and annotation in existing Xenopus community tools such as Xenbase ( http://www . xenbase . org , RRID:SCR_003280 ) [11 , 12] and XenMARK ( https://genomics . crick . ac . uk/apps/XenMARK , RRID:SCR_014924 ) [9] . Two of us ( MG and IP ) were motivated to undertake this research by the desire to complement our high resolution time series data in Xenopus tropicalis [1 , 2] with expression localisation data mined from public image collections , and to promote and enable further work on computational image analysis within the community . Earlier work [9] had suggested a way forward through crowd sourcing of manual annotation , but the generation , and donation to the community , by others of us ( AC-U and RP ) of a large collection of 33 , 289 informative in situ images , suggested that we consider computational approaches . This large set of images contained multiple images at given developmental stages for each gene , and we reasoned that a systematic reduction of this ( around 10-fold ) technical redundancy would yield a more useful and tractable set of images for use by other researchers via submission to Xenbase , the Xenopus the model organism database . Computational tools devised to achieve this would necessarily form a sound basis for further progress in image analysis in Xenopus . We do not at this stage provide a solution to the problem of registering embryo outlines with a model representation .
This algorithm locates the outline of the embryo within the image . We made two assumptions ( i ) that the distribution of colour and texture within the embryo is distinct from the distribution of colour and texture in the background , and ( ii ) that at least part of the embryo is more or less centrally located within the image . These are generally reasonable for the great majority of images we have seen during the development of this work . As a useful side-effect we can also detect images where we believe the embryo touches or is intersected by the edges of the image frame . Images are first processed to remove potential illumination artefacts and then downscaled . The degree of downscaling depends on the image , but is usually between 4- and 32-fold . Colour content and context are analysed for each downscaled pixel , and modelled as a mixture of either two ( un-cleared images ) or three ( cleared images ) Gaussian distributions . Pixels are assigned to the most likely distribution , and the image is mapped accordingly . The spatial distribution of each set of assigned pixels over the image is then considered: if a component is spread more uniformly across the image than other more compactly and centrally distributed component ( s ) , then that component is considered to represent background . Isolated foreground regions that are small or close in colour to the background are re-assigned to the surrounding value . Embryo outlines are thus defined as the border between the background and other regions . Then the embryo outline is smoothed and moved inwards by the width of the low resolution pixels used at this stage . A detailed technical description of these processes can be found in Methods §2 , and also see Figs 1 and 2 for illustration and examples . The embryo outline and its bounding box , with sides parallel to the image edges , are recorded with the image data , and a flag is set if the embryo outline touches the edges of the image . This algorithm determines the most likely hues within the previously detected outline of the embryo for stain and pigmentation . Analysis of the colour distribution inside the embryo outline is used to find statistically independent colour components of each image . These components are compared to the ( detected ) background colour ( as bleed-through is possible ) , and likely stain and pigment colours: stain is assumed to be relatively blue-green and pigment relatively red-brown ( see Methods §3 ) . Heat maps of the determined stain and pigment colours are extracted from the data using adaptive thresholding , and overlaid on the image ( Fig 1 and Fig 2 ) . A score representing the degree of stain is associated with each image , and can be used to rank image for selection amongst sets of known duplicates ( see Methods §6 ) . This was useful in our analysis of the large , highly redundant , image collection ( see below ) . The overall image analysis workflow consists of 8 steps , these are summarised as follows ( see also the visualisations in Figs 1 and 2 ) : To validate the performance of our algorithms we used visual inspection of significant numbers of images selected by random sampling from our available collections . We would have preferred a purely computational method , but to our knowledge there are no suitable data sets of manually marked up in situ images of Xenopus embryos available . The closest available images were the manually marked up images from the XenMARK project[9] , where the stained regions had been ‘registered’ by eye with , and transferred to , the embryo model diagrams . Even had we solved the registration problem for these embryos to enable a computational comparison with these data , we note that the subjective judgement applied during the XenMARK annotation process , as to the presence and limits of stained regions , would be much the same as using an expert annotator to compare side-by-side images of stained embryo and extracted stained regions . We further note that at this stage we were validating the correct identification of in situ stained regions within the images , irrespective of our understanding of their anatomical location . We therefore validated our algorithms in two ways: quick visual inspection of two thousand images after steps ( iii ) & ( iv ) ( see Outline Workflow above ) to check correct identification of the embryo , as opposed to the background , within the image; and more intensive inspection of two hundred images after final mark up at step ( viii ) by WISH experts for correct interpretation of the in situ stain within the embryo . The quick tests assessed three characteristics of the segmentation process: whether the image background component was correctly identified , whether the selected connected region corresponded generally to the embryo , and whether intersections of the frame edge with the embryo were correctly identified . For these tests we randomly sampled 1000 images from our local hosting of the XenMARK database , as well as 1000 images from the Ciau-Uitz/Patient collection . The errors observed were sufficently distinctive as to be effectively non-subjective , and tests results were scored true or false , with false positive and false negative results distinguished for the embryo/image boundary collision test . Most of the tests were passed at well over 99% , with the exception being for embryo/image edge collisions where false positive results were around 5% , depending on which collection images were from . These data are presented in more detail , along with expected error rates and corresponding 95% confidence intervals , in Table 1 . The more intense inspection by two WISH experts looked at the precision of identification of the embryo outline and the extent of both the stained and pigmented regions within the embryo . The 200 tested images were randomly sampled from both the local XenMARK images and the Ciau-Uitz/Patient collection in proportion to the numbers of images in each collection . Each expert assessed the same set of images , but were instructed not to compare notes during this process . The experts were presented side-by-side with the original and marked up images and asked to give a subjective assessment of good , intermediate or bad for each of the three criteria: embryo outline , stained region and pigmented region . For subsequent analysis these assessments were converted to scores of 1 . 0 , 0 . 5 and 0 . 0 respectively . These data are presented in Table 2 . Overall the results were encouraging , with both experts rating the algorithm for outline detection and expression domain extent ( stained region ) as close to or better than 90% in the good or intermediate categories . However it is quite notable that the correlation between the experts’ individual converted numerical scores was only a little over 0 . 5 for stained regions and 0 . 6 for pigmented regions . The underlying cause of this apparent discrepancy is likely in the different interpretation of the terms good and intermediate between the two experts , with Expert 1 being consistently more generous at the intermediate/good boundary then Expert 2 . These results underscore the general problem in converting the variable intensity of the stained region into a computationally tractable expression pattern . We address this problem in part by providing a two-tone scale for mark up in situ stained regions . The pigmented region generally scored worse than the other criteria , although this is obviously of lower concern . We suspect , but have not shown in detail , that the dissimilarities in scores by the experts were attributed to their different assessment of the impact of artefacts caused by imaging conditions on the extracted pigment patterns . One of the more obvious artefacts affecting annotated stain pattern occurred in images where the embryo was illuminated from one side . In these cases the algorithm tended to interpret darker areas caused by shadow as more intensely stained . To test the effectiveness of the algorithms , and to give us the opportunity to produce coherent sets of time dependent gene expression images , we applied them to a highly redundant image collection comprised 33 , 289 individual in situ images of Xenopus laevis embryos . These represented expression of 548 genes over the classical Nieuwkoop & Faber developmental stages [13] , mostly between NF stage 6 ( 32-cell stage ) and NF stage 50 ( late tadpole stage ) , with an approximate 10-fold redundancy at each genes and development stage . We refer to this image set in the text as the Ciau-Uitz/Patient collection after its originators ( AC-U and RP ) . This collection has been described previously [14] , as have the methods by which they were produced[15] . The images from this collection used to illustrate our method have not been previously published . The collection had been pre-screened by one of the originators ( AC-U ) to retain only images of stages with clearly detectable gene expression , and in total the collection contains 2781 gene/stage groups . Embryos had been imaged either directly after histological staining , or after additional treatment with a clearing agent . In general , both types of preparation were available for each gene and stage . Un-cleared embryos had been imaged against an orange/red background , and cleared embryos against a grey background . All images were whole-mount , and although the majority of the images included the whole embryo , almost a third of the images contained close-ups of specific regions of the embryo . Images generally had associated meta-data , notably the gene name or probe/sequence ID and the developmental stage , all embedded in the name of the image file . Nearly all the Stage 22 and later images were lateral views; early stages included mixture of views . See Fig 3 for a visual depiction of the problem and its resolution . Our aim was to reduce redundancy in this collection by extracting single representative whole-embryo images for each gene in the collection at each developmental stage , and for the cleared and uncleared embryos . In addition , for the earlier quasi-spherical embryonic stages , we also wished to select images to represent the different anatomical views of the embryo and expression patterns . To achieve these ends we needed two additional algorithms: the first of these simply classifies the images into cleared and un-cleared on the basis of their statistical distributions of pixel colours , whilst the second uses image similarity clustering to identify different views of the spherical stage embryos using the previously detected in situ stain patterns . These algorithms are described in outline here , and more detail is given in Methods §4 and §5 . This algorithm classified the Ciau-Uitz/Patient images into two groups , those with un-cleared and those with cleared embryos . These had been consistently photographed against an orange/red background or a grey background respectively . This knowledge was used to sort the images on the basis of the statistical properties of the distribution of pixel colour in LAB space within each image , using a Gaussian mixture approach . See Methods §4 for details . We found 18 , 254 un-cleared images , 15 , 034 cleared images , and 1 image was rejected as un-classified . Classification was important ( a ) to allow selection of both cleared and un-cleared images for each gene/stage where both were present , and ( b ) to allow a mixture of either two ( un-cleared ) or three ( cleared ) Gaussian distributions for the embryo/background analysis . The algorithm assesses similarity between expression patterns , and clusters images into groups , ideally representing different orientations of spherical embryos when photographed from different angles . Image comparison is performed after discovery of the embryo boundary and mark up of stain regions: within a group of same ( spherical ) stage images , each image is compared to all others by finding the combination of relative shift , rotation , scaling and shearing that maximises the overlap of stained regions . The minimum discrepancy achieved between two images is used as a dissimilarity metric , and pair-wise dissimilarities are used to perform clustering , of which the sub-groups represent the diversity of views of the expression pattern ( Methods §5 and Fig 4 ) . This algorithm represents a possible first step towards resolution of the image registration problem , although we take it no further in the current work . We tested the image classification method using the quick visual inspection described above . We used the same set of 1000 images randomly selected from the Ciau-Uitz/Patient collection , and compared the predicted classification ( cleared/un-cleared ) with our observations . The image classification tool made no errors . These data are presented in Table 1 . In addition to this , we also assessed the expression pattern clustering performed with spherical stage embryos from the Ciau-Uitz/Patient collection . We had earlier noted that 79 image groups had embryo orientation information embedded in the image file names; this had not been used to support the computational clustering . We therefore compared the partitioning of the images by clustering to the partitioning provided by the image name annotation ( see Methods §8 ) , finding the sensitivity and the specificity of expression pattern clustering to annotated orientation to be 60 . 5% and 74% respectively . This lower sensitivity is primarily caused by non-informative expression patterns ( i . e . uniform staining or absence of it ) in some embryos , compounded by some imprecision between the described and likely actual viewing angles . On the other hand , the specificity value is explained by imperfect clustering of intrinsically variable expression domains and stain intensities . The distinctive step for this analysis is to take any group of similar images and rank them according to the extent of in situ stain detected , from which a representative image can be easily selected ( Methods §6 and Fig 3A ) . The image selection pipeline runs as follows: ( a ) images are first classified as un-cleared or cleared to determine whether the initial image analysis needs to use two or three Gaussian components; ( b ) images are then analysed using the primary algorithms ( described above ) for embryo outline and in situ stain , recording whether the embryo touched the image frame or not , the position of the embryo outline and its bounding box , and the location and overall amount of in situ stain; ( c ) images are sorted into groups according to gene and developmental stage information; ( d ) images of spherical stages embryos are further grouped by anatomical viewpoint by clustering on the in situ stain patterns; ( e ) images within each final group are then ranked by in situ stain content to enable selection of one representative image , in our case the one with most stain , and selecting whole embryo images ahead of partial ones; and finally ( f ) images where the embryo was rather small are cropped to +15% of the embryo outline bounding box for display purposes . These functions are all provided within the command line Python program ‘select_images’ , included in the ‘isimage’ module described below . Application of the ‘select_images’ program ( based on an earlier but fully functional version of the ‘isimage’ module ) to the Ciau-Uitz/Patient collection resulted in the selection of 4 , 852 images , suitable for immediate web display , from the original 33 , 289 images . This smaller set was submitted to Xenbase , and is displayed on the appropriate gene pages . This effective consolidation of the original collection would have been extremely difficult to achieve by any other method . To illustrate the power of this analysis to organise this large pool of data , showing the evolution of gene expression patterns during development , we include the selected images for the developmentally important genes prdm1 , ank1 and hoxb3 ( Fig 5 ) . This illustrates well the importance of separating the spherical stage images by orientation , giving a clear picture of the intricate gene expression patterns developing through gastrulation and the setting up of neural patterning . To view the non-redundant version of the Ciau-Uitz/Patient image collection go to the Expression Search page in Xenbase , enter Patient Lab in the Experimenter field and click on the Search button . The two main algorithms , for embryo outline detection and stain/pigment decomposition , are the backbone of our image analysis suite and form the primary image analysis workflow described above . The image clustering algorithm was developed as a useful tool for grouping expression patters for early stage embryos , but is also a potential step towards image registration . These algorithms are implemented as parts of a Python module 'isimage’ , including the program ‘analyse_image’ which provides access to the algorithms from the command line and allows expression pattern extraction to be performed on a per image basis . Code for these algorithms is made freely available on https://pypi . python . org/pypi/isimage/ .
We have presented a general framework for analysis of whole mount in situ hybridisation images in Xenopus which is based on two specific advances . The first advance is to base segmentation around a novel method for building a statistical model of the image based on analysing colour and colour gradient in separate scales . The second advance is in the separation of in situ stain and pigment colouration using a hint based method taking in the prior ( per image ) determination of likely background colour in the segmentation step . For the first advance , we have introduced an approach for unsupervised building of the explicit statistical model of the image background . It is based on capturing both colour and colour gradient in two scales and then using Gaussian mixture analysis to find the best separation of segments having different properties . The spatial distribution of segments is then analysed , and the background model is selected . Finally , the resulting boundary is smoothed employing re-normalised probabilities under the background model as the external force in the curvature minimizing PDE . The novelty of this segmentation algorithm is in the way it utilises colour , spatial and edge information . This is in contrast to existing general purpose image analysis algorithms [16 , 17] , which augment colours with spatial information , using edges as external constraint . Here we have jointly modelled the colour and the colour gradient , thus incorporating edge information into the GMM , whilst spatial distribution of pixels is used downstream to the GMM to classify the Gaussian mixture components . This approach was motivated by the known difficulties encountered by edge detectors in whole mount in situ images: ( i ) finding the correct transition between background and unstained regions at the edges of cleared and half-transparent parts of un-cleared embryos , and thus missing the correct outline[18]; and ( ii ) when the embryo is imaged against a feature rich background ( for instance , and commonly , crushed ice ) , and the given approach detects spurious segments in the background[19 , 20] . Joint modelling of the colour and texture cues brings our algorithm closer to the texture classification method published by Permuter and colleagues[21 , 22] . This is , however , not completely suitable for in situ images because wavelets ( employed in their approach ) capture the texture at all scales [23] , whereas in segmentation of in situ images variation smaller than a certain scale is unlikely to be significant . Thus the colour and the gradient data modelled by the GMM in our approach were captured in two consecutive layers of the Gaussian pyramid , ensuring that only significantly large texture elements are captured . Such an approach allowed us to put an upper limit on the number of Gaussian mixture components: in un-cleared images the model consisted of 2 components representing the embryo ( s ) and the background , and in cleared images no more than 3 components were considered , representing staining , the unstained embryo body and the background . For the second advance , we have suggested a hint based method for pigment/stain separation and subtraction of the background colour , corresponding to bleed-through , from the embryo region of the image prior to analysis . The algorithm estimates the number of independent colours in a masked embryo image based on information theoretic considerations . Then , by employing FastICA algorithm and colour hints provided ( primarily that stain is relatively blue-green and pigment relatively red-brown ) , it estimates and classifies stain and pigment colours . These hints would be configurable for application to other systems . Our image analysis framework allows the determination of an object’s outline ( i . e . the embryo’s outline ) in an image , with minimal assumptions about background and object properties . It also allows the extraction of stain patterns from the image whilst excluding natural pigmentation from consideration . The quality of the analysis was independently checked by two WISH experts , who found it performed well . In addition , application of these tools allowed us to reduce redundancy in a set of 33 , 289 Xenopus embryo WISH images , resulting in 4 , 852 high quality representative images , making this collection amenable to display in a public resource . Analysis of the image collection , including pattern clustering where needed , took around 12 hours on 40 compute cores . The framework is published as a Python module ‘isimage’ , and includes the image selection pipeline and a command line utility to extract the expression pattern from the image . The significance of our approach to the initial segmentation of the image is well illustrated by its ability to find the embryo boundary in a wide range of image background colours and textures , not to mention variation in the shape , position and orientation of the embryo ( seen clearly in Fig 2 ) . This makes it potentially an ideal tool for retro-analysis of existing image collections , and stands in contrast to some of the earlier successes in the field which relied on controlling aspects of the image appearance such as background colour or texture compared to the embryo[3 , 5] , effectively tuning the performance of their algorithms towards the images sets for which they were developed . We believe that our approach has great promise for the development of a more widely applicable tool set . Our choice of manual validation at different steps in the image analysis pipeline was driven by a number of considerations , not the least of which was the availability of WISH experts to assess performance . In addition , we had some concerns about the potential for unconscious bias in the construction of a gold-standard reference set of manually annotated images , especially in the delineation of in situ stain regions . It is clear that notional boundaries of stained regions are often poorly defined as strong staining shades gradually into weaker and unstained regions , and that subjective judgments of these are inevitably made even by experienced annotators . This might be self-fulfilling if these image sets were constructed by ourselves , or lock the algorithm onto a particular operator bias , producing results with which other experts might not agree . The potential for different interpretation we saw clearly within our own experts and their judgment of how well the stain and pigment recognition algorithm worked . In the absence of a suitable gold standard we felt it was more effective to understand the actual performance of our algorithms , improving them iteratively by studying their behavior , and ultimately allowing other experts to assess their effectiveness . Nevertheless , we suspect there may be room for improvement , and are keen to put these codes into the public domain where others may build on our ideas . A computational approach to validation was used in a recent paper describing image analysis in Drosophila [24] . They randomly sampled 200 in-situ images and tested the performance of their segmentation and registration algorithms against manually segmented and registered embryos . This may have been important , given the inclusion of the more complex registration step , and given that both segmentation and registration are ( presumably ) less prone to subjective variation in manual operations than in situ staining . The primary weakness of the method is in the colour identification of pigmented regions of the embryo , and a tendency to be affected by brightly illuminated or shadowed sections of the embryo , which may say as much about the limitations of digital imaging under extremes of contrast . In this sense , our project has some clear pointers for optimising image generation where it is likely to be associated with subsequent computational analysis: notably avoiding bright and non-uniform illumination , and shadows . The difficulty in correctly identifying the extent of pigmented regions is less of a problem , as we are primarily interested in mapping the in situ stain; but we do believe that mapping the pigmented areas independently ensures that stain identification is more robust . In future work we will turn to the problem of registration , where we hope to be able to use models of known regional expression in combination with a refinement of our image comparison methods based on transformations of position , scale , rotation and shear to identify the likely anatomical viewpoint .
The algorithms developed were implemented as a set of functions and classes in the programming language Python . The code is based on numpy , scipy , sklearn , OpenCV libraries and organised in Python module ‘isimage’ in a way that allows use of either the individual algorithms or the image selection pipeline as a whole . The code can be freely downloaded from https://pypi . python . org/pypi/isimage/ . For each image in the LAB colour space , a Gaussian pyramid [25] is constructed {Ik ( x , y ) }k=1n; where n is the number of layers in the pyramid and Ik:R2→R3 is the k-th layer of the pyramid . Search for an object is performed simultaneously in two adjacent layers of the Gaussian pyramid . The largest dimension of the biggest layer used is less than 200 pixels . From each of the two layers , colour and edge information are extracted . The edge information is represented as partial derivatives ∂∂xIk ( x , y ) and ∂∂yIk ( x , y ) computed with the Scharr operator [26] . The data extracted from the low-resolution layer are interpolated to match the high-resolution layer dimensions , with the same Gaussian kernel used for the pyramid creation . The information from both layers is combined resulting in 18 parameters for each pixel . The data is then ‘whitened’ and only informative principal components are used in the subsequent analysis . Principal components whose singular values , divided by the sum of all singular values , exceeded 10−6 are considered informative . To learn the borderline between the background and the foreground , the data is modelled as a mixture of Gaussian distributions . Since in un-cleared images the embryo is very distinct from the background , those images are modelled as the mixture of up to two Gaussians representing an embryo and the background . On the other hand , in cleared images the difference in colour and texture between unstained parts of embryo and the background can be subtle , compared to their difference from stained regions . In these cases , the two-component mixture will often draw the line between stained and unstained areas of the embryo thus counting unstained regions as the background . To handle this , cleared images are modelled as having up to 3 components , with an assumption that background is captured in one component and the embryo is captured in two other components . The actual number of components is determined with Bayesian information criterion[27] . The GM model was fitted using a random sampling of the image , but excluding data within three pixels of the image edges . GM fitting and component classification is repeated three times , then the most likely foreground/background decomposition is brought forward for further analysis . Once the model is fitted and pixels were assigned to one of the components , there is a need to classify the components themselves as representing either the object or the background . Here Xi is the set of pixels of the image I , which is classified as belonging to a component Ci . w and h are the width and the height of the image respectively . To choose the most likely classification of the components , a Bayesian model selection approach is used: P ( M|X ) ∝P ( X|M ) P ( M ) P ( X|Mj ) =P ( Xi=j|Mj ) P ( Xi≠j|Mj ) Here M is a random variable representing a model and Mj is the model in which the jth component is believed to be the background . The prior distribution of the models was uniform . Based on the assumption that the embryo resides in the middle of the picture , background pixels are modelled to be distributed uniformly across the image . And object pixels are modelled to be distributed normally around the centre of the image . Here N ( 0 , Σ ) is a two-dimensional normal distribution with zero mean and Σ covariance matrix . W−1 ( Ψ , ν ) denotes the inverse Wishart distribution , the conjugate prior to multivariate normal with known mean . The parameters of the prior distributions Ψ and ν were chosen to be ν = 1 and Ψ=[ ( w2 ) 200 ( l2 ) 2] . Γ2 is the multivariate gamma function . nj is the number of pixels which belong to the foreground under the jth model . If none of the models is substantially better , the component whose pixels are present the most often at the image edge is classified as the background . The assumption is that the image contains only one embryo , but the foreground found above , along with the embryo outline , will have some noise in the form of a number of disconnected islands . In order to remove the noise in the foreground , a connectivity graph is created by connecting adjacent pixels belonging to the foreground . Disconnected sub-graphs of the connectivity graphs are extracted by a spectral graph theory approach[28] . The graph is recursively cut at points where the sorted elements of the eigenvector associated with zero valued eigenvalue of the Laplacian matrix of the graph exhibit the biggest jump exceeding the threshold 10−4 . The sub-graphs are compared based on their size and the difference of the average colour from the average colour of the background . The island having the maximum product of the square root of its size and the difference from the background is selected as the embryo outline . Where Si is a disconnected sub-graph of the connectivity graph S; MSi is the average colour of pixels in sub-graph Si; Mbg is the average colour of the background . The steps of splitting the foreground into disconnected components and selecting the most outstanding island are repeated twice , with the first round taking the entire foreground into account and the second round ignoring the parts of the image that are 3 pixels away from the image edges . The outline is considered touching the image edge if the selected foreground component touches the image edge in both cases . The embryos in the image are assumed to have no holes , thus all holes inside the closed contour around the selected foreground element are filled . In case the photographed embryo extends beyond the image , it is sometimes necessary to close the embryo contour along the image edge before filling the holes . To close the embryo contour along the image edge a randomly selected quarter of pixels in the 2-pixel band around the image edge is marked as foreground and then islands containing a single pixel are reverted to the background . The process is repeated until the size of the biggest foreground element increases by less than 5% during the last iteration , but no more than 100 times . The resulting outline is smoothed by minimizing local curvature of the outline contour using the geodesic active contour framework proposed in [29] . The framework assigns to a curve an energy functional , which depends on the contents of an image . To minimize the functional [29] proposes a contour evolution partial differential equation ( PDE ) , the stationary state of which minimizes the functional . The PDE contains three members on the right hand side; first corresponding to curvature force , which minimizes local curvature; second is balloon force , which tends to expand or contract the contour; third is the image attraction force , which makes the contour reflect the contents of an image . The stationary state of the equation is found using the level set approach as suggested in [29] , with zero balloon force everywhere and 8 iterations for curvature force . Since the unsmoothed contour is the collection of points where the probability of belonging to the background under GMM equals the probability of belonging to the foreground , it is natural to use the probabilities as a base for the image attraction force; the log-likelihoods of pixels are first divided by the minimum log-likelihoods for the background and foreground respectively and then summed . Images with the outline touching the image edge are considered to be presenting incomplete embryo , and a flag is set in the image data to record this . Next , the outline is scaled up to match the original image dimensions . The resulting contour is then contracted by the number of pixels corresponding to half the scaling factor between the original image and the smallest layer in the Gaussian pyramid that is used for the embryo search , to make the final embryo outline . A bounding box is recorded in the image data , which is the rectangle with sides parallel to the images edges that just contains the embryo outline . In the ‘select_images’ program there is a pre-processing step before stain distribution extraction , since differences in embryo illumination can affect the estimation of the stain intensity . Where appropriate , the background colour is assumed to be the same for all compared images ( either cleared or un-cleared images ) in a specific gene/stage group where the images are from the same collection . Thus any differences in background colour amongst those images are assumed to be caused by imaging conditions . The differences in average luminosity in all images to be compared are compensated . Then the images are processed independently . An image is converted into CMY colour space . The model behind the analysis assumes that an image is “painted” with a small number of paints , with each pixel colour being a linear mixture of different amount of each paint: xi=Asi Where xi is a CMY colour of ith pixel , A = [as , ⋯] is a matrix containing CMY values of each of the paints normalised to unit length as its columns , si is a vector representing amounts of each paints in the pixel . One approach to find a solution to the equation is independent component analysis; here we use FastICA algorithm to find the independent components [30] . The algorithm solves the equation: K ( X−X¯ ) =MS Where M is a symmetric n×n “mixing” matrix; K is a n×m whitening matrix; m is the dimensionality of the data; n≤m is a number of independent components; S is latent “source” variables . Despite the speed and underlying assumptions of the FastICA algorithm aligned well with the needs of this project , the algorithm has some drawbacks . As seen from the above equation , the FastICA algorithm finds a solution up to a multiplicative constant; it rather finds independent axes in data since the signs or magnitudes of the independent components cannot be determined . Furthermore , since FastICA finds the solutions for mean-centred rather than for zero-centred data an independent axis would correspond to a spectrum of colours rather than to the single colour of the respective paint . To get around these issues , prior knowledge of the expected colours of the paints is used . From the equation above , the maximum possible number of components cannot exceed the dimensionality of the data , three colour channels in our case . Thus no more than three colour components are expected: stain , pigment , and background where C = [cs , cp , cb] . The background colour is estimated by averaging the colour of pixels outside the embryo outline , whilst stain and pigment colours , blue and brown respectively , were the same for all images in Ciau-Uitz/Patient collection; all expected colours are normalised to unit length . Not all of the three components will always be present in an embryo image , thus there is a need to estimate the actual number of independent components . The FastICA algorithm finds the solution by choosing such entries in M that minimize the normality of the distribution of “source” variables . From that , it appears natural to estimate the number of components by maximizing the average information content per component as measured by Kullback-Leibler divergence of the empirical distribution of a “source” component from the fit Gaussian distribution . If the number of components does not match the number of expected colours , some of the expected colours are assumed not present in the image and are removed from the set . At this stage the stain colour is assumed to always be present , thus if the estimated number of components is one the expected stain colour is the only one left in the set . If the estimated number of independent components is two whilst the number of expected colours is three , there is a need to identify which colour is missing . Since it is preferable to detect faint stain , whilst faint pigment can safely be ignored , the assumption at this step is that stain colour is always present and the missing colour is either background colour or pigment colour . The ambiguity is resolved by maximising the linear combination of the absolute values of the determinant of the correlation matrix between the distribution of the colours in the image and the distribution of independent “sources” , and the cosine between the normals to the planes formed by the vectors left in the set and two first principal components ( principal plane ) of the pixel colours . Where C is a matrix containing in its columns n expected colours including stain colour . Xe is colour values of pixels inside the embryo outline . T = M−1K is matrix of independent components . u×v^ means cross-product of u and v normalized to unit length . If the resulting set of expected colours includes the pigment colour , the stain and pigment colours are adjusted . The adjustment is done by rotating vectors representing the colours in the CMY colour space around their mean by an angle ranging from -15 to +15 degrees in order to make the plane formed by the vectors as parallel as possible to the principal plane of the pixel colours . Using both independent axes and expected colours , it is possible to estimate the components of A . The estimation is done in two steps: first , the proposed components of A are computed from the data and independent axes; second , the proposed components are compared to expected colours; the set of proposed components closest to the expected colours are accepted . The computation of the proposed components is based on the assumption that the components of A should be as far as possible from the average colour . Thus , after picking an independent axis and choosing a direction from the mean , the proposed component then equals the normalised to unit length point on the independent axis where the projection of image pixels close to the axis in colour space is maximal . To increase robustness of the method to the colour imprecisions , only pixels sufficiently distant from white colour are used . Where x is the colour of a jth pixel inside embryo outline , ‖x‖>lmin , it is set to 0 . 15; mk*^ is the kth column of M* = K+M normalised to unit length; zl∈{1 , −1} is a direction with respect to mk* . Subscript notation x∥v means the parallel and x⊥v the orthogonal component of x with respect to v such that x = x∥v+x⊥v; dmax controls the effective distance of the pixel colours from the independent axis , and is set to 0 . 05 . The best set of proposed components is found by minimizing the weighted average distance between proposed components and the expected colours multiplied by the specificity of the match . Where A′ is the proposed components matrix; n is the estimated number of independent components; ai′ is the i-th column of A′; ci is the i-th column of Ce; wi is a weight associated with each expected colour , the weights reflected a prior confidence in that colour: ws = 0 . 5 , wp = 0 . 05 , wb = 1 . Since the background colour is computed from the image it has the highest confidence . There is less confidence in prior knowledge of the stain colour since it can vary due to imaging conditions , and choice of stain reagents . The argument behind the much smaller confidence level for pigment colour is two-fold; firstly , the colour can vary due to biological differences or imaging conditions . Secondly , the model used here assumes linear colour mixing whereas the imaging condition can produce saturation effects and hence non-linear colour mixing in lighter or darker parts of images , the number of independent components can be overestimated with the superfluous components being far from any of expected . The low weight for the pigment allows that false component to be associated with the pigment in case saturation occurs in the lighter part of the spectrum . In case the pigment component is associated with saturated stain , the estimation of A is done assuming no pigment component is present . The estimated stain colour as^ is considered confidently estimated if the relative positive contribution of expected stain colour in as^ is over 5% . The spatial stain distribution is found by solving the equation X=A^S for all image pixels and taking the first “source” components . ‘Adaptive thresholding’ is applied to the stain distribution to make sure that only significant staining is taken into account . This is done by modelling the stain distribution inside the embryo outline as a mixture of two Gaussian distributions , one of which would represent ‘noise’ and the other would be considered the ‘signal’ . The noise is filtered out by selecting a threshold so that 95% of noise is under the threshold . The threshold is range limited by the interval [0 . 25 , 0 . 67] because values of staining/pigmentation below 0 . 25 level would be too close to white to be significant; on the other hand staining/pigmentation above 0 . 67 would be significant anyway , even if doesn’t form a pattern . Where S is the random variable representing staining; t is the threshold . If the background colour has non-zero projection on the stain colour it results in the “background” noise . To remove the noise from the spatial stain pattern without creating sharp artefacts , a smooth mask is created from the embryo outline as follows . Mean and standard deviation of stain amount in the area outside the embryo outline are computed . The mean amount of stain inside morphological gradient of the embryo outline is computed and recorded . The embryo outline is eroded for one round . These two operations are repeated several times , or until the mean of the stain in the gradient band exceeds the mean plus two standard deviations of the stain in the background . Then a smooth mask is created with a sigmoid profile with the inflexion point located at the distance from the embryo outline where the mean amount of stain in the corresponding gradient band is at the minimum , or if a minimum is not reached , at half way to the maximum of the mean amount of stain . In the Ciau-Uitz/Patient collection of 33 , 289 images , un-cleared images had orange/red backgrounds and cleared images had grey backgrounds . They were sorted into groups according to the colour distribution of pixels within each image . Initially , images were converted into LAB colour space , and pixel values accessed via standard library functions . To capture the colour distribution of pixels in an image , means Mi and covariance matrices Ci were computed for each image . Components of the mean vector and lower triangular parts of the Cholesky decomposition of the covariance matrices were combined to produce a data point representing colour distribution in a particular image . To learn the best separation between cleared and un-cleared images , the distribution of the data was modelled as a 2–component Gaussian mixture [31] . The model was fit using the expectation maximization algorithm . As a result images were assigned to one of the two components , hence separating cleared and un-cleared images . Embryo images , in a particular gene/stage groups with recorded stage earlier than 22 , are clustered on their expression patterns , with cleared and un-cleared images clustered separately . To find the distance between images , the spatial stain distribution of each image is aligned with those of all other images in the group . Images of stain distribution are normalized by the standard deviation of the pixel intensities and down-sampled so none of their sides exceeds 100 pixels . Alignment is done by minimizing a function with the squared Euclidian distance between spatial stain distributions of the images as the external energy with respect to linear-affine transformation of one of the images[32] . The distance is penalized for scaling . Minimization is done with BFGS algorithm . Where Si ( ⋅ ) is the spatial stain distribution in an image; α is the regularization constant . For each pair of images in the group , one of the images is taken as a reference and minimization is done from eight initial positions of the template image: 4 rotations by 90 degrees of original image and the same of a flipped image . Then the process is repeated with the other image taken as a reference . The minimum of the 16 minimal distance values is taken as the distance between the expression patterns . All the pairwise distances taken with the minus sign formed a similarity matrix . The clustering is done by adaptive affinity propagation algorithm [33] with the number of clusters not exceeding four . In general , images are ranked on the 85th percentile of the stain distribution and the image of the highest rank in the gene/stage group is selected . In case of clustered images of early embryos , the total similarity of an image in the cluster is added to 85th percentile of the stain distribution to account for how well the image represents the cluster . Expected error rates and corresponding 95% confidence intervals were calculated under the Bernoulli model with uninformative Beta ( 1 , 1 ) prior . Partition of the images by the clustering was compared to the partition by embryo orientation using Wallace pairwise agreement coefficient [34 , 35] . The Wallace coefficient from partition A to partition B is a ratio WA→B=a ( a+b ) ; where a and b are entries of a mismatch matrix [abcd] , which in row 1 has the numbers of pairs in the same cluster and in row 2 the numbers in the different clusters of A , and in columns the same for the partition of B . In our case , the Wallace coefficient has the meaning of sensitivity of the clustering to embryo orientation , i . e . the proportion of pairs of images put in the same cluster that have the same embryo orientation . We augmented the clustering sensitivity by a clustering specificity coefficient d ( c+d ) . All algorithm parameters were tuned using manual procedure similar to cross validation , with a training image set that reflected image collection variability . The procedure consisted of recursively applying the following two steps until optimal parameter values were found . First , parameters were adjusted to allow the algorithm to perform best on a small subset of the training set containing images the algorithm performed worst at . Then the algorithm with parameter value found at the previous step was applied to the whole training set to assess the generality of the value .
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An important component of research into the function of genes in the developing organism is an understanding of both when and where the gene is expressed . Well established molecular techniques can be used to colour the embryo in regions where the gene of interest appears , and researchers will photograph such treated embryos at different stages of development to build up the story of the gene’s use . Small numbers of these expression pattern images may easily be examined by eye , but getting usable information from large collections of such images would take an enormous investment in time by trained scientists . Computational analysis is much to be preferred , but the task is complex and difficult to generalise . The frog Xenopus is an important model for studying vertebrate development , but up till now has had no purely computational methods available for analysing gene expression . Here we present a suite of computational tools based on a range of mathematical methods , capable of recognising the outline of the embryo against a variety of backgrounds , and within the embryo separately recognising areas of both gene expression and natural pigmentation . These tools work over a wide range of embryo shapes and imaging conditions , and , in our opinion , represent a major step towards full automation of anatomical gene expression annotation in vertebrate embryology .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
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"morphogenic",
"segmentation",
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"mathematics",
"vertebrates",
"pigments",
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] |
2018
|
New methods for computational decomposition of whole-mount in situ images enable effective curation of a large, highly redundant collection of Xenopus images
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The process of assigning a finite set of tags or labels to a collection of observations , subject to side conditions , is notable for its computational complexity . This labeling paradigm is of theoretical and practical relevance to a wide range of biological applications , including the analysis of data from DNA microarrays , metabolomics experiments , and biomolecular nuclear magnetic resonance ( NMR ) spectroscopy . We present a novel algorithm , called Probabilistic Interaction Network of Evidence ( PINE ) , that achieves robust , unsupervised probabilistic labeling of data . The computational core of PINE uses estimates of evidence derived from empirical distributions of previously observed data , along with consistency measures , to drive a fictitious system M with Hamiltonian H to a quasi-stationary state that produces probabilistic label assignments for relevant subsets of the data . We demonstrate the successful application of PINE to a key task in protein NMR spectroscopy: that of converting peak lists extracted from various NMR experiments into assignments associated with probabilities for their correctness . This application , called PINE-NMR , is available from a freely accessible computer server ( http://pine . nmrfam . wisc . edu ) . The PINE-NMR server accepts as input the sequence of the protein plus user-specified combinations of data corresponding to an extensive list of NMR experiments; it provides as output a probabilistic assignment of NMR signals ( chemical shifts ) to sequence-specific backbone and aliphatic side chain atoms plus a probabilistic determination of the protein secondary structure . PINE-NMR can accommodate prior information about assignments or stable isotope labeling schemes . As part of the analysis , PINE-NMR identifies , verifies , and rectifies problems related to chemical shift referencing or erroneous input data . PINE-NMR achieves robust and consistent results that have been shown to be effective in subsequent steps of NMR structure determination .
The usual approach to the solution of the problem of assigning labels to subsets of peaks ( spin subsystems ) assembled from multiple sets of noisy spectra is to collect a number of multidimensional , multinuclear datasets . After converting the time domain data to frequency domain spectra by Fourier transformation , peaks are picked from each spectrum for analysis . Methods have been developed for automated peak picking or global analysis of spectra to yield models consisting of peaks with known intensity , frequency , phase , and decay rate or linewidth [7] , [8] . In the ideal case , the resulting peak-lists identify combinatorial subsets of two or more covalently bonded nuclei by their respective frequencies ( Figure 2 ) . These subsets must be “assembled” in a coherent way to “best” correspond to specific atoms in the amino acid sequence of the protein . In practice , peak lists do not report on all nuclei ( because some peaks are missing ) , and “noise peaks” ( peaks incorrectly reported as true peaks ) are commonplace . In the examples analyzed here ( Table 1 ) , the level of missing peaks varied between 9% and 38% , while the level of noise peaks varied between 10% and 135% . The large number of false positives as well as false negatives typically present in the data result in an explosion of “ambiguities” during the assembly of subsets . A common feature among prior approaches has been to divide the assignment of labels into a sequence of discrete steps and to apply varying methods at each step . These steps typically include an “assignment step” [9]–[12] , a secondary structure determination step [13]–[15] , and a “validation step” [16] . The validation step , in which a discrete reliability measure indicates the possible presence of outliers , misassignments , or abnormal backbone chemical shift values , is sometimes omitted . Other steps can be added , or steps can be split further into simpler tasks . For example , backbone and side chain assignments frequently are carried out sequentially as separate processes . Some approaches to sequence-specific assignment rely on a substantially reduced combinatorial set of input data by assuming a prior subset selection , e . g . , prior spin system assembly [17] , [18] . The specification of conformational states can be added as yet another labeling step . For example , backbone dihedral angles can be specified on a grid ( e . g . , 30° intervals ) as determined from chemical shifts [19] , coupling constants and/or NOEs [20] , or reduced dipolar couplings [21] . The NMR assignment problem has been highly researched , and is most naturally formulated as a combinatorial optimization problem , which can be subsequently solved using a variety of algorithms . A 2004 review listed on the order of 100 algorithms and software packages [22] , and additional approaches are given in a 2008 review [23] . Prior methods have included stochastic approaches , such as simulated annealing/Monte Carlo algorithms [24]–[26] , genetic algorithms [27] , exhaustive search algorithms [17] , [28]–[30] , heuristic comparison to predicted chemical shifts derived from homologous proteins [31] , heuristic best-first algorithms [32]–[34] , and constraint-based expert system that use heuristic best-first mapping algorithm [35] . Of these , the most established , as judged from BMRB entries that cite the assignment software packages used , are Autoassign [10] and GARANT [27] . Similarly , a wide range of methods have been used to predict the protein secondary structural elements that play an important role in classifying proteins [36] , [37] . Prior approaches to assigning a secondary structure label to each residue of the protein have included the Δδ method [38] , the chemical shift index method [14] , a database approach ( TALOS ) [19] , an empirical probability-based method [39] , a supervised machine learning approach [40] , and a probabilistic approach that utilizes a local statistical potential to combine predictive potentials derived from the sequence and chemical shifts [13] . Recently , a fully automated approach to protein structure determination , FLYA , has been described that pipelines the standard steps from NMR spectra to structure and utilizes GARANT as the assignment engine [41] . The FLYA approach demonstrates the benefits of making use of information from each step in an iterative fashion to achieve a high number of backbone and side chain assignments . Our goal is to implement a comprehensive approach that utilizes a network model rather than a pipeline model and relies on a probabilistic analysis for the results . We reformulate the combinatorial optimization problem whereby each labeling configuration in the ensemble has an associated but unknown non-vanishing probability . The PINE algorithm enables full integration of information from disparate steps to achieve a probabilistic analysis . The use of probabilities provides the means for sharing and refining incomplete information among the current standard steps , or steps introduced by future developments . In addition , probabilistic analysis deals directly with the multiple minima problem that arises in cases where the data does not support a single optimal and self-consistent state . A common example is a protein that populates two stable conformational states . The PINE-NMR package described here represents a first step in approaching the goal of a full probabilistic approach to protein NMR spectroscopy . PINE-NMR accepts as input the sequence of the protein plus peak lists derived from one or more NMR experiments chosen by the user from an extensive list of possibilities . PINE-NMR provides as output a probabilistic assignment of backbone and aliphatic side chain chemical shifts and the secondary structure of the protein . At the same time , it identifies , verifies , and , if needed , rectifies , problems related to chemical shift referencing or the consistency of assignments with determined secondary structure . PINE-NMR can make use of prior information derived independently by other means , such as selective labeling patterns or spin system assignments . In principle , the networked model of PINE-NMR is extensible in both directions within the pipeline for protein structure determination ( Figure 1 ) : it can be combined with adaptive data collection at the front or with three-dimensional structure determination at the back end . Such extensions should lead to a rapid and fully automated approach to NMR structure determination that would yield the structure most consistent with all available data and with confidence limits on atom positions explicitly represented . In addition to its application to NMR spectroscopy , the PINE approach should be applicable to the unbiased classification of biological data in other domains of interest , such as systems biology , in which data of various types need to be integrated: genomics ( DNA chips ) , proteomics ( MS analysis of proteins ) , and metabolomics ( GC-MS , LC-MS , and NMR ) data collected as a function of time and environmental variables .
The fundamental idea of PINE is to embed the original assignment problem into a higher dimensional setting and to use empirically estimated compatibility ( or similarity ) conditions to iteratively arrive at an internally coherent labeling state . These conditions are embodied in the form of a parameterized Hamiltonian ( energy function ) that evolves at each iteration step . In the quasi-stationary regime , this construction yields clusters , defined as subsets of chemical shift data with assigned labels . The clusters have strong intra-cluster links and highly localized inter-cluster couplings . We view each possible cluster of related experimental data in the domain as a “site” that is to be potentially labeled . More specifically , our goal is to discover ( learn ) the map f that relates the “domain” ( set of subsets of data ) to the “codomain” ( set of subsets of labels ) :where X = [x1 , x2 , … , xm] is the set of data values available from all experiments , and L = [L1 , L2 , … , Ln] is the set of labels associated to the chemical shifts . At first it may appear that this map is trivial , because one protein has precisely one set of correct chemical shifts . However , breaks in the backbone sequential data , incompleteness of experimental peak lists , and the presence of many noise peaks renders the discovery of a deterministic one-to-one map to the sequential labels unpromising . Rather than discovering a single map , we opt to find a set of maps , each with its associated probability . More directly , we choose to associate subsets of labels from the list L to subsets of data from the list X , each with a commensurate probability: In order to formulate the computational problem , we require that the labels for data values satisfy constraints that arise from the system of neighborhoods built around each data value . The system of neighborhoods is a dynamic state variable that co-evolves with the probability values . We assign an initial set of labels , L , with associated weights to each input data point , S ( e . g . , chemical shift ) and introduce a measure of similarity based on distances between “neighboring points” ( Figure 3 ) . Typically , in our starting configuration , the possible labels for each data value far exceed the number of sites . The set of labels contains the “null” label to allow for the case where a data element cannot be labeled . The approach used to measure the global compatibility or support for the specific labeling of site S at iteration step m is to aggregate the compatibilities over versions of individual evidences by applying a variation of the belief propagation algorithm [42] . The evidence for assignment is weighted by the probability of each “neighbor” being correct , and the probabilities at stage m can be updated by replacing them by the new weight configuration state . As probabilities evolve , the information content of changing configurations is monitored for the optimally “informative” state . The resulting model is analogous to the random cluster Fortuin and Kasteleyn ( FK ) model [43] . In practice , a straightforward implementation leads to densely connected networks with noisy weights and no principled way to control the iteration steps . To implement the intuitively appealing ideas presented above that are designed to find the optimal state in the form of marginal probabilities , we have devised an iterative approach that utilizes topology selection followed by a variation of belief propagation algorithm [42] and subsequent adjustment of initial weights and topology . This topology selection step plays a key role in achieving robust and computationally efficient results . We proceed by analogy to FK [43] . Let G = ( V , E ) be any general graph , with e∈E an edge in G , and ν∈V a vertex . The set of assignments ( or labels ) for each vertex is designated by [1 , 2 , … , q] . The “configuration energy” of this system is encoded in the partition function: ( 1 ) In this formula , the outside sum is performed over the configuration states of the system represented by the map λ , and the inside product measures the compatibility of the vertex labels joined by the edge e . Each edge is weighted by the factor and has end-point vertices , and δ is the compatibility measure of end-point vertices configuration . By defining and , Eq 1 can be rewritten as: ( 2 ) In the setting of statistical physics , the Boltzmann weight of a configuration is , where H ( the sum in the exponential ) represents the energy of the configuration and β is a parameter called the inverse temperature . Because the weights are assumed to be positive , they can be interpreted probabilistically ( after normalization by Z ) as a probability measure on the states for the graph G where N is the number of vertices . In the standard random-cluster model , the neighborhood structure , or topology , of the graph is prescribed , and the objective is to find the ground state for a given set of weights by varying the “spin” , or labeling , configurations . In our case , we are determining the ground state ensemble and the topology of the model at the same time . At each iteration step i , we define Ai , a subset of the graph G , where , and evaluate the partition function for this subset . We evolve the topology of the graph at each iteration by the addition and removal of edges and by refining the edge weights toward the optimum topology as described in the algorithm section . A local Bayesian updating procedure updates the weights , and the local rate of change of weights is used to modify the corresponding local topology of the graph . On the subsequent iteration , our algorithm reintegrates these local modifications in the context of the entire network and attempts to establish a new quasi-stationary state . The algorithm must address two critical challenges . The data that describe edge weights and states in Eq 2 are derived from empirical relationships that involve noisy data , and , therefore , a straightforward deterministic search of the resulting combinatorial space would be infeasible . In addition , the computational complexity of the resulting problem grows rapidly with the number of labels and the topology of the graph; thus , a suitable starting and evolving representation of the topology , and a corresponding approximation algorithm is the key to obtaining a robust solution to this problem . The probabilistic construction used in PINE-NMR belongs to the general class of graphical models in which dependencies among random variables are constructed ahead of the inference task . In cases where the graph of dependencies is acyclic , there are powerful and efficient algorithms that correctly maximize the marginal probabilities through collecting messages from all leaf nodes at a root node [44] . When the graph is not acyclic , current algorithms for graphs with cycles often reach oscillatory states , converge to local maxima , or achieve incorrect marginals due to computational difficulties . Approaches have been described in the literature for dealing with a single loop condition [45] or for converging under alternative free energy approximations [46] , [47] . “Tree-based reparameterization” algorithms [48] have been described as a general approach that iteratively reparameterizes the distributions without changing them on the subtrees in the original graph . These algorithms , which are geared toward addressing the approximation of marginals in the presence of loops , represent trade-offs among robustness , accuracy , computational speed , and efficiency of implementation . Our modification provides a simple extension that can be described as an adaptive form of coarse-to-fine approximation . We start with a “coarser topology” and explore more refined factorizations of the probability distribution and look for stable fixed points . In our adaptive approach , the extension of the state space ( embodied in the algorithm ) plays a critical role . In intuitive terms , the additional degrees of freedom ( null states ) provide “room for change” for existing distributions as the topology is being refined . The internal working of the stepwise factorization of the probability distribution requires a coarse estimate on the initial threshold that reduces the connectivity degree of the graph . In our case , this approximation is arrived at using a combination of theory and empirical investigation . Figure 4 presents an overview of the probabilistic network implemented in PINE-NMR . Sets of probabilistic influence sub-networks are combined into a larger influence network , and each sub-network may have its own computational model used to perform the inference task . The entire probabilistic network is constructed by considering the conditional dependencies of the sub-networks . The actual implementation of PINE-NMR entails a fairly complicated network with more than 30 , 000 lines of code in Matlab and other supporting scripting language . A descriptive and stepwise version is given below . 1 . Read input data and check for errors . If errors are found , report errors and abort . 2 . Align the 1H , 15N , and 13C dimensions of all spectra independently . 3 . Generate spin systems ( Figure 5 ) . 4 . Estimate the b factor and c factor , which are the measures of data quality defined as follows:In calculating any of the above formulas , only the fields with choices are considered . For example if none of the experiments provided by the user has HA information , HA fields are not used in the calculation . 5 . If ( b<0 . 4 or c factor<0 . 2 ) # comment: Report low data quality to the user and stop . The low data quality check can be manually overridden through user requests . However , low “quality factors” are strong indicators of “highly incomplete” data and the web service discourages the use of results from low quality data . 6 . Otherwise , set K = 0 ( iteration counter ) . Repeat: 7 . K = K+1; ( iteration counter ) . 8 . Triplet amino acid typing: 9 . Derive the backbone assignment network weights based on amino acid typing scoring , connectivity experiments , latest backbone assignment , and possible outlier detections from the last iteration ( Figure 6 ) :T is a threshold value for the connectivity score , which is defined as , c*max_connectivity_score , c is the quality factor defined in 5 , and Pk-1 ( xn ( i ) ) is the probability of assigning xn ( i ) to triplet residue n in the iteration k−1 . 10 . Select the network topology; calculate the threshold for removing low-weight edges from the network based on the quality of the data , use: 11 . Apply the belief propagation algorithm [42] to find the marginal probabilities Pkn ( xn ( j ) ) of assigning triplet spin systems xn ( j ) to triplet ( tripeptide ) residues n . 12 . Given the marginal probabilities of the triplet residue assignments , derive the probabilistic assignment of the individual backbone atoms . 13 . Detect and remove the outliers in the backbone assignments [16] . 14 . Derive the secondary structure of each amino acid based on the formula: ( 5 ) pn ( s|xn ( j ) ) is the probability of residue n to be in the secondary structure state s given the assignment xn ( j ) derived from the method described in [13] , and Pkn ( xn ( j ) ) is the assignment probability of triplet residue with the center residue n , to triplet spin system xn ( j ) . The summation is over all the possible choices of tripeptides in the protein sequence . 15 . If no convergence , probabilities are the average probability of last three iterations . “No convergence” indicates the presence of “nearby” local minima . 16 . For every amino acid , generate an energetic model network and apply the Belief Propagation [42] to derive final probabilistic side chain assignments as described in supplementary material Protocol S1 . 17 . Report the final probabilistic assignments: backbone , side chain , secondary structure prediction , and possible outliers . The output can be specified to conform to variety of formats , including Xeasy , SPARKY , and NMR-STAR ( BMRB ) . The input to PINE-NMR consists of the amino acid sequence and multiple datasets known as peak lists ( chemical shifts ) obtained separately from selected , defined NMR experiments . The peak lists consist of sets of real-valued two-dimensional , three-dimensional , or four-dimensional vectors , denoted by lXij∈Rl l = 2 , 3 , 4 . The dimension of the data is denoted by l , the index j indicates that the observation is from the jth dataset , and the index i denotes the ith observation within the dataset . To compare data from different experimental sets ( different j ) that have shared subspaces ( signals from nuclei in common ) , we consider only the common subspace . This allows us to omit the index l in subsequent formulas . The similarity ( or nearness ) is used to build an initial system of neighborhoods . The approximate starting value for similarity is given a probabilistic interpretation by using Eq 3 ( Basic Algorithm: 3 . a ) to compare each datum ( peak ) Xij with the reference datum ( peak ) Xmn . The peaks in the most sensitive experiments in the dataset ( normally 15N-HSQC or HNCO ) are used as the initial reference set . We define a common putative object , called the spin system ( Figure 6 ) , by aligning the peaks along the common dimensions and by registering them with respect to reference peaks according to Eq 3 . The total number of states of the spin system is equal to the combinatorial set of all label choices including the null state . The preservation of all neighborhood information at this step is particularly important for the analysis of data from larger proteins in which noise peaks and real peaks are closely interspersed . The spin-system scoring step is used to integrate the spin system sub networks by assigning a score to each possible label that can be associated to a spin system . This process makes use of empirical chemical shift probability density functions , calculated from combined BMRB ( chemical shift ) and PDB ( coordinate ) data from proteins of known structure , for each atom of every amino acid type in three label states: α-helix , β-strand , and neither helix nor strand ( other ) [13] . The general form of the score is obtained by computing the probability of a chemical shift X having the label n ( residue number ) as described in Basic Algorithm: 8 . a . This approach connects amino acid typing and secondary structure state determination through a conditional dependency model . The successive application of weighted measures ( Basic Algorithm ) , leads to the definition of a complex network of relationships and weights among correlated sets of information at the global level ( Figure 3 ) . This process establishes an initial system of neighborhoods ( Figure 2 ) . Whenever an initial set of probabilities is unavailable , a uniform distribution is assumed as the starting state . The challenge is to address the computationally demanding problem of deriving the backbone and side chain assignments from amino acid typing and other experimental data ( connectivity experiments ) according to the model described above . Rather than modeling the assignment of labels to individual peaks , or assigning spin systems to a single amino acid , we generate triplet spin systems and label them to overlapping triplets of amino acids in the protein sequence ( Figure 5 ) . The selection of tripeptides instead of single residues reduces the complexity of the graph by eliminating a substantial number of labeling choices; however , it may introduce additional noise to the network due to possible erroneous spin system assembly . Given the trade-off between noise level and network complexity , we found that triplets yielded the optimum choice among other combinations of residues . However , the resulting network of weights and relationships has a complex topology in which a large fraction of relationships ( edges ) arise entirely from noise in the data , and the resulting random field is not amenable to a straightforward implementation . To overcome this problem , we determine , from spin system scoring and connectivity constraints , an initial topology and the sets of weights for the backbone ( Figure 6 and Basic Algorithm: 9 ) and side chain assignments ( Protocol S1 ) . The topology ordinarily is dependent on the weights and a set of parameters ( thresholds ) . These values typically are noisy and incomplete and are contaminated by false positives and false negatives . Our goal is to evolve the initial state of the system toward an “optimally coherent” state without the need for any manual parameter settings by carefully managing the selection of network topology . An initial topology for the network is determined by removing all edges with potential weights below a threshold value . The threshold value is calculated ( Basic Algorithm: 10 ) automatically by approximating the level of success achievable by each threshold ( Figure S1 ) . For a fixed set of edge values , this function is generally unimodal and defines the appropriate threshold for the starting state . At each threshold , a variation of the belief propagation algorithm [42] operates on the dense multigraph to effectively prune many edges and to derive the posterior probabilities that define clusters ( or labels ) . After each iteration step , the posterior probabilities of all label assignments are utilized to determine local topology modifications and new edge weights . Secondary structure labels are dependent variables derived from prior chemical shift assignments . Each chemical shift assignment has an associated probability , and we derive the probabilities for the assignment of secondary structure labels from a normalized and weighted sum of associated probabilities . After computing the probability of each residue n to be in each of three conformational states ( s = helix , strand , other ) by the method described in [13] for different assignment configurations , the overall secondary structure probability is calculated by Eq 5 ( Basic Algorithm ) . Note that this step involves a shift in the point of view from chemical shift centric to residue centric . Posterior probabilities derived in each iteration of the assignment process are used as local prior probabilities in the next round of assignment , provided that ( 1 ) the assignment has not been detected as an outlier , ( 2 ) the assignment of chemical shift is correlated with the assignment of secondary structure consistent with known empirical distributions , and ( 3 ) the assignment is consistent with established connectivity constraints . If one or more of the above conditions are not met , the results are deemed inconsistent because the resulting probabilities appear as outliers of the marginals supported by the current graph topology . This view is driven by the notion that the equilibrium of our fictitious system is the fixed point of the energy functional , with the factorization induced by our graph . In order to reach the consistent state , scores are re-evaluated and a new local score is computed for the next iteration; a new topology is generated , and the computational steps are repeated . The iteration process continues until a stationary or quasi-stationary state is reached , i . e . , when the topology of the network and the labeling probabilities do not vary significantly . The iteration process leads to “self-correction” through appropriate adjustments to the topology of the underlying network in order to preserve maximum information .
PINE-NMR is designed to analyze peak lists derived from one or more of a large set of NMR experiments commonly used by protein NMR spectroscopists . This set ( listed on the PINE-NMR website ) currently includes data types used for backbone and aliphatic side chain assignments . ( PINE-NMR will be expanded in the future to handle aromatic side chain assignment . ) To test the software , we asked colleagues at the Center for Eukaryotic Structural Genomics ( CESG ) and the National Magnetic Resonance Facility at Madison ( NMRFAM ) to provide subsets of data from projects that had led to structure determinations with assigned chemical shifts deposited in the BMRB [49] . We wanted the assignments to have been refined and vetted in light of a structure determination , because we took the BMRB deposited values to be “correct” . In most cases , the input data supported the determination of both backbone and aliphatic side chain assignments . In some cases , the input data supplied supported only the determination of backbone assignments . The peak lists were provided by the persons submitting the data without any specification for the peak picking software , threshold , or other parameters . Table 1 summarizes the PINE-NMR results for all datasets provided . The input datasets are indicated along with the size of the protein . A backbone or side chain assignment was scored as “correct” if the top ranked ( highest probability ) PINE-NMR assignment corresponded that in the BMRB deposition . The assignment accuracy is given as the number of “correct” assignments divided by the total number of assignments supported in theory by the input data expressed as a percentage . “The “correct” ( BMRB ) assignments had the benefit of additional information coming from NOESY data and filtering with respect to structure determination . Also listed in Table 1 is the backbone “assignment coverage” achieved by PINE-NMR ( defined as the total number of correct backbone assignments in comparison to the total backbone assignments in the corresponding BMRB deposition expressed as a percentage ) . The secondary structure accuracy reported in Table 1 compares the PINE-NMR result with the secondary structure of the deposited three-dimensional structure as determined by the DSSP software [50] . It can be seen that the accuracy of the PINE-NMR results correlates with the data quality factor . The outlier count is defined as the number of C′ , Cα , or Cβ atoms detected as possible outliers in the final assignment by the LACS method [16] . In the majority of cases , the assignment accuracy was above 90% for backbone resonances and above 80% for aliphatic side chain resonances . Two cases in Table 1 yielded assignment accuracies below 90% . In the case of the 177-residue protein ( At5g01610 ) , the lower performance was due to the poor quality of data from a highly disordered region of the protein . A human expert was unable to go beyond the PINE-NMR assignments , and additional data were required to complete the protein structure determination . In the case of the 299-residue protein ( At3g16450 ) , its stereo array isotope labeling ( SAIL ) pattern [51] gave rise to chemical shift deviations that degraded expected matches . In this case the performance of PINE-NMR could be improved by incorporating corrections for the deuterium isotope effects on the chemical shifts . An illustration of the improvement achieved by combining information comes from comparing the assignment accuracy results from PINE with those from PISTACHIO [12] ( Table 1 ) . PISTACHIO is an automated assignment tool developed earlier that does not make use of inferred secondary structure or outlier detection implemented in PINE-NMR . The results from PINE-NMR also are superior to those achieved by iterative pipelining of the individual assignment ( PISTACHIO [12] ) , secondary structure determination ( PECAN [13] ) , and outlier detection ( LACS [16] ) steps ( results not shown ) . The tests of PINE-NMR shown in Table 1 are highly stringent , in that minimal information is provided . Separate tests ( results not shown ) demonstrate that the performance is improved if the input peak lists have been pre-filtered to correspond to spin systems . The results of website users provide a separate measure of the performance of PINE-NMR . Since July , 2006 , users have analyzed more than 1 , 300 sets of chemical shift data . Without access to the final structures and chemical shift assignments for these proteins , these results could not be analyzed , as in Table 1 , with regard to correct assignments and secondary structure . Instead , we used the results from Table 1 to estimate the empirical conditional probability of incorrect labeling in the user PINE-NMR output: P ( incorrect label| plabel = x ) . Assignments with a probability higher than 0 . 95 generally were found to be correct ( Table 1 ) . Using the data submitted to the PINE-NMR web site , we selected a representative sample of proteins with numbers of residues and data quality factors similar to those in Table 1 . We then used the empirical estimate of accuracy to analyze the results from these proteins ( Table S1 ) . The outcome was in substantial agreement ( in a statistical sense ) with the results shown in Table 1 . Of particular note are two proteins submitted to PINE twice ( the proteins with 120 residues and 160 residues in Table S1 ) . In each case , after an initial submission of the data , the user provided additional experimental data prior to another round of analysis . The additional data improved the empirical estimate of accuracy and led to additional assignments at improved levels of confidence . The level of accuracy and completeness achieved in favorable cases by a single automatic PINE-NMR computation was sufficient for the initial downstream steps of structure determination . For example , the PINE assignment output for ubiquitin , which was obtained from the input of automatically picked peak lists from HSQC , HNCO , CBCA ( CO ) NH , HNCACB , C ( CO ) NH , H ( CCO ) NH , HCCH-TOCSY , HBHA ( CO ) NH , and C13-HSQC spectra , along with 15N-NOESY and 13C-NOESY spectra for this protein were provided as input to the Atnos [52]/Candid [53] program . The only manual step in the structure calculation was the determination of cross β-strand hydrogen bond constraints for the amino acid residues shown to be in β-sheet by the PINE analysis of secondary structure ( an effort taking only about one hour ) . Hydrogen bond constraints for α-helical regions were introduced based on the results of the PINE secondary structure analysis . The resulting 20 conformers that best fit the input data had an rmsd of 1 . 1 Å for backbone atoms and 1 . 7 Å for all heavy atoms ( 0 . 8 Å for backbone residues and 1 . 3 Å for all heavy atoms in ordered residues as analyzed by PSVS [54] . This structure had a backbone rmsd of 1 . 23 Å from the highly refined ubiquitin structure determined from NMR data deposited in the PDB ( 1d3z ) . Without the manual hydrogen bond constraints the structure had a backbone rmsd of 2 . 77 Å from the 1d3z structure . The level of assignments achieved by PINE-NMR for small proteins meets or exceeds the assignment levels that led to successful structure determination of small ( under 100 residue ) proteins from chemical shift data alone [5] . PINE-NMR also can be useful for semi-automated analysis of larger proteins that require for structure determination the collection of additional data such as dipolar couplings , manual NOESY assignments , or aromatic side chain assignments . We have developed PINE-NMR in ways that enable expert input , for example , by specifying a selective labeling scheme , pre-assigned cluster labels , pre-assigned spin systems , or pre-assigned cluster labels for subsets of the data . For pre-assigned cluster labels , PINE-NMR can act as a verification tool , for example , by checking their internal consistency with peak lists or by detecting chemical shift referencing problems or outliers ( the LACS report ) . The software performs spectral alignment , detects excessive noise peaks , uncovers experimental inconsistencies , recognizes the insufficiency of input data , and identifies nomenclature conflicts . The latest version of PINE-NMR is available for public use through a webserver at http://pine . nmrfam . wisc . edu . The PINE-NMR server offers complete backbone and side chain chemical shift assignment , secondary structure determination , and possible referencing error or outlier detection . The server supports a variety of convenient input and output formats , including Sparky , Xeasy , and BMRB ( NMR-STAR ) . PINE-NMR also accepts prior information that reflects experimental information the user wishes to specify , such as fixed input ( pre-assigned labels ) , selective labeling pattern , or assembled spin systems in cases where segments of the protein have been labeled by other means .
Application of the PINE algorithm to the NMR assignment problem has led to a tool that is capable of analyzing data in a self-correcting manner without the need for the user to manipulate any parameters in the software . The public availability of PINE-NMR through an online server has made it possible for a variety of users to test its accuracy and robustness . The PINE algorithm reformulates an otherwise intractable network of interactions within the context of an energy minimization problem . To address the high computational complexity of the minimization problem , we have devised a local approximation algorithm with reliable global properties . To address the non-convexity of the energy functional and the potential of “getting stuck” in local minima , we perform successive approximations with increasingly more complex energy functionals and with the reweighting of solutions . Our evolution and selection of the initial network topology of PINE-NMR emerged through the examination of two quantities: ( 1 ) the estimated conformity across all datasets with respect to a single reference dataset ( b factor ) , and ( 2 ) the estimated conformity between pairs of datasets that contained complementary information ( c factor ) . These quantities , which are calculated as described in the Basic Algorithm , were found to be generally dependent on the size of the protein and the number of false positive and false negatives in the input data . In intuitive terms , the combination of these quantities measures the degree of conformity between the vertex and edge potentials in the network model . The numerical approximation of this quantity ( in analogy to quantity called a matching polynomial ) is encoded in the fourth root of the product of b and c . For example , when pairs of data in the dataset have low conformity measures , the network topology ( e . g . change in the edge set ) is strongly influenced by label assignments . These same quantities are also used in the computation of the quality factor and the predicted number of residues assigned ( Table S1 ) . After a user submits input data to the server , PINE-NMR performs a preliminary evaluation . If factors b and c do not satisfy the required threshold , PINE reports the problem to the user and suggests possible remedies . Otherwise the assignment process continues . Typically the datasets that yielded high-quality assignments in PINE-NMR had b factors equal to 0 . 65–0 . 85 and c factors equal to 0 . 4–0 . 6 . The impact of topology selection can be investigated computationally by running simulations that test the computational complexity ( running time ) and accuracy of the results as a function of increasing network complexity . For small proteins , where the number of false positives and negatives is small , increasing network complexity leads asymptotically to higher accuracy ( Figure S1A ) . The network energy remains stable as more edges are added , and the computational complexity drops sharply as soon as an “essential network topology” is achieved . For larger proteins , increasing network complexity initially leads to higher accuracy , but accuracy falls off at the highest levels of complexity ( Figure S1B ) . The most accurate label assignments are achieved when the cardinality of the edge set for the network is small . Therefore , selecting a more complex network of interactions not only is computationally inefficient but may also lead to decreased accuracy . Inaccuracies within more complex networks tend to propagate . Specifically , high complexity neighborhoods with large numbers of edges were found to degrade the accuracy of their neighbors , and , although this effect typically is local , it also can have long-range impact . These findings reinforce the importance of selecting good initial topology and underscore the advantages of local , as opposed to global , topology modification . In practical terms , additional knowledge about the structure of a protein can improve the data interpretation . For example , NMR experts often use their experience and knowledge of similar structures or structural folds to make decisions – this knowledge is often hard to codify in an algorithm . In some instances , the bias is subtle . For example , the use of data from BMRB in order to generate simulated peaklists that are to be subsequently assigned is afflicted with bias , because the data in BRMB are highly likely to be associated with a known structure and , therefore , higher information content ( sharper localization of parameters according to Bayes' formula ) . One of the challenges in protein NMR spectroscopy is to minimize the time required for multidimensional data collection and analysis without sacrificing the quality of the resulting protein structure . We are in the process of coupling PINE-NMR to ( HIFI-NMR ) [55] , an innovative approach that uses adaptive reduced dimensionality NMR data collection . For 3D triple-resonance experiments of the kind used to assign protein backbone and side chain resonances , the probabilistic algorithm used by HIFI-NMR automatically extracts the positions ( chemical shifts ) of peaks with considerable time-savings compared with conventional stepwise approaches to data collection , processing , and peak picking . The combination of HIFI- and PINE-NMR will support fully automated , probabilistic , NMR data collection and analysis through assignments , determination of secondary structure and backbone dihedral angles . We are currently developing protocols for including H ( C ) CH-COSY , CCH-TOCSY and common four dimensional NMR experiments in the PINE-NMR network . Our future plans also include the inclusion of NOESY data , which will extend side chain assignments to aromatic residues [56] and support assignments of larger proteins [3] . The core computational model of PINE should be applicable to other problems where automated clustering is needed . For example , when DNA microarray data are used to explore all genes of an organism in order to detail their biochemical networks , automated clustering of gene networks can provide unbiased information about the underlying biology .
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What mathematicians call the “labeling problem” underlies difficulties in interpreting many classes of complex biological data . To derive valid inferences from multiple , noisy datasets , one must consider all possible combinations of the data to find the solution that best matches the experimental evidence . Exhaustive searches totally outstrip current computer resources , and , as a result , it has been necessary to resort to approximations such as branch and bound or Monte Carlo simulations , which have the disadvantages of being limited to use in separate steps of the analysis and not providing the final results in a probabilistic fashion that allows the quality of the answers to be evaluated . The Probabilistic Interaction Network of Evidence ( PINE ) algorithm that we present here offers a general solution to this problem . We have demonstrated the usefulness of the PINE approach by applying it to one of the major bottlenecks in NMR spectroscopy . The PINE-NMR server takes as input the sequence of a protein and the peak lists from one or more multidimensional NMR experiments and provides as output a probabilistic assignment of the NMR signals to specific atoms in the protein's covalent structure and a self-consistent probabilistic analysis of the protein's secondary structure .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"mathematics",
"biotechnology/protein",
"chemistry",
"and",
"proteomics",
"biophysics/experimental",
"biophysical",
"methods",
"computational",
"biology",
"chemical",
"biology/protein",
"chemistry",
"and",
"proteomics"
] |
2009
|
Probabilistic Interaction Network of Evidence Algorithm and its Application to Complete Labeling of Peak Lists from Protein NMR Spectroscopy
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Human cystic echinococcosis ( CE ) is highly endemic in the Tibetan regions of Sichuan where most families keep guard dogs and where there are considerable numbers of ownerless/stray dogs . Strong Buddhist beliefs do not allow for elimination of stray dogs , and many strays are actually fed and adopted by households or monasteries . On account of the high altitude ( 3900–5000 m ) , pasturage is the major agricultural activity in this area . The harsh mountainous climate often leads to many grazing animals dying on the pasture at the end of a hard winter . The skin and some meat are taken , and the rest of the animal is left for scavenging birds and animals . The poor sanitation and hygiene , the Buddhist doctrine of allowing old livestock to die naturally , plus the unrestricted disposal of animal viscera post-slaughter may be responsible for the high prevalence of human CE in this setting . As part of a large collaborative control program for CE in Ganzi County , situated in the west of Sichuan Province , surveillance for Echinococcus infection in domestic dogs using a coproantigen method and necropsy of unwanted dogs was carried out prior to ( in 2000 ) and after ( in 2005 ) dog anthelminthic treatment ( 5 mg/kg oral praziquantal at 6 month intervals ) to determine the efficacy of the treatment for control . The prevalence of E . granulosus only in dogs by necropsy was 27% and 22% , and prevalence of both Echinococcus spp . by necropsy was 63% and 38%; prevalence of both Echinococcus spp . by coproantigen analysis was 50% and 17% . Necropsy of sheep/goats ( age <1 to 12 years ) ( prevalence of E . granulosus in 1–6-year-old animals was 38% and in 10–12-year-old animals was 70% ) and yaks ( age 4 years ) ( prevalence of E . granulosus was 38% ) was undertaken to determine the baseline transmission pressure . Protoscoleces were only found in very old sheep/goats and yaks . Necropsy of dogs in the Datangma district indicated that there was no apparent significant change in the overall prevalence of E . granulosus in unwanted dogs after 5 years of 6-month praziquantel treatment . However , this was likely due to the number of dogs available for necropsy being too small to reflect the real situation prevailing . There was a highly significant decrease in Echinococcus prevalence after the 5-year treatment program shown by coproantigen-ELISA . This indicated a decreasing but continuing risk for re-infection of domestic and stray dogs . Genotyping of E . granulosus samples obtained from necropsied sheep/goats and yaks and from locally infected humans at surgery was carried out to determine the strain of parasite responsible for human infection . DNA genotyping indicated that only the sheep strain ( G1 ) of E . granulosus was present in the study area . Considerable re-infection rates of E . granulosus among dogs indicated a high infection pressure from infected livestock in this region , most likely from older animals dying on the pasture . A combination of livestock vaccination with the Eg95 vaccine , which is effective against the sheep strain of E . granulosus , and dog anthelmintic treatment , thus targeting two critical points of the parasite life-cycle , would avoid the conflicts of religion or local culture and could achieve the goal of hydatid control in the long term .
Cystic echinococcosis ( CE ) , caused by ingesting the eggs of the dog tapeworm Echinococcus granulosus is distributed worldwide in both humans and ungulates [1] , and is a major public health problem in western China [2] , [3] . E . granulosus has a two-host carnivore-prey life cycle , which commonly involves dogs and farm livestock . A common source of infection for dogs is offal from infected livestock . Two types of hydatid cysts can be observed in the various intermediate hosts: fertile cysts , in which brood capsules containing protoscoleces are both joined to the germinal layer and are free in the hydatid fluid filling the cyst cavity , and infertile cysts , which may not produce protoscoleces or are immature and are therefore unable to continue the life cycle of the parasite [4] , [5] . Tibetan communities in north-western Sichuan Province are hyper-endemic for echinococcosis [6] . Poor sanitation and hygiene , the Buddist doctrine of allowing old livestock to die naturally , plus the unrestricted disposal of animal viscera post-slaughter may be responsible for the high prevalence of human CE . As part of a large collaborative control program for CE , surveillance for Echinococcus infection in domestic dogs using a coproantigen method and necropsy of unwanted dogs was carried out in Datangma district , Ganzi County , which is situated in the west of Sichuan province , near the border with the Tibet Autonomous Region , prior to ( in 2000 ) and post- ( in 2005 ) 6-monthly dog anthelminthic treatment to determine the efficacy of the treatment for control . Necropsy of sheep/goats ( age <1 to 12 years ) and yaks ( age 4 years ) was undertaken to determine the baseline transmission pressure . As well , genotyping of E . granulosus samples obtained from necropsied sheep/goats and yaks and from locally infected humans at surgery was carried out to determine the genotype of parasite responsible for human infections . Previous examination of yaks ranging in age from 4 to 12 years old in the field and at local slaughter houses in Ganzi County , revealed a high prevalence of E . granulosus cysts of the common sheep-dog strain ( G1 genotype ) but no cysts with protoscoleces [7] . This is in contrast to other published abattoir records such as in western Iran [8] where fertile cysts were found in 75% of sheep and goats , 45% in cattle and 15% in buffaloes . Notably , the prevalence of human CE has been shown by ultrasound to be as high as 6% in Ganzi villages that are dedicated to pastoralism [9] , although as no other intermediate host animals were examined for the presence of hydatid cysts during the earlier survey , it was unclear which animals were responsible for infecting dogs [7] . Among E . granulosus , there is recognised genetic heterogeneity whereby different strains have been shown to have different host preferences , morphology and other biological characteristics , likely to impact on control [10] . Although the common domestic sheep-dog strain ( G1 genotype ) is responsible for most human infections , there is substantial evidence to indicate that other strains also infect humans [10] . Genotyping of E . granulosus samples obtained from necropsied sheep/goats and yaks , and from locally infected humans at surgery in Datangma district was carried out to determine the strain of parasite responsible for human infection .
Datangma district , Ganzi County is in the north of Ganzi Tibetan Autonomous Prefecture , Sichuan Province , and consists of four townships ( Cha-zha , Da-de , Ka-long and Cha-long ) ( Fig 1 ) . Pasturage is the only agricultural activity in Datangma because of the high altitude ( from 3900 m to 5000 m ) . Serious winter snow falls and summer flooding occur approximately every 5 years , and often lead to 50–70% loss of livestock in some villages/households . Within this study area , there are 89000 yaks , 25000 sheep , 8000 goats and 10000 horses ( 1999 records ) which provide a primary income source for the local nomads . Most families keep at least one guard dog , and there are also large numbers of ownerless , stray dogs . The main source of water is the Daqu ( moon ) river that crosses the district from northwest to southeast; it provides sufficient water for human and livestock consumption , but there is no formal drinking water system . The water in the river is not clean as various items including dung , and yak , sheep and even human cadavers are thrown into it . Most of the households collect water several times a day for personal use and drink it without boiling . The population is composed primarily of transhumant herdsmen , who move their animals between winter pasturelands , now associated with fixed settlements , and higher altitude summer pasturelands not associated with fixed settlements . Hygiene and sanitation are extremely poor . In addition , people live in close proximity to both owned and unwanted domestic dogs . All owned ( 4263 ) or stray dogs ( 1500–2000 ) in the study area were to be treated with praziquantel ( PZQ ) twice a year from the year 2000 onwards , at the end of spring and the end of autumn . It was not difficult to treat stray dogs because they were usually close to households or monastries and were always looking for food . Each dog received a 200 mg pill in tsampa ( a mixture of barley meal and ghee ) , and was observed to make sure that the pill was ingested . At a recommended dose of 5 mg/kg , the 200 mg pill is sufficient for a 40 kg dog , and even the large Tibetan mastiffs would not exceed this weight . The dog coproantigen ELISA detection method used both prior to and following treatment of all dogs in the study area has been described [11] . On six occasions ( Sept . /2000 , Sept . /2003 , May/2004 , Oct . /2004 , Apr . /2005 , Oct . /2005 ) , faecal samples were collected from dogs from 20 selected households from each of 29 villages surveyed . They were allocated a number ( 1–580 ) . Unwanted dogs were euthanized humanely by feeding them a pill that contained para-amino propiophenone ( PAPP ) . The small intestine was removed and opened longitudinally . After visual inspection of the surface of the intestine , the areas containing worms were placed in closed bottles of 0 . 85% ( w/v ) NaCl ( saline ) and vigorously shaken . Released worms were fixed in 4% ( v/v ) of 40% ( v/v ) formaldehyde in saline , either before or after relaxing overnight in tap water . E . granulosus worms were identified by morphology and were differentiated from E . multilocularis using light microscopy according to WHO Guidelines [12] . Other taeniid cestodes present in the necropsied dogs were identified based on established morphological criteria [13]–[15] . The only two Taenia species found were Taenia serials and T . pisiformis . It is possible that T . multiceps was present and could not be differentiated from T . serialis . However , there was no record in this region of T . multiceps metacestodes in sheep or goats , and also , surprisingly , no T . hydatigena or T . ovis . Yaks were observed after slaughter at Ganzi County abattoir . Sheep and goats were usually home-killed and were not available for necropsy inspection . The assistance of village and County cadres was sought in order to obtain young ( 4 years old ) and older aged yaks ( 17–18 years old ) or sheep and goats ( aged 10–12 years ) . Animals were killed humanely by their owners , according to the Tibetan Buddhist custom . The head was presented , together with lungs and liver , so that an estimate could be made of the age of the animal . Yaks were aged from horn growth rings while sheep were aged by inspecting the number of incisor teeth and the degree of tooth wear . Organs were palpated and macroscopic cysts removed . The organs were then sliced at 2 mm ( liver ) or 4 mm ( lungs ) intervals to discover any small cysts . Cysts were dissected from liver or lung tissue . The cysts were then incised , and inner membranes and any brood capsules containing protoscoleces were rinsed in saline and then fixed in 95% ( v/v ) ethanol . Fixed samples were transported to the Australian laboratory for genotype analysis . Echinococcus cysts from sheep , goats and yaks were obtained from the study area as described above . An individual isolate represents parasite material collected from a single hydatid cyst . Human cystic material from patients of Datangma district was collected during surgery at Ganzi County Hospital . All cystic samples were examined by microscopy and isolates from sheep , goats , yak and humans that had protoscoleces and/or germinal membranes present were analysed by DNA sequencing of the atp6 gene [16] . Chi-square values and 95% confidence intervals were calculated using Epi-Info ( Centres for Disease Control and Prevention , Atlanta , GA ) for analyses of prevalence . Comparisons between groups were performed using chi-square of Fisher's exact tests . The level of statistical significance was set at P = 0 . 05 , unless otherwise stated . This study was reviewed and approved by Sichuan CDC , Sichuan Provincial Ethics Committee and the Institutional Review Board of the Ethics Committee of the First Teaching Hospital of Xinjiang Medical University as well as the Tibetan community representatives . Written informed consent was obtained from all participants before commencement . All animal work was carried out with the approval of AgResearch New Zealand limited , Wallaceville Animal Research Centre Animal Ethics Committee .
Except for one yak isolate ( yak 3; small ( 2 cm ) cyst diameter , with no germinal membrane or protoscoleces present ) , all samples collected from animal intermediate hosts ( sheep , goats and yaks ) and humans were amenable to DNA typing by mitochondrial atp6 gene sequencing . If protoscoleces were not present , the typing was done on DNA extracted from germinal membrane . All samples were typed as the G1 genotype of E . granulosus; some minor mutations in the sequences obtained were apparent as shown in Table 4 . These substitutions were distributed across the length of the 513 bp fragment examined . The sequence analysis allowed the definition of 8 different types of atp6 sequence represented by variation at 13 different nucleotide positions listed in Table 4 . Of these , there were 6 isolates with variation at position 360 of the atp6 gene . Seven isolates had substitutions at positions 23 , 24 , 26 , 43 , 45 , 50 , 73 , 75 , 86 , 265 and 243 . Isolates with a silent substitution at position 360 originated only from animal hosts; none of the human isolates had the mutation . All the E . granulosus atp6 sequences obtained were translated into open reading frames thus eliminating the presence of pseudogenes .
The most reliable method for diagnosis of Echinococcus spp . in definitive hosts is by necropsy , because worm burdens can be accurately estimated and parasites collected for identification [17] . However , necropsy usually results in a biased sample , in that only unwanted dogs can be necropsied . Coproantigen detection of Echinococcus spp . in canine hosts has shown great promise [11] and provides a complementary method for diagnostic and surveillance purposes . Necropsy results prior to the start of the control program undertaken in the Datangma district study area in 2000 showed that only 23% of dogs had no worms , with prevalences for E . multilocularis and E . granulosus being 36% and 27% , respectively . Two Taenia species ( T . pisiformis and T . serialis ) with a similar transmission life-cycle to E . multilocularis ( hares necropsied around townships often had E . multilocularis cysts in their liver ) , were found in 32% of dogs , and these were probably acquired by animals eating infected hares or rodents . Praziquantel is currently the most effective anthelmintic available for echinococcosis control in carnivores [18] , [19] . Mathematical models have been developed to describe the transmission dynamics and more recently to simulate control options [20] , [21] . The lengthening of the treatment intervals to beyond the pre-patent period for Echinococcus spp . can be effective because the mean time to re-infection is often considerably longer than six weeks [20] , [21] . Necropsy of dogs in the Datangma district indicated there was no apparent significant change in the overall prevalence of E . granulosus in unwanted dogs after 5 years of 6-monthly praziquantel treatment . The intensities of infection also did not appear to differ between the two sampling periods . However , this was likely due to the number of dogs available for necropsy being too small to reflect the real situation prevailing . The highly significant difference in Echinococcus prevalence after the 5-year treatment program shown by the coproantigen-ELISA indicated a decreasing but continuing risk for re-infection of domestic and stray dogs , and therefore of livestock . In intermediate hosts , cysts of E . granulosus are usually detected at post-mortem abattoir examination of the viscera . Although this can provide important epidemiological data , which can be used to define likely echinococcal infection pressure [22]–[24] , the main disadvantage of the approach is that samples obtained at slaughterhouses are potentially biased because this material is not generally accessible to dogs . In Tibetan areas , some animals are slaughtered ( usually late autumn ) for meat , but most old animals are allowed to die naturally . It is very difficult to necropsy these animals so that information about the E . granulosus infection status of very old animals is hard to obtain due to the strong Buddhist religion and life-style . Interruption of the echinococcal parasite lifecycle in intermediate hosts has been shown to be important [25] . A vaccine for animal intermediate hosts with the Eg95 vaccine has been shown to be highly effective as an intervention against the dog/sheep ( G1 genotype ) strain [26]–[29] . The hydatid isolates examined here and previously from Ganzi indicated that only the G1 strain of E . granulosus is present and is responsible for human CE prevalence in this and other hyperendemic regions of North West China [3] , [6] , [7] , [9] , [16] , [30] . Some minor genetic polymorphism in the atp6 sequence was evident in a number of the isolates , with the most frequent change being a substitution ( G/A ) at position 360 compared with the G1 reference sequence in isolates collected from the majority of hosts except humans . Whether these polymorphisms in the atp6 sequence reflect any biological or public health relevance for control may require further investigation on a larger scale , since genetic variation may affect infectivity and pathogenicity of E . granulosus [10] , [31] , [32] . The genotyping results are highly relevant in terms of control as livestock vaccination with the Eg95 vaccine would not only be beneficial in increasing the value of livestock production , but would also prevent the transmission of the parasite to dogs . The vaccine only prevents new infections and does not eliminate cysts already present . Thus , it would take a number of years before all the previously infected livestock were removed from the population . A modelling study has suggested a high probability of success even if anthelmintic treatment is only given every 6 months to 60% dogs with as few as 60% of sheep vaccinated [25] . This illustrates the cumulative effect of controlling the parasite at more than one point in its life cycle and may indicate the most promising means of control for this region . A control strategy consisting of a combination of livestock vaccination by using Eg95 and routine anthelmintic dog treatment would be particularly useful in this low income rural area where CE is so highly endemic , where resources are scarce and continual reintroduction of E . granulosus through sylvatic cycles or from neighbouring counties , provinces or even countries are constant threats . Unfortunately the study environment also has high transmission of E . multilocularis , which accounts for approximately 50% of human infections . The Eg95 vaccine would not affect the transmission of E . multilocularis . This will make overall control of echinococcosis more difficult . Control of E . multilocularis will require greater attention to hygiene and to more frequent treatment of dogs with praziquantel . Also , until the coproantigen test can be made specific , surveillance using this test will include both species of Echinococcus . Nevertheless , there did appear to be some encouraging progress made during the 5 years of 6-monthly dog treatments in Datangma district . Many studies have illustrated that the development rate of E . granulosus cysts corresponds with the longevity of the intermediate host and that large cysts , 10–20 cm in diameter , are generally fertile [33] . In our observations , the frequencies of “potential-fertile” cysts increased in lesions with a diameter>21 mm in 100 four-year old yak necropsied . In the Tibetan region E . granulosus also appears to mature towards the end of the natural lifetime of its intermediate hosts . The fertile cysts were only found in sheep/goats around the age of 10–12 years ( Table 2 ) and in very old yaks , possibly because the drop in immunity associated with aging allows the cysts to mature and develop protoscoleces . This finding is significant for future hydatid control in Tibetan areas and it might seem logical to begin a control program by removing all old or unproductive animals . Such a recommendation , however , though good for the economics of pastoralism , may change the farming practices that Tibetans have established over a thousand years of grazing animals on the high plateau . The combination of livestock vaccination with dog anthelmintic treatment might be a more acceptable strategy than removal of stray dogs and old livestock . In this way , a control program would avoid the conflicts of religion or local culture , but could still achieve the goal of hydatid control in the long term . However , now that new epidemiological information has become available there are already suggestions that a program of hydatid control should include removal of old and unproductive animals [34] and ownerless stray dogs .
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Human cystic echinococcosis ( CE ) is highly endemic in Tibetan regions of Sichuan . As part of a control program for CE in Datangma district , Ganzi County , necropsy of strays and coproantigen-ELISA of all dogs was carried out prior to and post-drug treatment to determine the efficacy of the treatment for control . Examination of sheep/goats and yaks was undertaken to determine the baseline transmission pressure to dogs . The necropsy results indicated no apparent significant change in the overall prevalence of E . granulosus in unwanted dogs after 5 years of 6-month treatment . In contrast , there was a highly significant decrease in Echinococcus prevalence in domestic/stray dogs after the 5-year treatment program shown by coproantigen-ELISA . This indicated a decreasing but continuing risk for re-infection of dogs resulting from high infection pressure from the numerous infected domestic animals . DNA genotyping indicated the presence only of the sheep strain ( G1 ) of E . granulosus in the study area . A combination of livestock vaccination with the highly effective Eg95 vaccine and dog drug treatment , targeting two critical points of the parasite life-cycle , would avoid the conflicts of religion or local culture and achieve the goal of hydatid control in the long term in the area .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases/helminth",
"infections"
] |
2009
|
Echinococcus granulosus Infection and Options for Control of Cystic Echinococcosis in Tibetan Communities of Western Sichuan Province, China
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CD8+ T cells can exert both protective and harmful effects on the virus-infected host . However , there is no systematic method to identify the attributes of a protective CD8+ T cell response . Here , we combine theory and experiment to identify and quantify the contribution of all HLA class I alleles to host protection against infection with a given pathogen . In 432 HTLV-1-infected individuals we show that individuals with HLA class I alleles that strongly bind the HTLV-1 protein HBZ had a lower proviral load and were more likely to be asymptomatic . We also show that in general , across all HTLV-1 proteins , CD8+ T cell effectiveness is strongly determined by protein specificity and produce a ranked list of the proteins targeted by the most effective CD8+ T cell response through to the least effective CD8+ T cell response . We conclude that CD8+ T cells play an important role in the control of HTLV-1 and that CD8+ cells specific to HBZ , not the immunodominant protein Tax , are the most effective . We suggest that HBZ plays a central role in HTLV-1 persistence . This approach is applicable to all pathogens , even where data are sparse , to identify simultaneously the HLA Class I alleles and the epitopes responsible for a protective CD8+ T cell response .
Human T cell lymphotropic virus-type 1 ( HTLV-1 ) is an oncogenic retrovirus that infects between 10 and 20 million people worldwide . Of these infected individuals , 1–6% develop adult T cell leukaemia/lymphoma ( ATL/ATLL ) and a further 2 to 3% develop a variety of chronic inflammatory syndromes including HTLV-1-associated myelopathy/tropical spastic paraparesis ( HAM/TSP ) ; the rest remain lifelong asymptomatic carriers ( ACs ) of the virus . Most HTLV-1-infected individuals mount a large , chronically activated CD8+ T cell response to HTLV-1 and it is unclear why this fails to eradicate the virus . Furthermore , there is evidence for both protective [1]–[3] and pathogenic effects [4]–[7] of HTLV-1-specific CD8+ T cells . The attributes of a protective anti-HTLV-1 response in vivo are unknown , although specificity for the viral protein Tax is a strong candidate . There are good reasons to believe that a Tax-specific CD8+ response [8] may be particularly protective . Firstly , Tax is the immunodominant HTLV-1 antigen [9] , [10] . Secondly , HLA-A*02 , which is associated with protection in southern Japan [11] , binds several Tax epitopes [12] , notably Tax 11–19 , which is bound unusually strongly [13] . Thirdly , Tax is one of the first HTLV-1 proteins to be expressed and it has been shown , for HIV-1-infected cells in vitro , that CD8+ T cells specific to early viral proteins are particularly effective in viral control [14] . Finally , it has been shown that the selective pressure exerted on Tax is higher in asymptomatic carriers than in those that have developed HAM/TSP [15] . What constitutes an effective CD8+ T cell response is difficult to ascertain in any infection . Measurements of CD8+ T cell frequency , phenotype , function and specificity are informative but , because antigen load influences each of these factors , it can be difficult to ascertain if a particular immune profile is the cause or effect of good pathogen control [16]–[19] . An alternative approach is host genotype analysis . Polymorphisms in immune-related genes , particularly the HLA class I genes , have been associated with outcome in many human infections , notably Plasmodium falciparum , Mycobacterium tuberculosis , HIV-1 , HTLV-1 and Hepatitis B Virus infection . The benefit of a genotypic analysis is that the direction of causality is unequivocal; the drawback is that , in common with all “omics” approaches to identify biomarkers , mechanistic insight is limited . Provided linkage disequilibrium can be ruled out , class I associations imply that the protective effect is mediated by CD8+ T or NK cells . However , why one particular allele should be protective remains unclear and so a class I association provides no information about how to manipulate the immune response to enhance protection . The aim of this study was to develop a method to test the hypothesis that the effectiveness of an individual's HTLV-1-specific response and thus their proviral load and HAM/TSP risk was determined by the epitope binding properties of their HLA class I alleles . This method resulted in the identification of the viral protein HTLV-1 basic leucine zipper factor ( HBZ ) as a significant immunogenic target for both proviral load reduction and reduced disease risk . The HBZ gene was identified recently [20] , it is encoded by the complementary strand of the HTLV-1 genome and its promoter lies in the 3′ LTR rather than the 5′ LTR . Our approach is generally applicable to all pathogens , including those in which few epitopes have been identified experimentally .
Approximately 50 HLA class I-epitope pairs have been identified for HTLV-1 [12] , [21]–[23] ( mainly from the immunodominant protein Tax [24] in the context of A*02 ) ; this represents a small and non-random fraction of the ∼2200 nonamer epitopes that could be bound by the alleles of the Kagoshima cohort studied here ( Methods ) . Therefore we used epitope prediction software to systematically predict HTLV-1 epitopes . The epitope prediction software that we used has been extensively validated for a number of other organisms including HIV-1 where it has provided useful insight [25]–[31] , but because of the lack of experimental data , it has not previously been tested for HTLV-1 . To validate the epitope prediction software , we measured experimentally the binding affinity of 200 HTLV-1 peptide-allele combinations ( Table S1 in Supporting Information S1 ) . We found a strong positive correlation between experimental measurement and the theoretical prediction for each of the two epitope prediction methods used namely Metaserver and Epipred ( Metaserver: all P <0 . 00001 , Spearman's rank correlation; Fig . 1 . Epipred: all P <0 . 001 , Spearman's rank correlation; Fig . S1 in Supporting Information S1 ) . We conclude that these epitope prediction software packages accurately predict relative ( i . e . rank order ) HTLV-1 peptide binding affinities . Throughout this article figures in the main text are obtained using Metaserver , corresponding figures from Epipred are in Supporting Information S1 . All conclusions were replicated by both methods and by an alternative metric ( Supporting Information S1 ) . A number of associations between HLA class I alleles and proviral load or HAM/TSP risk in HTLV-1 infection have been identified in a population in southern Japan [3] , [32] . We compared the predicted HTLV-1 peptide-binding affinities of the two protective alleles , A*0201 and Cw*0801 , with those of the known detrimental allele , B*5401 ( Methods ) . Peptides from the HTLV-1 protein HBZ bound to HLA-A*0201 and Cw*0801 significantly more strongly compared to B*5401 ( P = 0 . 0002 , Wilcoxon-Mann-Whitney; Fig . 2 . Repeating the analysis with another protective allele from the A*02 family , namely A*0206 instead of A*0201 yielded identical conclusions P = 0 . 0007 , Wilcoxon-Mann-Whitney , data not shown ) . These P values needs to be treated with caution because the rank of the binding affinity of one HBZ peptide for A*0201 may not be independent of the rank of the binding affinity of a second peptide to A*0201 and similarly for Cw*0801 and B*5401 ( see Methods , independence of ranks ) . However , we also found that the difference in binding strength ( i . e . the rank of the top A*0201 binding peptide minus the rank of the top B*5401 binding peptide ) was significantly greater for HBZ than for other HTLV-1 proteins ( P <0 . 001 , binomial test ) . This statistic is based only on the top binding peptide so it does not assume different peptides have independent binding affinity ranks . Henceforth , we only considered the top binding peptide to avoid the potential problem of dependence ( Methods ) . Having established that the known protective HLA class I alleles code for molecules that bind to peptides from HBZ more strongly than the known detrimental allele , we examined peptide binding by all alleles in the Kagoshima cohort . We compared the predicted epitopes for asymptomatic carriers ( n = 202 ) and HAM/TSP patients ( n = 230 ) from the Kagoshima cohort . We predicted the HTLV-1 peptides bound most strongly by each individual , given their HLA class I types and then tested for differences between the two subject groups ( Methods ) . The results are shown in Table S2 in Supporting Information S1 . One result remained highly statistically significant after correction for multiple comparisons and was consistent across both prediction methods: asymptomatic carriers have HLA class I alleles that bind more strongly to peptides from HBZ compared to HAM/TSP patients ( Metaserver: P = 0 . 0002 , Wilcoxon-Mann-Whitney; Fig . 3 . Epipred: P <0 . 0001 , Wilcoxon-Mann-Whitney; Fig . S2 in Supporting Information S1 ) . To test whether this association was caused solely by the known protective and detrimental HLA allele families , the analysis for HBZ was repeated excluding A*02 and B*54 . The results showed that , amongst the HLA-A alleles , alleles from the A*02 family were responsible for the protective effect , whereas in HLA-B more than one allele family contributed significant effects . Overall , strong binding of HBZ peptides was associated with asymptomatic status , even when A*02 , B*54 and Cw*08 were excluded from the analysis ( Metaserver: P = 0 . 04 , Wilcoxon-Mann-Whitney . Epipred: P = 0 . 006 , Wilcoxon-Mann-Whitney; Table 1 ) . Next we investigated why strong binding of HBZ peptides was associated with remaining asymptomatic . One of the strongest correlates of HAM/TSP is a high HTLV-1 proviral load [33] . We therefore tested the hypothesis that strong binding of HBZ peptides was associated with a lower proviral load . The number of HLA class I alleles that each individual possessed that were predicted to strongly bind peptides from HBZ was plotted against their proviral load ( Methods ) . We found that the number of HLA Class I alleles that an individual had that strongly bound HBZ peptides was significantly negatively correlated with their proviral load ( Metaserver: P = 0 . 016 , Spearman's rank correlation; Fig . 4 . Epipred: P = 0 . 1 , Spearman's rank correlation; Fig . S3 in Supporting Information S1 ) . We tested this correlation independently in HAM/TSP patients and asymptomatic carriers and then combined the P values ( rather than simply testing the whole cohort ) , so this result does not follow trivially from our previous observation than asymptomatic carriers bind HBZ significantly more strongly than HAM/TSP patients . An alternative metric , the binding strength of the top HBZ-binding peptide to each allele instead of the number of strongly binding alleles , yielded an identical conclusion i . e . there was a significant negative correlation between the proviral load and the strength of binding to HBZ peptides ( Metaserver: P = 0 . 008 , Spearman's rank correlation . Epipred: P = 0 . 003 , Spearman's rank correlation ) . Next we compared our peptide-binding analysis of HLA class I genotype with a traditional frequency-based “presence or absence of an allele” analysis . Previously a “traditional” analysis yielded inconsistent results [3] , [32] , [34] . For example , A*02 was a significant predictor of load in ACs but not in patients with HAM/TSP . We therefore directly compared the ability of the novel peptide binding method and the traditional genotype method to predict proviral load in ACs and HAM/TSP patients ( Table S3 in Supporting Information S1 ) . This analysis showed that binding HBZ was a significant predictor of proviral load in both ACs and HAM/TSP patients ( P = 0 . 001 , P = 0 . 017 ) , but confirmed the finding that in a traditional analysis HLA-A*02 ( presence/absence ) was a significant predictor in ACs only ( P = 0 . 01 ) and HLA-B*54 for HAM/TSP patients only ( P = 0 . 019 ) . The proportion of variance in proviral load explained was also marginally higher for the peptide binding analysis than the traditional analysis . The observation that HBZ binding strength correlated with proviral load in both ACs and HAM/TSP patients suggests that peptide binding is the more fundamental predictor than HLA genotype . Our findings demonstrate that the HTLV-1 protein that is associated with the most significant reduction in HAM/TSP risk when bound by HLA class I molecules ( i . e . HBZ ) is also , independently , associated with a significant reduction in proviral load when bound . We wished to investigate whether this relationship held across all proteins . We therefore produced two ranked lists of proteins . In the first list we ranked the HTLV-1 proteins according to whether they were bound more strongly by asymptomatic carriers or HAM/TSP patients ( Fig . 5 x-axis; at the extremes ACs were significantly more likely to bind peptides from HBZ , HAM/TSP patients were significantly more likely to bind peptides from Env ) . This list could be viewed as the rank order of targets for a vaccine designed to reduce HAM/TSP risk . In the second list we ranked the proteins according to whether binding their peptides was associated with a lower proviral load ( Fig . 5 , y-axis; at the extremes , binding of HBZ was associated with a significantly lower proviral load , whereas binding of Env was associated with a significantly higher proviral load ) . This list could be viewed as the rank order of targets for a vaccine designed to reduce proviral load . We then compared these two sets of ranks and found them to be strongly positively correlated ( Metaserver: RS = 0 . 86 , P = 0 . 0005 , Spearman's rank correlation; Fig . 5 . Epipred: RS = 0 . 66 , P = 0 . 02 , Spearman's rank correlation; Fig . S4 in Supporting Information S1 ) . That is , proteins whose peptides are bound strongly by asymptomatic carriers are , independently , those associated with a lower proviral load when bound . This observation has two important implications . Firstly , HLA class I binding of peptides from different proteins has a differential impact on both proviral load and HAM/TSP risk; i . e . CD8+ efficiency ( ability to reduce proviral load and disease risk ) is determined by protein specificity and we have established a list of protein targets of the most efficient response to the least efficient response . Secondly , the fact that across all alleles and across all proteins , peptide binding associated with immune control ( reduced proviral load ) is strongly correlated with prevention of HAM/TSP is the strongest evidence yet that the CD8+ T cell response can have a beneficial role in HTLV-1 infection . We calculated the prevented fraction of disease attributable to the possession of one or more strong binding alleles to HBZ [3] ( Methods ) . This showed that the possession of strong HBZ-binding HLA alleles prevented ( Fp ) ≈48% ( 12 . 3% SD ) of potential cases of HAM/TSP in the study population . However , although we found that a high proportion of potential HAM/TSP cases are prevented by strong HBZ binding , it should be noted that the strength of HBZ binding is not the only determinant of disease status: in a logistic regression model , the strength of HBZ binding alone could only correctly classify 55% of cases of HAM/TSP . These results strongly imply that HBZ-specific CD8+ T cells play a protective role in HTLV-1 infection . HBZ immunogenicity has been studied in ATL patients [35] , [36] but it is unknown whether a HBZ-specific CD8+ T cell response is generated or even whether HBZ protein is expressed in asymptomatic carriers and HAM/TSP patients . We therefore sought to identify HBZ-specific CD8+ T cells in fresh PBMCs from HTLV-1 infected individuals . We assayed IFN-γ production by ELISpot following stimulation in vitro with a pool of overlapping peptides that spanned the entire HBZ protein . Of 45 subjects tested , 31% had detectable HBZ-specific CD8+ T cells ( Fig . 6 ) . An independent CD8+ T cell assay , ( CD107a mobilisation ) , confirmed that HBZ-specific CD8+ T cells are present in PBMC from infected individuals . We conclude that HBZ protein is expressed in vivo and is immunogenic . Recently , Sumeori et al established an HBZ-specific CD8+ T cell clone that recognised HBZ26–34 ( GLLSLEEEL ) in the context of HLA-A*0201 [37] . They showed that this clone was able to lyse an autologous B-lymphoblastoid cell line ( B-LCL ) that had been loaded with HBZ peptide but that cells from an ATL patient were resistant to killing . We investigated whether the same CD8+ T cell clone was able to kill naturally-infected cells from non-leukemic HTLV-I-infected individuals . First we confirmed the finding of Sumeori et al that autologous B-LCL loaded with HBZ26–34 peptide could be lysed by the CD8+ T cell clone ( data not shown ) . Then we demonstrated , by a classical chromium release assay , that naturally-infected CD4+CD25+ cells from the PBMCs of 3 out of 4 HLA-A*0201+ non-leukemic patients were lysed by the CD8+ T cell clone but that cells from 3 out of 3 HLA-mismatched donors were not lysed ( Fig . 7 ) . We conclude that naturally-infected cells from AC and HAM/TSP patients are susceptible to lysis by an HBZ-specific clone . How does the immunogenicity of HBZ compare to Tax ? We compared the predicted top binding peptide from HBZ and Tax respectively to 43 HLA class I alleles ( the maximum capacity of Metaserver ) . Peptides from Tax were predicted to bind significantly more strongly than peptides from HBZ ( P = 0 . 00002 , paired Wilcoxon-Mann-Whitney; Fig . 8A ) . Consistent with this prediction , the frequency of Tax-specific CD8+ T cells by IFN-γ ELISpot was also significantly greater compared to HBZ CD8+ T cells in 45 HTLV-1-infected individuals ( P = 0 . 000006 , paired Wilcoxon-Mann-Whitney; Fig . 8B ) .
We show that strong predicted binding of peptides from the HTLV-1 protein HBZ is associated with a reduced risk of HAM/TSP and a reduced proviral load in a population with endemic HTLV-1 infection in southern Japan . We demonstrated that protection is not limited to a small subset of HLA class I alleles previously associated with disease status and proviral load ( HLA-A*02 and HLA-Cw*08 ) , but is generally associated with HLA class I alleles that bind strongly to HBZ . Given that a protein-specific HLA-restricted association is more likely to be mediated by CD8+ T cells than NK cells which show limited protein specificity we interpret this work in the context of the CD8+ T cell response . Prior to this analysis , CD8+ T cells specific for the HTLV-1 protein Tax were often considered as the best candidate for ‘efficient’ or ‘protective’ CD8+ cells because of the immunodominance of Tax in the CD8+ T cell response [9] , [24] . Our finding that binding of HBZ peptides rather than Tax peptides is protective raises the question: why is HBZ a critical target for the immune response ? HBZ functions by binding to cellular factors of the JUN and ATF/CREB families [37] . There are two major splice variants of the HBZ transcript , SP1 and SP2; the variant SP1 is more abundant and is the variant used in this study [38] , [39] . The abundance of HBZ transcript has been previously correlated with disease severity [39] . Expression of HBZ suppresses Tax-mediated transactivation through the 5′ LTR [20] , [40] and thereby inhibits expression of other HTLV-1 genes [20] , [41]; HBZ can be expressed in the absence of transcription of other HTLV-1 genes . Additionally , HBZ RNA promotes the proliferation of infected T-lymphocytes [35] . This dual action – reduction of HTLV-1 expression and subsequent protection from immune surveillance , and enhancement of infected cell proliferation – probably confers a survival advantage on HBZ-expressing cells and is consistent with the observations that HBZ enhances persistence in HTLV-1 inoculated rabbits [41] and that ATL cells often have a hypermethylated or deleted 5′ LTR but an intact functional 3′ LTR [35] . We hypothesise that if HBZ-specific CD8+ T cells are weak or absent then infected cells that express HBZ but not other viral proteins will evade immune surveillance and proliferate rapidly , leading to an increase in proviral load . HBZ-specific CD8+ T cells would then play an important role in preventing this proliferation of provirus-positive cells and blocking this strategy of persistence . If this conclusion is correct that CD8+ T cell recognition of HBZ plays a central role in the control of HTLV-1 replication then one might expect that HBZ would have evolved to minimize class I binding . Consistent with this hypothesis , we find that the predicted binding affinity of HLA molecules to HBZ peptides is significantly weaker than that of Tax peptides and that the frequency of HBZ-specific CD8+ T cells is significantly lower than the frequency of Tax-specific CD8+ T cells . Although the low immunogenicity of HBZ is precisely what we predict given its central importance in maintaining HTLV-1 persistence , it is nevertheless striking that these low T cell frequency responses are so important . This result challenges the prevailing assumption that the immunodominant response to a pathogen is the most important . We demonstrated using two different assays ( IFNγ ELISpot and CD107 mobilisation ) that HBZ-specific CTL are present in PBMC from HAM/TSP patients and ACs . We further show that naturally infected cells , isolated directly from HAM/TSP patients and ACs , are susceptible to lysis by an HBZ-specific CTL clone . Suemori et al have previously reported that the same HBZ-specific CTL clone was unable to lyse leukemic cells isolated from a patient with adult T cell leukemia [37] . The observation that aleukemic but not leukemic cells can be lysed may be because leukemic cells express lower levels of HLA: HBZ peptide on their surface or because leukemic cells can be inherently harder to lyse [42]–[44] . This approach to studying the association between HLA class I genotype and the outcome of infection has a number of strengths compared with a traditional frequency-based analysis . Firstly , it is more mechanistic: knowing that binding HBZ is associated with a reduced proviral load and disease risk compared with knowing that A*02 is associated with these outcomes is a simultaneously more fundamental and more applicable level of understanding . Secondly , identification of protective epitopes immediately suggests a practical approach to measure and enhance , via therapeutic vaccination , the efficiency of an individual's anti-viral response . Thirdly , because the same effect ( e . g . HBZ binding ) can be identified for many alleles it is less likely to be a spurious result of linkage disequilibrium or genetic stratification . Finally , effects due to multiple low-frequency alleles can be captured because analysis is made at the level of peptide binding rather than allelic frequency . In summary , using a novel and generalizable approach , we have identified one of the constituents of an effective CD8+ T cell response in HTLV-1 infection .
We used two different algorithms to predict HLA class I epitopes: Metaserver and Epipred . Figures based on Metaserver predictions are in the main text , the corresponding figures for Epipred are in Supporting Information S1 . Other than our initial comparison ( protective against detrimental alleles ) , analysis was limited to A and B loci for two reasons: Metaserver does not have algorithms for the C loci and C loci predictors tend to be less accurate because of the lack of peptide-HLA-C experimental binding affinities to train the software . Metaserver provided coverage of 84% of the total count of A/B alleles in the Kagoshima cohort . The missing alleles were: A*0207 , A*0210 , A*2603 , A*3201 , B*1301 , B*1501 , B*1508 , B*1511 , B*1518 , B*2704 , B*3701 , B*3802 , B*4005 , B*4006 , B*4601 , B*4801 , B*5201 , B*5501 , B*5504 , B*5601 , B*5603 , B*5605 , B*5705 , B*5901 , and B*6701 . We were able to obtain predictions for [A*0207 , A*0210] , A*2603 and [B*4005 , B*4006] to a resolution of 2 digits by combining the predictions of other A02* , A26* and B40* predictors according to their frequency in Kagoshima . We estimate that approximately 2 , 200 peptides could be bound by the alleles present in the Kagoshima cohort . This figure is 1% [45] of the 3 , 389 overlapping nonamers of the HTLV-1 proteome multiplied by the number of unique alleles ( 65 ) in the cohort . The accuracy of epitope prediction algorithms has increased to such an extent that the correlation between predicted binding affinities and measured binding affinity is as strong as the correlations of measurements between different laboratories [50] . The specificity of epitope predictors has been tested by predicting a set of CTL epitopes and subsequently verifying CD8+ T cell responses against these epitopes experimentally . Using this technique has yielded true-positive ( correctly predicted ) estimates of 62–80% [51] . Using the more direct approach of mass spectrometry to determine HLA-peptide binding yielded a true positive rate of greater than 98% [52] . Additionally , we verified the prediction software we used ( Metaserver and Epipred ) for HTLV-1 peptides . Both prediction methods that we use produce a score for each peptide-HLA that represents the binding strength of that complex . In theory this score would allow us to compare predicted binding affinities between alleles . However , between allele comparisons can be problematic . Firstly , within-allele comparisons ( i . e . predictions for different peptides to the same allele ) are thought to be more comparable than predictions between alleles [45] . Secondly , whether or not a normalisation procedure should be applied for between-allele comparisons is still being debated in the community [48] . To avoid the potential problem of between-allele comparisons we used the rank measure technique introduced by Borghans et al . [53] in which she quantified the strength of peptide-HLA class I binding for peptides from a particular protein by ranking the binding score of peptides from the protein of interest to the allele amongst the binding score of peptides from the entire proteome to that allele; this approach has been successfully applied in the context of HIV infection [25] , [28] . Specifically , we split each protein in the HTLV-1 reference sequence into overlapping nonamers offset by a single amino acid . Using the epitope prediction software , a predicted binding affinity score was calculated for each of these peptides to each HLA allele of interest . For each allele we ranked all nonamers from the proteome from the strongest to weakest predicted binding scores . This produced a list of rank values for each protein to that particular allele that quantified the binding relationship between that allele and the protein ( an example is given in Table S4 in Supporting Information S1 ) . To check for robustness we also repeated all calculations using an alternative to the rank measure: the raw predicted affinity score . We found that our conclusions were robust to the choice of method ( Table S5 in Supporting Information S1 ) . We were concerned that the binding of the top 8 peptides from a protein to an allele may not be independent of one another . Since , the strength of the strongest binder provides information ( i . e . an upper bound ) about the strength of the second highest binder . For this reason , apart from Fig . 2 , only the top rank for each protein-allele pair was used . The REVEAL HLA-peptide binding assay ( ProImmune Ltd . , Oxford , UK ) was used to quantify peptide-HLA binding . For each allele-peptide combination that was tested , assembly of peptide-HLA complexes was quantified by ELISA with a conformation-dependent anti-HLA antibody . Samples of assembling peptide-HLA complexes were taken at a defined time point and snap-frozen in liquid nitrogen prior to analysis . The assembly for each peptide-HLA complex was then compared against a positive control peptide for that allele as the percentage of assembled peptide relative to that control . We selected four HLA class I alleles and 50 HTLV-1 peptides for each allele . The allele choice was based on allele frequency in the Kagoshima database and included 2 A alleles and 2 B alleles as well as alleles for which we knew that the epitope prediction tended to be poor . The 50 HTLV-1 nonamer peptides for each allele were selected to represent a range of predicted binding affinities , from weak to strong binding peptides . They originated from 4 HTLV-1 reference strain proteins: Tax , HBZ , Gag and Polymerase . Due to allele coverage ( see above ) , it was necessary to use Metaserver for A*0201 and B*5401 and Epipred for Cw*0801 . As the rank values were derived for each allele separately , it was acceptable to use different prediction methods for each allele in this case . Epipred predicts binding to allele families rather than individual alleles and so we calculated binding to Cw*08 . The ranks of the strongest binding 8 peptides from each protein to the alleles A*0201 and Cw*08 ( 16 rank values ) were compared against the ranks of the strongest binding 8 peptides to the allele B*5401 ( 8 rank values ) . A Wilcoxon-Mann-Whitney test was performed for each protein to test for differences between the two sets of rank values . The analysis was repeated using top 5 and top 10 as well as top 8 binding peptides , conclusions were robust to the choice of number of peptides ( Results in Supporting Information S1 ) . Finally , to avoid the potential problem of lack of independence of ranks ( see “independence of ranks” above ) we performed a binomial test on the difference in strength of binding of A*02 and B*54 to HBZ compared to all other HTLV-I proteins . The null hypothesis we tested was “the difference in binding of detrimental and protective alleles to HBZ is comparable to the other HTLV-1 proteins” . For each of the 12 HTLV-1 proteins we calculated the ranks of the single highest ranking peptide from that protein to A*02 and B*54 . We then calculated the difference of these two ranks ( detrimental – beneficial ) for each of the 12 proteins and asked , using the Binomial test , whether the difference in binding for HBZ was larger than would be expected under the null hypothesis . The analysis was carried out on each HTLV-1 protein in turn . For each individual in the Kagoshima cohort , the rank of the top binding peptide from the HTLV-1 protein to each of the individual's A and B HLA class I alleles was found ( see The Rank Measure ) . These ranks were then split into two groups – those from HAM/TSP patients and those from asymptomatic carriers ( AC ) . The two sets of ranks ( HAM/TSP vs . AC ) were then compared for each protein using a Wilcoxon-Mann-Whitney test ( null hypothesis: HAM/TSP patients and asymptomatic carriers bind the protein equally strongly ) . We considered each HTLV-1 protein in turn . Firstly , we split the cohort by disease status ( AC or HAM/TSP ) . Then , for each individual , we counted the number of alleles they possessed that were strong binders to the protein of interest and then tested for a correlation between the number of strong binders to the protein and proviral load using the Spearman rank correlation . A strong binding allele to a particular protein was defined as one that was in the top 40% of alleles . That is , the rank of the top binding peptide from the HTLV-1 protein to each of the individual's A and B HLA class I alleles was found ( see The Rank Measure ) . This set of rank values ( pooled HAM/TSP and AC ) was then ordered from highest to lowest rank and the alleles that were represented in the top 40% of these ranks were defined as strong binding alleles to that protein . Importantly , for each protein , we looked at the relationship between strength of binding and proviral load separately in HAM/TSP patients and ACs and then combined the P values using Fisher's combined test ( rather than simply looking at the relationship in the whole cohort ) . Therefore we could be confident that any relationship between protein binding and proviral load that we found did not follow trivially from a relationship between protein binding and disease status and the fact that asymptomatic carriers have a significantly lower load than HAM/TSP patients . Our alternative metric for this method used the Rank Measure to quantify the strength of binding of peptides from each HTLV-1 protein to each individual's A and B alleles . We then tested for any correlation between these values and the individuals' proviral load for HAM/TSP patients and asymptomatic carriers . All analysis was performed with two independent epitope prediction algorithms ( Metaserver and Epipred ) and with two different methods ( rank method , raw score method ) ; additionally an alternative approach to comparing protective v detrimental alleles ( based on the binomial test ) and to comparing proviral load with strength of binding were investigated . Conclusions were highly robust ( Table S5 in Supporting Information S1 ) . All statistical analysis was carried out using the R Project for Statistical Computing [54] . The tests were non-parametric with the exception of multiple linear regression . All P values reported are 2-tailed . Fisher's combined probability test was used to combine P values . General linear model analysis [55] was used to identify which factors were predictors of proviral load , either in ACs or patients with HAM/TSP . To calculate the prevented fraction ( Fp ) of disease [3] , [56] , we used a 2×2 contingency table . The entries in the four cells were as follows: a ( HAM/TSP , positive for protective genotype ) = 183 , b ( HAM/TSP , negative for protective genotype ) = 47 , c ( AC , positive for protective genotype ) = 181 , d ( AC , negative for protective genotype ) = 21 . The fraction ( Fp ) of potential cases of HAM/TSP in the population that is prevented by the protective genotype is given by Fp = ( 1−R ) ×[1− ( d×r1/b×r2 ) ] , where R = prevalence rate of HAM/TSP in the population ( estimated as 1% of the HTLV-1-infected population ) , r1 = a+b and r2 = c+d . Fp is approximately normally distributed: the standard deviation is given by SD ( Fp ) = ( 1−R−Fp ) ×√[ ( c/d×r2 ) + ( a/b×r1 ) ] . Peripheral blood mononuclear cells ( PBMC ) were isolated from whole blood from HTLV-1 infected individuals by density gradient centrifugation . PBMC from HLA A*0201+ and HLA A*0201− HTLV-1 infected individuals were depleted of CD8+ cells , then enriched for CD25+ cells using MACS beads ( Miltenyi Biotech , Germany ) , according to manufacturer's instructions . CD25+ cells were cultured for 16h to allow for viral antigen expression and presentation , then labelled with 51Cr by incubating for 1h in the presence of 50–100 µCi Na2CrO4 ( MP Biomedicals , USA ) . Labelled cells were washed extensively and placed in culture in triplicate ( 40 , 000 cells/well ) in the presence of defined ratios of HBZ-1 , a CTL clone which recognises HBZ 26–34 in the context of HLA A*0201 [37] , alone or in the presence of 5% Triton x-100 ( Sigma Aldrich ) . As a control , 51Cr labelled B-LCL ( autologous to the CTL clone ) were cultured at the same ratios , with and without 1 µM HBZ 26–34 peptide . After 4h , culture supernatants were harvested , placed on a scintillation plate , and 51Cr release was assayed using a beta counter . Total specific lysis was calculated using the following formula: [chromium release ( test well ) −chromium release ( no CTL control ) ]/ [chromium release ( Triton−100% lysis ) −chromium release ( no CTL control ) ]*100 , expressed as a percentage specific lysis of total cells . As not all CD4+CD25+T cells are infected , and thus do not represent targets for the CTL line , an estimate of specific lysis of infected cells was also calculated , making the conservative assumption that all the viral load is present in CD25+ cells , [57] , and that the CTL line only kills infected cells . Percentage infected cells lysed was calculated using the following formula: [Percentage total cells lysed]/[fraction of CD4+CD25+ cells that are infected i . e . provirus positive] . The reference strain is from [58] , with the exception of HBZ , which was identified more recently and described in [35] ( Supporting Information S1: HTLV-1 reference strain ) .
|
A large immune response to the retrovirus HTLV-1 does not always prevent HTLV-1-associated diseases . Indeed , it has been shown that CD8+ T cells may contribute towards the inflammatory disease associated with HTLV-1 infection . This observation has led to the hypothesis that it is the ‘quality’ of the immune response towards HTLV-1 that is important , and not simply a response in itself . Using a combination of computational and experimental methods we have investigated T cell ‘quality’ . We have found that specificity is an important determinant of CD8+ T cell quality with recognition of the viral protein HBZ enabling the host to make a more effective immune response . This approach can be used for other pathogens to identify what HLA class 1 alleles and the parts of the pathogen they bind to are responsible for a protective CD8+ T cell immune response . This work informs basic immunology: “what constitutes a protective CD8+ T cell response ? ”; vaccine design: “which antigens elicit the most effective response” and virology: “which viral proteins are key players in the strategy of persistence ? ” .
|
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"Abstract",
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"virology/immune",
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2010
|
HLA Class I Binding of HBZ Determines Outcome in HTLV-1 Infection
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The interaction among multiple microbial strains affects the spread of infectious diseases and the efficacy of interventions . Genomic tools have made it increasingly easy to observe pathogenic strains diversity , but the best interpretation of such diversity has remained difficult because of relationships with host and environmental factors . Here , we focus on host-to-host contact behavior and study how it changes populations of pathogens in a minimal model of multi-strain interaction . We simulated a population of identical strains competing by mutual exclusion and spreading on a dynamical network of hosts according to a stochastic susceptible-infectious-susceptible model . We computed ecological indicators of diversity and dominance in strain populations for a collection of networks illustrating various properties found in real-world examples . Heterogeneities in the number of contacts among hosts were found to reduce diversity and increase dominance by making the repartition of strains among infected hosts more uneven , while strong community structure among hosts increased strain diversity . We found that the introduction of strains associated with hosts entering and leaving the system led to the highest pathogenic richness at intermediate turnover levels . These results were finally illustrated using the spread of Staphylococcus aureus in a long-term health-care facility where close proximity interactions and strain carriage were collected simultaneously . We found that network structural and temporal properties could account for a large part of the variability observed in strain diversity . These results show how stochasticity and network structure affect the population ecology of pathogens and warn against interpreting observations as unambiguous evidence of epidemiological differences between strains .
Interactions between strains of the same pathogen play a central role in how they spread in host populations . [1–7] . In Streptococcus pneumoniae and Staphylococcus aureus , for instance , several dozen strains can be characterized for which differences in transmissibility , virulence and duration of colonization have been reported in some cases [8 , 9] . Strain diversity may also affect the efficacy of prophylactic control measures such as vaccination or treatment . Indeed , strains may be associated with different antibiotic resistance profiles [3 , 5 , 10 , 11] , and developed vaccines may only target a subset of strains [2 , 3 , 12] . With the increasing availability of genotypic information , it has become easy to describe the ecology of population of pathogens and to monitor patterns of extinction and dominance of pathogen variants [13–17] . However , the reasons for multi-strain coexistence patterns ( e . g . coexistence between resistant and sensitive strains ) or dominance of certain strains ( e . g . in response to the selection pressure induced by treatment and preventive measures ) remain elusive . One may invoke selection due to different pathogen characteristics , but also environmental and host population characteristics , leading to differences in host behavior , settings and spatial structure may affect the ecology of strains [14–19] . In particular , human-to-human contacts play a central role in infectious disease transmission [20] . This is increasingly well described thanks to extensive high-resolution data—including mobility patterns [21–23] , sexual encounters [24] , close proximity interactions in schools [25 , 26] , workplaces [27] , hospitals [16 , 28–31] , etc . —that enable basing epidemiological assessment on contact data with real-life complexity [32 , 33] . For instance , the frequency of contacts can be highly heterogeneous leading more active individuals to be at once more vulnerable to infections and acting as super-spreaders after infection [24 , 33–35] . Organizational structure of certain settings ( school classes , hospital wards , etc . ) and other spatial proximity constraints lead to the formation of communities that can delay epidemic spread [36 , 37] . Individual turnover in the host population is also described as a key factor in controlling an epidemic [20 , 38] . It is likely that , since they impact the spread of single pathogens , the same characteristics could affect the dynamics in multi-strain populations . It was shown , indeed , that network structure impacts transmission with two interacting strains [39–46] , the evolution of epidemiological traits [47–49] and the effect of cross-immunity [50 , 51] . Yet in these cases , complex biological mechanisms—such as mutation , variations in transmissibility and infectious period , cross immunity—were used to differentiate between pathogens , thereby making the role of network characteristics difficult to assess in its own right . For this reason , we focused on the dynamical pattern of human contacts and examined whether it contributes to shaping the population ecology of interacting strains under minimal epidemiological assumptions regarding transmission . We described a neutral situation where all strains have the same epidemiological traits and compete via mutual exclusion ( concurrent infection with multiple strains is assumed to be impossible ) in a Susceptible-Infected-Susceptible ( SIS ) framework . We studied the spread of pathogens in a host population during a limited time window , disregarding long-term evolution dynamics of pathogens . More precisely , new strains were introduced through host turnover rather than de novo mutation or recombination in pathogens . We quantified the effect of network properties on the ecological diversity in strain populations with richness and dominance indicators . We assessed in turn heterogeneities in contact frequency , community structure and host turnover by comparing simulation results obtained with network models exhibiting a specific feature . We then interpreted S . aureus carriage in patients of a long-term care facility in the light of these results .
We simulated the stochastic spread of multiple strains on a dynamical contact network of individuals ( nodes of the network ) . Individuals can be either susceptible or infected with a single strain at a given time , and , for each strain , β and μ indicate the transmission and the recovery rate respectively . We assumed turnover of individuals , who enter the system with rate λin , and associated injection of previously unseen strains , carried by incoming individuals with probability ps . We considered synthetic network models , each displaying a specific structural feature , as well as a real network reconstructed from close-proximity-interaction data collected in a hospital facility . We calibrated all network models to the same average quantities—average population size V ¯ , fraction of active nodes a ¯ , average degree k ¯ and strength of the community repartition pIN , when applicable—that were chosen to correspond with the hospital network used in the application . Epidemiological parameters were motivated by the duration of S . aureus carriage in patients . A larger range of values was explored in some cases to address their impact on the dynamics . We analyze the structure of strain population at the dynamic equilibrium by computing , for each network model , ecological diversity measures , including species richness and evenness/dominance indices [52 , 53] . All details about network models , numerical simulations and ecological indicators are described in the Materials and methods section . In order to probe the effect of contact heterogeneity on strain ecology we compared a homogeneous model ( HOM ) in which all nodes have the same activity potential , i . e . they have equal rate of activation to establish contacts , with a heterogeneous model ( HET ) , akin to the activity-driven model described in [34] , where the activity potential is different across nodes and is drawn from a power-law distribution . Fig 1 shows the results of numerical simulations comparing HOM and HET models . Sample epidemic trajectories are reported in Fig 1A . Here every strain is indicated with its own color to display its dynamics resulting from the interaction with the other strains . Fig 1B–1D shows summary statistics in varying strain transmissibility β . The prevalence presents a well-known behavior for both static and dynamic networks ( Fig 1B ) : contact heterogeneities lower the transmissibility threshold above which total prevalence is significantly above zero , thus allowing the spread of pathogens with low transmissibility . At the same time , however , heterogeneities hamper the epidemic spread when β is large , reducing the equilibrium prevalence [35] . Fig 1C shows the average richness , i . e . the number of distinct strains co-circulating . For low values of β HET displays larger richness values compared to HOM . This trend reverses as β increases , and the richness is lower in HET consistently with the lower level of prevalence . The relation between richness and prevalence , however , is not straightforward . For instance , the reduction in richness for high β values is important even for the case with limited contact heterogeneity , when prevalence is barely affected . The scaling between prevalence and richness is not linear as β varies ( Fig 1D ) , and the relation between the two quantities varies appreciably among contact networks . In correspondence of a fixed value of prevalence , heterogeneous networks have lower richness—e . g . a prevalence value of ∼0 . 8 corresponds to ∼20% lower richness in HET with respect to HOM , as highlighted in Fig 1D . This fact can be explained by the dynamical properties of epidemics on heterogeneous networks . Active nodes , involved in a larger number of contacts , get infected more frequently [35] . Also , a randomly chosen node is likely surrounded by active nodes [33] . As a consequence , injected strains often find their propagation blocked by active infected nodes . In this way , contact heterogeneities enhance the competition induced by mutual exclusion and hamper the wide-spread of emerging strains , similarly to what was found in [46] . This mechanism is further confirmed by looking at the persistence time of strains ( S2 Fig in the supporting information ) . Above the epidemic threshold , it is on average shorter in heterogeneous networks than in homogeneous ones . The distributions are however more skewed in heterogeneous networks , indicating that more strains are going extinct rapidly , while a few others can survive for a long time in the population . If on the one hand hubs accelerate the extinction of certain strains , on the other they act as super-spreaders , amplifying the propagation of other strains . We find that this impacts profoundly the distribution of strains’ abundances , i . e . the strain-specific prevalence . Fig 2A shows that the latter is broader for the HET network , with the most abundant strain reaching a larger proportion of cases . This situation is synthesized by the Berger-Parker index , that quantifies the level of unevenness or dominance of a given ecological system [52 , 53] . This is defined as the relative abundance of the most abundant strain ( see Materials and methods section ) . Fig 2B shows that Berger-Parker index increases with β for all networks . This is expected since at low β strains′ transmission chains are short and barely interact , while they interfere more at higher values of transmission potential . The Berger-Parker index is always higher in a heterogeneous network , even when the comparison is made at fixed values of richness ( Fig 2C ) . An alternative indicator , the Shannon evenness , shows a similar behavior as displayed in S3 Fig . The fraction of strains going extinct also depends on stochastic effects in a finite size population . We indeed found that increasing network size , when temporal and topological properties were the same , led to an increase in both persistence time and richness ( S4 Fig ) . This shows that interference among transmission chains is reduced in larger populations . However , the relative abundance distribution remained similar , showing that it is primarily affected by the nodes’ activity distribution ( S5 Fig ) . Eventually , we tested whether additional mechanisms of strain injection were leading to different results . In S6 Fig we assumed new strains to infect susceptible nodes already present in the system with rate qs , mimicking in this way transmissions originating from an external source , as it can happen in real cases . The plot of S6 Fig shows the same qualitative behavior described here . We considered a community model ( COM ) with nC communities in which all nodes are as active as in HOM , but direct a fraction pIN of their links within their community and the rest to nodes in the remaining nC − 1 communities . The closer pIN is to 1 , the stronger the repartition in communities is . Fig 3A and 3B shows that a network with communities displays a higher richness for large β; even when community structure barely affects prevalence ( Fig 3B ) . However , the effect is important only when communities are fairly isolated ( pIN = 0 . 99 ) and the injection from the outside is not so frequent—otherwise the effect is masked by strain injection which occurs uniformly across communities . In particular , for the values of pIN = 0 . 78 and ps = 0 . 079 , chosen to match the hospital application , the difference with the homogeneous case is very small . The limited role of community structure is also confirmed by the fact that once this feature is combined with heterogeneous activation—in a model with the activation scheme of HET and the stub-matching of COM—the latter property has the dominant effect and the richness decreases ( S1 Fig ) . The relation between richness and prevalence remains the same when adding the injection of new strains due to the transmission from an external source . This mechanism further increases the richness . When β is high and the fraction of infected nodes is close to one , however , such a mechanism is hindered by the fact that susceptible nodes , that can get infected from the external source , are rare ( see S6 Fig ) . This is why richness starts to decrease for high values of β . We tested the consequences of communities in strain dominance by plotting the Berger-Parker index in Fig 3C . For low β , the behavior of the Berger-Parker index follows the trend in richness . The initial decrease in this indicator is due to the increase in richness , that occurs at constant prevalence and is thus associated to a decrease in the average abundance [54]—green curve corresponding to pIN = 0 . 99 and ps = 0 . 01 . At larger values of β , instead , increased competition levels induced higher dominance levels . The increase in strain diversity is due to the reduced competition among strains introduced in different communities . When coupling among communities is low , indeed , strains may spend the majority of time within the community they were injected in , thus avoiding strains injected in other communities . Fig 3D confirms this hypothesis by showing the Inverse Participation Ratio ( IPR ) [55] that quantifies uniformity in the repartition of abundance across communities . Values close to zero indicate uniform repartition , while , conversely , values close to 1 indicate that , on average , a strain is confined within a single community for most of the time ( more details are reported in the Materials and methods section ) . The strength of the community structure does not affect the repartition of the total prevalence ( squares in the plot ) , however it alters the average IPR value computed from the abundance of single strains , thus strains become more localized as pIN increases . Notice that a certain degree of localization is present also in the homogeneous network , due to those strains causing very few generations before going extinct . As a sensitivity analysis we tested whether the main results obtained so far are the same in a more realistic situation where additional heterogeneous properties of nodes are accounted for . We consider the case in which infectious duration varies across individuals , as happens for S . aureus colonization . S7 Fig shows that the inclusion of three classes differing in recovery rate reduces richness and increases the Berger-Parker index with respect to the homogeneous recovery . However , the effects discussed so far—e . g . reduction and amplification of richness in HET and COM , respectively—are still present . Node turnover represents another important property of a network that may impact the ecological dynamics of strains for two reasons: incoming individuals contribute to richness by injecting new strains; on the other hand , the removal from the population of infected nodes breaks transmission chains and hampers the persistence of strains . The result of the interplay between these two mechanisms is summarized by the plot of richness as a function of β and node length of stay , τ , —Fig 4A . The figure , obtained with the HOM model , shows two distinct regimes . In the former case , richness decreases as τ increases , because replacement of individuals becomes slower and injections less frequent . In the high β regime , instead , the average richness at fixed β does not depend monotonically on the node turnover but it is instead maximized at intermediate τ . Interestingly , the optimal value of τ decreases as β increases . This behavior can be explained by looking at the balance between injection and extinction that determines the equilibrium value of richness , N ¯ S . This reads [56]: N ¯ S = λ inp s T pers ( β , τ ) = V ¯ p s T pers ( β , τ ) τ , ( 1 ) where λinps is the rate at which new strains are introduced and Tpers is the average persistence time of a strain . The trade-off between injection and extinction appears as the ratio between the two time scales , Tpers and τ . In the limit τ → 0 the spread plays no role , even for high β . As τ increases , newly introduced infectious seeds have a higher probability to spread , thus the average extinction time initially increases super-linearly with τ ( see S8 Fig in the supporting information ) resulting in an increase of richness . However , past a certain value of τ , Tpers does not grow super-linearly anymore , thus a further increase in τ is detrimental for pathogen diversity because it is associated to fewer introductions . This general behavior was not altered by the accounting for introductions by transmissions from an external source as shown in S6 Fig . We derive an approximate formula for Tpers considering an emerging strain competing with a single effective strain formed by all other strains grouped together . This formulation , enabled by the neutral hypothesis , makes it possible to write the master equation describing the dynamics and to use the Fokker-Planck approximation to derive persistence times ( see Materials and methods section ) . Analytical results well reproduce the behavior observed in the simulations , and , in particular , the value of the length of stay maximizing richness for different β as shown by the comparison between white stars and continuous line in Fig 4B . The quantitative match for other values of ps is reported in S9 Fig . Unlike richness , Berger-Parker index always increases monotonically with the length of stay—Fig 4B . This behavior is due to the correlation of this indicator with average abundance , similarly to what we discussed in the previous section . We conclude by analyzing the real-case example of the S . aureus spread in a hospital setting [10 , 57] . We used close-proximity-interaction ( CPI ) data recorded in a long-term health-care facility during 4 months by the i-Bird study [16 , 28 , 31] . These describe a high-resolution dynamical network whose complex structure reflects the hospital organization , the subdivision in wards and the admission and discharge of patients [58] . Together with the measurements of contacts , weekly nasal swabs were routinely performed to monitor the S . aureus carriage status of the participants and to identify the spa-type and the antibiotic resistance profile of the colonizing strains . The modeling framework considered here well applies to this case . The SIS model is widely adopted for modeling the S . aureus colonization [59 , 60] , and the assumption of mutual exclusion is made by the majority of works to model the high level of cross-protection recognized by both epidemiological and microbiological studies [61 , 62] . The dynamic CPI network was previously shown to be associated with paths of strain propagation [16] . Consistently , we assumed that transmission is mediated by network links with transmissibility β . In addition , new strains are introduced in the population carried by incoming patients , or through contacts with persons not taking part in the study . Fig 5A shows weekly carriage and its breakdown in different strains . Prevalence and richness fluctuate around the average values 87 , 3 ± 6 , 3 cases and 39 , 8 ± 2 strains , respectively . Simulation results are reported in Fig 5B , that displays the impact of transmission and introduction rate on richness and prevalence . When qs is low we find a positive trend between richness and prevalence , consistently with the synthetic case . For larger values of qs the trend appears instead different . As transmissibility increases , richness initially grows with prevalence and then decreases after a certain point . This behavior is the same as observed in S6 Fig and stems from the reduction of susceptible nodes , that causes a decline in the expected injection rate—see Materials and methods section . To quantify the effect of contact patterns on S . aureus population ecology we compared simulation results with the ones on a network null model . Specifically , we built the RAND null model that randomizes contacts while preserving just the first and the last contact of every individual . The randomization preserves node turnover , the number of active nodes and links and destroys contact heterogeneities and community structure along with other correlations . Fig 5C shows the comparison for different transmissibility values . The effect of the network is consistent with the theoretical results described for a heterogeneous network , i . e . smaller richness values correspond to the same prevalence in the real network compared to the homogeneous one . We then quantified the level of dominance of the multi-strain distribution by means of the Berger-Parker index . We chose for each network the values of qs and β that better reproduce empirical richness and prevalence and , interestingly , we found that , for the two cases , same average richness and prevalence correspond to different levels of Berger-Parker index . The Berger-Parker index obtained with the real network is the highest and the one that better matches the empirical values—i . e . the empirical values are within one standard deviation of the mean for almost all weeks . Based on this result we argue that contact heterogeneities , along with the other properties of the contact network , contribute to the increased dominance of certain strains .
Multiple biological and environmental factors concur in shaping pathogen diversity . We focused here on the host contact network and we used a minimal transmission model to assess the impact of this ingredient on strain population ecology , quantifying the effects of three main network properties , i . e . heterogeneous activity potential , presence of communities and turnover of individuals . Results show that the structure and dynamics of contacts can alter profoundly strains’ co-circulation . Contact heterogeneities were found to shape the distribution of strains’ abundances . Highly active nodes are known to play an important role in outbreak dynamics by acting as super-spreaders [33] . At the same time , however , they were found to enhance the interference between the transmission chains of different strains , thus hindering the spread of an emerging variant [46] . Here we showed that the combination of these two dynamical mechanisms reduces the richness and increases the level of heterogeneity in strains’ abundances . In particular , hubs could allow strains with no biological advantage to generate a large number of cases and outcompete other equally fit strains . This mechanism may potentially bias the interpretation of biological data . Dynamical models that do not properly account for contact structure could overestimate the difference in strains’ epidemiological traits in the attempt to explain observed fluctuations in strain abundance induced in reality by super-spreading events . Moreover , these models could provide biased assessment of transmission vs . introduction rates . The presence of communities causes the separation of strains and mitigates the effect of competition thus enhancing co-existence . A similar behavior was already pointed out before [46 , 51 , 59 , 64] , e . g . for the spread of S . pneumoniae , as induced by age assortativity [64] , for the case of S . aureus where distinct settings were considered [59] , and for a population of antigenic distinct strains in presence of cross-immunity [51] . We found that the impact of community structure is not so strong , and it is likely minor when individuals of different communities have frequent contacts . No appreciable variation was observed , indeed , for pIN = 0 . 78 , chosen to match the inter-ward coupling of the hospital network . Similar results can be expected for school classes or workplace departments presenting a similar level of community mixing . The effect on richness becomes appreciable for low community coupling ( e . g . pIN = 0 . 99 in Fig 3 ) . This is consistent with a certain degree of diversity observed among strains belonging to separated communities , as it is the case of different hospitals [15] . Eventually , the analysis of turnover of individuals revealed major effects on strain diversity , when this mechanism is also the main driver of the introduction of strains in the population . When transmissibility is low richness decreases with host length of stay . When transmissibility is above the epidemic threshold we showed the existence of an optimal value of the length of stay that maximizes strain richness as a result of the interplay between two competing time scales , namely the typical inter-introduction time and the average persistence time of a strain . This provides insights for the spread of bacterial infections in transmission settings , such as hospitals or farms , that are of particular relevance for the spread of antimicrobial resistance and that are characterized by a rapid host turnover [15 , 31 , 65] . For the case of hospitals , for instance , they suggest that variations in patients’ length of stay , as induced by a change of policy , could have appreciable effects on the population structure of nosocomial pathogens . We adopted a neutral model to better disentangle the relative role of the different network properties . A wide disease-ecology literature addressed the consequences of neutral hypotheses on multi-strain balance in order to provide a benchmark for interpreting the observed co-existence patterns and gauging the effect of selective forces potentially at play [11 , 18 , 66 , 67] . Many of these works addressed , for instance , the co-existence between susceptible and resistant strains of S . pneumoniae [11 , 66] . However , this assumption was rarely adopted in network models , that consider for the majority strains with different epidemiological traits with the aim of describing pathogen selection and evolution [47–49 , 68] . Strains were assumed to have the same infection parameters in [50 , 51] , where the role of community structure and clustering was analyzed in conjunction with cross-immunity . With respect to these works , the minimal transmission model used here enabled a transparent comprehension of the role of the network . Multiple identical SIS processes can be mapped , in fact , on a single SIS process , in such a way that the wide literature of single SIS processes allows for a better understanding of the behavior recovered in the simulations [32 , 33] . Strains can be also grouped in two macro-strains . This strategy allowed us to adopt the viewpoint of an emerging strain and study its competition with the others seen as a unique macro-strain . The associated master equation and Fokker-Planck approximation allowed computing the average extinction time , capturing the key aspects of the dynamics . In a future work this theoretical framework could be extended to consider other network topologies . It could , for instance , be coupled with the activity-block approximation to describe heterogeneous networks . Additional numerical analyses , based on a similar transmission model , could also address other properties known to alter spreading dynamics , such as heterogeneous inter-contact time distribution or topological and temporal correlations . As a case study , we analyzed the spread of S . aureus in a hospital taking advantage of the simultaneous availability of contact and carriage information [16] . The temporal and topological features of the network lead to a lower prevalence and richness with respect to the homogeneous mixing ( although the effect was quite small ) . In addition , similar prevalence and richness values are associated to different dominance levels in different networks—i . e . different values of the Berger-Parker index—with the real network leading to a higher dominance as observed in reality . This behavior can be explained by the theoretical results and can be attributed essentially to the effect of contact heterogeneities , considering that the community structure does not have appreciable effects for this network , as discussed above . The importance of accounting for host contacts and hospital organization in the assessment of bacterial spread and designing interventions has been recognized by several studies [16 , 28–31 , 63] . Here we show that this element may be critical also for understanding the population ecology of the bacterium . It is important to note however that , while the realistic network provides results that are closer to the data , this ingredient explains only part of the heterogeneity observed in the abundance . This shows that the contact network is a relevant factor , but other factors should be considered as well . The approach used here is intentionally simplified , as we focused on the main dynamical consequences of the contact network . Clearly , more detailed models can be designed to reproduce more closely the data . A certain degree of variation in the epidemiological traits could be at play , as for example the fitness cost of resistance [8] . Role of hosts in the network ( e . g . patients vs . health-care workers ) , and heterogeneities in health conditions , antibiotic treatment and hygiene practices are also known to affect duration of carriage and chance of transmission [16 , 28 , 31 , 63] . Eventually , we must consider that the comparison of model output with carriage data is also affected by the limitation of the dataset itself , already described in [16] . In particular , the weekly swabs may leave transient colonization undetected . Moreover , while the relevance of CPIs as proxies for epidemiological links has been demonstrated [16] , the transmission through the environment ( e . g . in the form of fomites ) is also possible . The understanding provided here can be relevant for other population settings , temporal scales and geographical levels . In addition , the modeling framework could be applied to pathogens other than S . aureus , such as human papillomavirus , S . pneumoniae and Neisseria meningitidis , for which the strong interest in the study of the strain ecology is justified by the public health need for understanding and anticipating trends in antibiotic resistance , or the long-term effect of vaccination [1 , 2 , 4 , 5] . With this respect , if the simple framework introduced here increases our theoretical comprehension of the multi-strain dynamics , more tailored models may become necessary according to the specific case . In particular , we have considered complete mutual exclusion as the only mechanism for competition . In reality , a secondary inoculation in a host that is already a carrier may give rise to alternative outcomes , such as co-infection or replacement [69] . In addition , infection or carriage may confer a certain level of long-lasting strain-specific protection and/or a short-duration transcendent immunity [11 , 50] . Eventually mechanisms of mutation and/or recombination are at play and their inclusion into the model can be important according to the time scale of interest .
We provide here details of the generative algorithms used for the contact network models . Network dynamics is implemented in discrete time according to the following rules common to all models: Turnover dynamics: new nodes arrive according to a Poisson process with rate λin and leave after a random time drawn from an exponential probability distribution with average τ . After a short initial transient , population size is Poisson distributed with average V ¯ = λ in τ . Upon admission , a node i is assigned with an activity potential ai , i . e . an activation rate , drawn at random from a given probability distribution P ( a ) . Any node retains this property throughout its whole lifespan . Activation Pattern: each node i becomes active with rate ai . It then receives a number of stubs drawn from a zero-truncated Poisson distribution with parameter κ—we require active nodes to engage in at least one contact . The average number of stubs , computed among active nodes , is thus given by κ/ ( 1 − e−κ ) , and the average degree can be computed by the latter quantity multiplied by the average activity potential . The active status lasts for a single time step . Stub-matching: stubs are then matched according to the actual model considered . We now describe in detail each network model: HOM: in this model each node has the same probability aH to be active during each time step; the activity distribution is thus P ( a ) = δ ( a − aH ) , where δ ( x ) is the Dirac’s delta function . Stubs are matched completely at random in order to form links , according to a configuration model [33] . We discard eventual self-links and multiple links that may occur during the matching procedure . HET: here each node i has its own activity rate ai , drawn from a power-law distribution P ( a ) ∝ a−γ , with a ∈ ( ϵ , 1] . We tune the variance by varying γ—lower γ higher variance . We then set ϵ to have the average activity a ¯ equal to aH in HOM . Stub-matching procedure is the same as in HOM . HET model is thus a variant of the activity driven model introduced in [34] with the difference that here contacts are created only among active individuals . COM: incoming nodes are assigned to one among nC communities with equal probability—so that communities have the same size on average—and belong to the same community throughout their whole lifespan . Stubs are matched according to the community each node belongs to . Precisely , any stub is matched either with another stub of the same community , with probability pIN , or with a stub of a different community , with complementary probability . Here the stub-matching procedure results in a larger number of lost links—to eliminate multiple links and self-loops—compared to HOM and HET , due to the difficulty in matching stubs within small groups . Thus , the parameter κ has to be adjusted manually to recover the same average degree as in HOM and HET . Each node has the same activity potential aH as in HOM . We use a dynamical contact network obtained from CPI data collected during the i-Bird study in a French hospital . Details of the network are already reported in [16] . Briefly , the dataset describes contacts occurring between 592 individuals from July to November 2009 . The study involved both patients and health-care workers , distributed in 5 wards , as well as hospital service staff . Every participant wore a wireless device designed to broadcast a signal every 30 s containing information about its ID . Signal strength was tuned so that only devices within a small distance ( around 1 . 5 m ) were able to register a contact . CPIs were finally aggregated daily , keeping the information about their cumulative duration within each day . We discard CPIs relative to the first 2 weeks and the last 4 weeks of dataset , corresponding to a period of adjustments in the measurements and progressive dismissal of the experiment , respectively . Simulations conducted with the CPIs network were compared with results obtained with a null model which we refer to as RAND . According to this randomization scheme the activity of a node is randomized while respecting the constraint that removal and addition of contacts must not alter the time of the first and the last contact of each node ( tS and tL respectively ) . Notice that RAND preserves the number of nodes that are present at any time in the network by preserving their first contact tS and their length of stay tL − tS . Null models randomizing the latter properties lead to misleading results when node length of stay is heterogeneous and node turnover occurs [70] . RAND also sets all contact weights equal to the average weight value . Spreading dynamics is stochastic and is performed in discrete time . At each time step of duration Δt , we update the state of each node: each infected node transmits the strain it is carrying to a susceptible neighbor with probability βΔt and it turns susceptible with probability μΔt . Notice that due to mutual exclusion , an individual can be infected by a single strain at a time [71] . Strain injection is given by the combination of two processes: incoming individuals bring a new strain with probability ps , and susceptible individuals turn infectious with a new strain with probability qsΔt . The two mechanisms mimic respectively incoming infectious individuals ( e . g . admission of colonized patients ) and transmission from an external source ( in the hospital example this corresponds to contacts with individuals that were not participating in the study ) . The expected injection rate , which accounts for both introduction mechanisms , is thus given by ι = λ in p s + S ¯ q s , where S ¯ is the average number of susceptible individuals at the equilibrium . In the theoretical analysis in the main paper we assumed qs = 0 for simplicity , thus variations in ι were induced by variations in λin and ps . The case qs > 0 was considered in the supporting information . Simulations on synthetic networks differ from those on the hospital network in the combination of the spreading and network dynamics . In the synthetic network case , at each time step of duration Δt = 1h , both network and spreading dynamics are simulated one after the other . On average , λinΔt new nodes enter in the population per time step , while existing nodes can leave with probability Δt/τ . Nodes then form contacts according to the specific generative network algorithm . Eventually , transmission and recovery are simulated as explained above . In order to reconstruct the equilibrium dynamics we run simulations for a sufficiently long time span , discarding a transient time of 4 ⋅ 104 time steps . We verified that the dynamical properties at the equilibrium are unaffected by initial conditions . For the hospital example , the network is an external parameter fed into the simulations . Contacts were aggregated daily keeping the information of their total duration . We used this information by considering a weighted network with the link weight , wij , representing the number of contacts of duration 30 s registered during the day between i and j . We then assumed Δt = 1 day and computed the probability of infection depending on the weight as 1 − ( 1 − β δ ) w i j , with δ = 30 s . We initialized the system with the same configuration observed in the data , i . e . the initial status for each node is set according to S . aureus carriage during the starting week . Simulation length is bound to the hospital contact network duration . In order to facilitate the comparison between the synthetic and the real scenarios , parameters of the network models were set based on the properties of the hospital network . The average size , the average activity potential and the average degree were set equal to the values estimated from the hospital network , i . e . V ¯ = 306 , a ¯ = 0 . 28 , k ¯ = 0 . 89 respectively . For the COM model the number of communities ( nC = 6 ) and one of the two explored values of pIN ( pIN = 0 . 78 ) were also informed by the data . Additional values of V ¯ and pIN were also tested . Epidemiological parameters were informed by the data in some cases—ps = 0 . 079 as computed from carriage data - , or chosen among plausible values for the S . aureus colonization—i . e . μ−1 , that was set equal to either 21 or 35 days with other values from 14 to 49 days explored in the supporting information . Values of β were explored systematically . For consistency , values of rates throughout the manuscript were always expressed per hour . Carriage data was obtained from weekly swabs in multiple body areas , including the nares . Swabs that resulted positive to S . aureus were further examined . Spa-type and antibiotic resistance profiles ( MSSA or MRSA ) were then determined . In this work we regard two strains as different if they differ in spa-type and/or antibiotic resistance profile . We considered carriage data obtained from nasal swabs dismissing other body areas since the anterior nares represent the most important niche for S . aureus [72] . We described strain population diversity through standard ecological indicators . The abundance of a strain i , Ni , is the strain-associated prevalence . From this quantity we computed the relative abundance , f i = N i ∑ i N i , and the relative abundance distribution , being the frequency of strains with relative abundance f . The Berger-Parker index is the relative abundance of the dominant strain , i . e . maxi fi . To analyze repartition of strains across communities we use the Inverse Participation Ratio ( IPR ) [55] . The general definition of this quantity is the following . Given a vector v → with l components {vi}i=1 , … , l , all within the range [0 , 1] , the IPR is given by: I P R = ∑ i = 1 l v i 4 . ( 2 ) If all the components are of the order ( l−1 ) then the IPR is small . Instead if one component vi ∼ 1 then IPR ∼ 1 too , reflecting localization of v → . The IPR for total prevalence is computed by setting vi equal to the fraction of infected individuals belonging to community i = 1 , … , l = nC , while the IPR for a single strain is computed by setting vi equal to the fraction of individuals infected by that particular strain and belonging to community i . We can extend the IPR computation to HOM case by assigning nodes to different groups as in COM but without affecting the stub-matching scheme . In order to estimate the value of the length of stay maximizing the average richness for a given value of β when the contact structure is given by the HOM network we consider a homogeneous mixing version of our system . Due to Eq ( 1 ) the calculation of the average richness reduces to the calculation of the average persistence time . In order to estimate such quantity we focus on a particular strain , labelled as “strain A” , which is injected at t = 0 and we group all other strains under the label “strain B” . We are allowed to do so because all strains have identical parameters . We therefore reduce our initial , multi-strain problem , to a two-strain problem . Since all new strains that will be injected after t = 0 will be labeled as strain B , it is clear that A is doomed to extinction since there exists an infinite reservoir of B . The average time to extinction is therefore the average time to extinction of strain A . Since HOM network realizes quite well homogeneous mixing conditions we regard our system as homogeneously mixed . Within this framework it is sufficient to specify the numbers of hosts infected by strain A ( nA ) , hosts infected by strain B ( nB ) and susceptible hosts ( ns ) . The master equation for the joint probability distribution P ( nA , nB , ns ) is given by [73]: P ˙ ( n A , n B , n s ) = β ′ V ¯ − 1 ( n A − 1 ) ( n s + 1 ) P ( n A − 1 , n B , n s + 1 ) + β ′ V ¯ − 1 ( n B − 1 ) ( n s + 1 ) P ( n A , n B − 1 , n s + 1 ) + μ ( n A + 1 ) P ( n A + 1 , n B , n s − 1 ) + μ ( n B + 1 ) P ( n A , n B + 1 , n s − 1 ) + λ o u t ( n A + 1 ) P ( n A + 1 , n B , n s ) + λ o u t ( n B + 1 ) P ( n A , n B + 1 , n s ) + λ o u t ( n s + 1 ) P ( n A , n B , n s + 1 ) + λ o u t V ¯ p s P ( n A , n B − 1 , n s ) + λ o u t V ¯ ( 1 − p s ) P ( n A , n B , n s − 1 ) − [ ( n A + n B ) ( β ′ V ¯ − 1 n s + μ ) + λ o u t ( n A + n B + n s ) + λ o u t V ¯ ] P ( n A , n B , n s ) , ( 3 ) Where β ′ = β k ¯ . Terms appearing on the right-hand side of the equation represent the probability flow associated to each transition event . The first four terms describe , in order , the infection due to strain A , the infection due to strain B , the recovery from A and the recovery from B . The remaining terms are then associated to the discharge of either one of the three types of individuals—infected with A , infected with B and susceptibles—and to the admission of infected of type B and susceptibles respectively . In order to obtain some approximate solution to this equation we assume that the average number of individuals nA + nB + ns and the total prevalence nA + nB do not fluctuate in time and are therefore equal to V ¯ and i ( ∞ ) V ¯ respectively , where i ( ∞ ) is given by: i ( ∞ ) = β ′ − μ − λ o u t + ( β ′ − μ − λ o u t ) 2 + 4 β ′ λ o u t p s 2 β ′ . ( 4 ) After performing the Van-Kampen size expansion we are left with a Fokker-Planck equation for the density of A f ( x = n A V ¯ ) = P ( n A ) : ∂ t f = − ∂ x ( D 1 ( x ) f ) + 1 2 V ¯ ∂ x 2 ( D 2 ( x ) f ) , ( 5 ) where D1 = β′ ( 1 − i ( ∞ ) ) x − μ − λout and D2 = β′ ( 1 − i ( ∞ ) ) x + μ + λout are the so-called drift and diffusion coefficients respectively . According to the theory of stochastic processes [73] the average extinction time Tpers ( x0 ) ( where x0 represents the initial density of strain A ) satisfies: D 1 ( x 0 ) d d x 0 T pers + 1 2 V ¯ D 2 ( x 0 ) d 2 d x 0 2 T pers = − 1 , ( 6 ) with boundary conditions Tpers ( 0 ) = 0 and d d x 0 T pers ( i ( ∞ ) ) = 0 . The solution is finally given by: T pers ( x 0 ) = i ( ∞ ) λ o u t p s [ E i ( − α i ( ∞ ) ) ( e α x 0 − 1 ) − e α x 0 E i ( − α x 0 ) + l n ( α x 0 ) + γ E ] , ( 7 ) where Ei ( x ) is the exponential integral function and γE is Euler-Mascheroni constant . When a new strain is introduced its prevalence is just 1 , therefore we estimate the average extinction time using T pers ( x 0 = V ¯ − 1 ) .
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Pathogens are structured in multiple strains that interact and co-circulate on the same host population . This ecological diversity affects , in many cases , the spread dynamics and the efficacy of vaccination and antibiotic treatment . Thus understanding its biological and host-behavioral drivers is crucial for outbreak assessment and for explaining trends of new-strain emergence . We used stochastic modeling and network theory to quantify the role of host contact behavior on strain richness and dominance . We systematically compared multi-strain spread on different network models displaying properties observed in real-world contact patterns . We then analyzed the real-case example of Staphylococcus aureus spread in a hospital , leveraging on a combined dataset of carriage and close proximity interactions . We found that contact dynamics has a profound impact on a strain population . Contact heterogeneity , for instance , reduces strain diversity by reducing the number of circulating strains and leading few strains to dominate over the others . These results have important implications in disease ecology and in the epidemiological interpretation of biological data .
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"methods"
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2019
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Host contact dynamics shapes richness and dominance of pathogen strains
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The Independent Action Hypothesis ( IAH ) states that pathogenic individuals ( cells , spores , virus particles etc . ) behave independently of each other , so that each has an independent probability of causing systemic infection or death . The IAH is not just of basic scientific interest; it forms the basis of our current estimates of infectious disease risk in humans . Despite the important role of the IAH in managing disease interventions for food and water-borne pathogens , experimental support for the IAH in bacterial pathogens is indirect at best . Moreover since the IAH was first proposed , cooperative behaviors have been discovered in a wide range of microorganisms , including many pathogens . A fundamental principle of cooperation is that the fitness of individuals is affected by the presence and behaviors of others , which is contrary to the assumption of independent action . In this paper , we test the IAH in Bacillus thuringiensis ( B . t ) , a widely occurring insect pathogen that releases toxins that benefit others in the inoculum , infecting the diamondback moth , Plutella xylostella . By experimentally separating B . t . spores from their toxins , we demonstrate that the IAH fails because there is an interaction between toxin and spore effects on mortality , where the toxin effect is synergistic and cannot be accommodated by independence assumptions . Finally , we show that applying recommended IAH dose-response models to high dose data leads to systematic overestimation of mortality risks at low doses , due to the presence of synergistic pathogen interactions . Our results show that cooperative secretions can easily invalidate the IAH , and that such mechanistic details should be incorporated into pathogen risk analysis .
In even the best studied host-pathogen systems , the exact relation between the inoculum size and the probability of disease is unclear . This “dose-response” relationship is not only of basic scientific interest [1 , 2] but is also important to accurately gauge disease risk in exposed human and livestock populations [3–10] . Unfortunately direct evaluation of disease rates at relevant pathogen doses can be unethical in humans or is experimentally intractable: at very low doses most experiments lack statistical power . To address this problem , data from high doses are extrapolated to lower ones by using predictive mathematical models [3–10] . These models are based on an important simplifying biological assumption: they accept the “independent action hypothesis . ” The independent action hypothesis has two components . It states “ ( a ) that bacteria act independently after inoculation , and ( b ) a mean probability ( 1 > p > 0 ) per inoculated bacterium of initiating a fatal infection which is constant and unaffected by the number of bacteria inoculated” [11–13] . The first of these claims is suspect in light of the fast-growing list of known cooperative behaviors in bacteria , like the widespread ability of bacteria to collectively alter their shared environment by secreting toxins , exo-enzymes and iron-scavenging molecules [14] . The second claim , on which standard dose-response models are built , is still largely accepted [3–10 , 15 , 16] . It has recently been pointed out that many epidemiological models implicitly assume this independence claim , and that this assumption can influence epidemiological dynamics [17 , 18] . Testing of the independent action hypothesis ( IAH ) has typically involved indirect inference of dose-response and co-infection experiments [1 , 11 , 15 , 16 , 19] . Though this work has been generally consistent with the IAH , to our knowledge the independent action hypothesis has never been directly confirmed nor rejected in any bacterial system . We test the IAH with Bacillus thuringiensis var . kurstaki , a widely occurring insect pathogen [20] , in larvae of the diamondback moth , Plutella xylostella . During sporulation each bacterium produces a proteinaceous toxin crystal ( Cry toxin ) . When a group of bacteria is ingested , these crystals are solubilised in the midgut and perforate it , facilitating host invasion and septicaemic proliferation in the haeomolymph [21] . These toxin crystals are ‘public goods’ because the toxins produced by any single cell can benefit all the cells in the midgut [22] . By independently manipulating toxin dose and bacterial density , we are able to demonstrate that toxins and spores interact to determine mortality and that toxins exhibit a threshold-like effect on mortality , thus invalidating the second component of the IAH . We then demonstrate that this failure leads to a systematic overestimation of infection risks at low doses by simulating the recommended dose-response procedures that rely on IAH-based mathematical models . This work demonstrates that bacterial cooperation can invalidate the independent action hypothesis and more generally that the formulation of risk assessment models should be driven by careful consideration of mechanisms of pathogenesis .
We studied the contributions of B . thuringiensis ( B . t . ) spores and toxins to mortality in the diamondback moth ( P . xylostella ) by infecting larvae with inocula of varying doses of plasmid-cured mutants , lacking genes for toxin production , combined with B . t . toxins produced by recombinant Escherichia coli ( see methods ) . Although the solubilized Cry toxins lead to cooperative interactions inside the larval host [22] , it would in principle still be possible that each bacterium has an independent probability of killing the host if the following conditions were both met: i ) if the mortality effects of toxins and spores were independent of one another and ii ) if the dose response of toxins themselves fit independent action assumptions . If these were both true then each bacterium could be ascribed an independent likelihood of causing mortality via the added effects of its toxins and spore . However , we will demonstrate that there is a spore toxin interaction in our system , and that the toxin effect is too threshold-like to conform to independence assumptions . We first explored the effect of spores on mortality by fixing the toxin quantity to either 60 or 180 pg , while varying spore dose ( Fig 1A ) . S1 Table ( in S1 Text ) shows a comparison of several models fit to this data; we found best support for the model y ~ Toxins + log ( Spores+1 ) + Toxins* log ( Spores+1 ) based on the Akaike Information Criterion ( AIC ) ( S1 and S2 Tables in S1 Text ) . Here spore dose contributed significantly to the virulence of B . thuringiensis infections ( for log dose β = 0 . 37 , SE = 0 . 063 , p < 10-8 ) , but this contribution was relatively minor ( increasing spore dose by three orders of magnitude delivers roughly a 20% increase in mortality ) . There is also a negative interaction between log spores and toxins ( β = -1 . 3 * 10–3 , SE = 4 . 7 * 10-4 , p < 10-2 ) . Most evidently at 60 pg , between zero and ~14 spores there is a substantial jump in mortality rates , indicating that toxin is not solely responsible for death at low doses . This pattern of a large shift in mortality from zero to non-zero spores ( at 60 pg , not 180 pg ) was repeatable in subsequent toxin experiments . This is most likely because at very high toxin levels , the toxins alone are sufficient to kill the host , whereas at lower doses septicaemia is the primary cause of death and so spore quantity matters more , in line with what occurs in natural populations . At higher doses , spores contribute significantly to mortality , though as stated above , this contribution is smaller than the contribution of toxins [23] . We then conducted the reverse assay , this time fixing the spore dose at 900 and combining it with a range of doses of toxin inclusion bodies . This assay was done in two independent experiments . In this constant spore dose assay , increasing toxins greatly increased the insect mortality rate ( Fig 1B ) . Fig 1B shows that the effect of toxins cannot be described by independent action because the data are too threshold-like . We use “threshold-like” to mean that the per-capita contribution to mortality increases with dose in the low dose range , in contrast to what independent-action models predict; we do not mean that there necessarily exists a hard threshold below which mortality cannot occur . For instance if each toxin molecule has some independent probability , p0 , of killing the host , then the “exponential” dose-response model , P ( k ) =1-e-p0k ( 1 ) detailed further below , describes the probability of mortality at expected dose k . When the data from experiment 1 are used to fit the model , the maximum likelihood fit is p0 = 0 . 0053 . For this first dataset only points below saturation ( up to 500 pg ) were used to fit this model; one technical issue that could have contributed to this intermediate saturation was that very high concentrations of toxins can deter feeding , making it difficult to ensure that all insects at very high doses consumed entire droplets . The second experiment had more low-dose data points and showed no intermediate saturation; it was best fit with p0 = 0 . 0165 . The exponential model , the steepest standard independent-action model where dose is Poisson distributed as it assumes no host variability , is unable to account for the sharp threshold-like rise ( and overestimates mortality at low doses ) in these experiments ( Fig 1B ) . In S1 Fig we show that a binomial model which assumes doses are known exactly rather than being Poisson distributed , as well as the beta-Poisson which encompasses host heterogeneity ( and will be described shortly ) , do not explain this effect either . Because the effect of toxins is too threshold-like to be described by independent action assumptions and also since the impact of toxins and spores toward mortality is non-independent , the independent action hypothesis fails in our host-pathogen system . It is not surprising that our toxin data are inconsistent with IAH-based models at low doses since there is no a priori reason to expect mortality as a function of toxin to follow any particular dose-response curve without careful consideration of the mechanisms of pathogenesis . In contrast to biological dose-response curves , chemical dose-response curves often explicitly incorporate threshold-like effects [24 , 25]; thus when bacteria secrete toxic metabolites , the IAH can easily be violated . It is increasingly realized that the disease dynamics in B . t . and other bacteria are driven by such non-independent processes inside the host [22 , 26 , 27] . The process of pathogenesis is often implicitly discussed as either following independent action assumptions or alternatively exhibiting an absolute threshold dose below which pathogenesis or mortality never occurs [9 , 10] . There is an intermediate possibility , as appears to be the case in our system , where cooperative action exists but without an absolute dose cutoff below which pathogenesis is impossible . One important application of experimental dose-response data is in determining disease risk in exposed host populations . In these exposed communities each host typically has a low likelihood of developing disease , and so the doses most relevant for public health applications are often very low . Unfortunately at these low disease rates , the data are limited and noisy so direct analysis lacks statistical power . As a result , standard practice is to determine risk at the relevant doses by extrapolating from higher dose results by using mathematical models [3–10 , 15 , 28 , 29] . These models are based on the assumption that each infecting cell has a constant and independent probability of causing disease . There are two commonly used models for dose response extrapolation , both based on the independent action hypothesis [6] . The “exponential model” in Eq 1 assumes each infected bacterium has a fixed probability , p0 , of causing host illness or death and that the mean ingested dose is k . This model can be further extended to account for variation in host susceptibility with P ( k ) =1-1F1 ( α , α+β , -k ) ≈1- ( 1+kβ ) -α ( 2 ) where α and β are parameters for the Beta distribution describing the likelihood of infection of a host per bacterium . The exact form , which uses a confluent hypergeometric function , is nearly always approximated to the above stated “beta-Poisson model” [6] , most accurate when β > 1 and α ≪ β . Generally , beta-Poisson curves reach full mortality more gradually than exponential curves because a small proportion of hosts resist extreme doses . All these IAH-based models are approximately linear at low doses and can easily overestimate risks if there are threshold-like effects , potentially driven by cooperation among infecting bacteria , as shown in Fig 1B . Dose-response data and fitted models for wild-type B . t are shown in Fig 2 . These data derive from two additional fully independent experiments with a total of N = 2073 larvae; see Methods for details . We used the standard methodology to determine low dose risks by assuming that we only had higher dose data for both datasets . To do this we first determined the maximum likelihood fits for the two most common dose-response curves ( exponential and beta-Poisson ) , fitting them to all points with infection rate above 20% ( all but the lowest 7 doses in experiment 1 , and all but the lowest 2 doses in experiment 2 ) . The beta-Poisson fit ( shown in Fig 2 ) is a better fit than the exponential model as it has a lower AIC ( Akaike Information Criterion ) . The best fit for the exponential model ( p0 = 0 . 00089; AIC = 33 . 19 for experiment 1 , p0 = 0 . 0012; AIC = 112 . 24 for experiment 2 ) is worse than the beta-Poisson model ( α = 3 . 80 , β = 3488 . 50; AIC = 30 . 60 for experiment 1 , α = 1 . 27 , β = 520 . 07; AIC = 56 . 85 for experiment 2 ) ; the beta-Poisson fits for both datasets is shown in Fig 2 . In practice , a researcher would choose a model based on the data available ( in our case , they would choose the beta-Poisson distribution over the exponential , based on the lower AIC at the higher doses ) , and then estimate risks at low doses given this best fit [3–10 , 15 , 28 , 30] . With the beta-Poisson model the highest dose below the points used for risk estimation is overestimated by 86 . 8% in experiment 1 and 160 . 5% in experiment 2 . To more directly test the error from using IAH-based models to fit to our data we simulated the standard process of estimating the infection risk at low doses [3–10 , 15 , 28 , 30] . Because of the large sample sizes that would be needed for resolution at low doses , researchers are forced to use mathematical models to approximate low dose risks ( in our data , infection probability less than 20% ) by fitting them to the available higher doses; the particular cutoff we used did not qualitatively affect the outcome of this approach . We resampled ( with replacement ) all the binomial data for each dose , to produce additional simulated experiments . The process was as follows: 1 ) generate “pseudo-data” by resampling the actual data for each dose with replacement , 2 ) determine the best fit exponential and beta-Poisson model based on the high doses ( above 20% infection ) , then pick whichever had a lower AIC , 3 ) use this model to extrapolate the mortality rate to the low doses , and 4 ) subtract the pseudo-data mortality rate at the dose of interest from the extrapolated mortality rate . When this difference is positive it indicates that the model fit overestimated risk and when negative that it underestimated the risk . We conducted this process separately in the two datasets , each with 5 , 000 simulated experiments . In experiment 1 , the low dose predictions that differed most systematically ( either underestimating or overestimating the correct risks ) were at doses of 35 , 75 , and 150 spores where mortality was overestimated 97 . 8% , 88 . 7% , and 97 . 7% of the time , respectively . Although some of the other low doses were actually underestimated , none were significantly so; among the other three nonzero doses in ascending order , risk was underestimated in 64 . 1% , 86 . 2% , and 55 . 8% of the simulations ( 50% corresponds to no bias in either direction ) . The distribution of the differences between the extrapolated risk and true value in the resulting runs from the 150 spore dose is shown in Fig 3A . The overestimation effect in experiment 2 was more dramatic; here there was a single non-zero data point with infection rate less than 20% , with expected dose of 130 . 59 ( from data in Fig 2 ) ; mortality was overestimated at this dose in 100% of all resampled datasets ( Fig 3B ) . It has been noted previously that the commonly used beta-Poisson model can differ from the exact , more complicated confluent hypergeometric form from which it is derived [31] . To determine whether the same bias exists when the confluent hypergeometric model is used , we performed the same procedure using this less commonly used , non-approximated version of the model . With this exact form of the model , risk estimates were similar to in the approximated one; the risk of fatal infection at the same low doses is over-estimated 98 . 0% of the time in the experiment 1 data at 150 spores and in 100% of runs with the experiment 2 data at 130 . 59 spores ( Fig 3C and 3D ) . Previous approaches to test the IAH have analyzed the rate of single strain infections among hosts co-infected with differentially tagged strains at low doses . In these experiments a prevalence of clonal infections is taken as evidence that only the progeny from one inoculated cell is recovered from the final systemic infection and therefore as support for the IAH [12 , 16 , 19] . Conversely if there is not a low dose range for which clonal infection occurs , this can be evidence for a cooperative infection as in previous work with a bacterial plant pathogen infecting a non-natural host [32] . A problem with tagging methods is that they can falsely identify bottlenecks during infection as evidence for independent action [33] . Indeed bottlenecks can occur when infection success is based on cooperative interactions . More convincing is a recent extension in a virus-insect system that formalized a simple ‘null model’ for probability of mortal infection and then varied doses of each of two tagged lineages in order to test for statistical deviations from this model [1] . Among six virus-insect systems tested , two were consistent with IAH predictions , and four were not; but as their approach was statistical rather than mechanistic , the causes of these departures from IAH predictions are unknown . Some of this may be explained by recent work that has shown that heterogeneity in host susceptibilities can cause both a shallow dose-response as well as an inflated rate of mixed infection among tagged strains [34] . The application of any mechanistic dose-response model to data implicitly asserts biological claims about the system . For instance the IAH model that best fit our wild type data was the beta-Poisson model ( Fig 2 ) , which explains a relatively gradual rise in mortality with dose as being caused by heterogeneity among hosts ( in contrast to the exponential model which assumes no such host heterogeneity ) . However if the marginal effect of additional toxins naturally diminishes as doses increase , then this leads to a slowly diminishing dose-response shape without requiring substantial host heterogeneity . So if in a model system the toxin dose-response were very shallow , applying the beta-Poisson model to the data would be implicitly claiming that there was extreme host heterogeneity even if the effect were just a property of the collective action of the toxin . One should be careful in directly comparing the insect mortalities with fixed spores and supplemented toxins ( Fig 1B ) to the mortality with wild type spores ( Fig 2 ) . There are differences between the toxin inclusion bodies of the wild type spores and the GM inclusion bodies produced by E . coli . Wild type inclusion bodies contain Cry1Ac , Cry1Ab , Cry1Aa and small quantities of Cry 2Aa [35] and are packaged in bipyridimal crystals , whereas the transgenic E . coli produces only the Bt toxin , Cry1Ac , which is packaged differently from the WT . Another difference was that in the fixed spores experiment ( Fig 1B ) the toxin was potentiated with a high and constant dose of spores ( 900 ) in each droplet , and so once there was adequate midgut perforation septicemia was nearly guaranteed . Though there are differences between the wild type and supplemented toxin dose responses , the effect of toxins is fundamentally non-independent . We have demonstrated that the IAH fails in B . thuringiensis due to the cooperative nature of its toxins , but how common might this be in other pathogens ? Closely related bacteria such as Bacillus cereus and Bacillus anthracis release a large number of diverse virulence factors [36] , as do other serious human pathogenic bacteria . For instance , anthrax toxins , cholera toxin , Staphylococcus alpha toxin , and Streptococcus pneumoniae toxin are all freely released and benefit neighboring related bacteria and therefore should be expected to violate IAH assumptions [37] . It has been previously argued that there might be mechanism-based rules governing broad trends in median dose-responses [26 , 27 , 37] . Here we have extended the appeal for a mechanistic focus in dose-response from median infection risk to understanding the shapes of these dose-response curves . Though we have concentrated our efforts on shared toxins , there are other social interactions that likely have major effects on dose-responses . For instance many bacteria release extracellular enzymes and small molecules that perform many other functions including immune cell evasion , cell-to-cell signaling ( i . e . quorum sensing ) , and biofilm formation . If a bacterium’s probability of passing a host barrier or harming the host increases with the secretions of other infecting cells [38] , the main assumptions of the IAH fails and dose-response curves are likely to be affected . We have shown that cooperative interactions between infecting pathogens can cause an error in risk assessment . What then can be done to better assess infection risk in such cases ? One approach may be to construct a more detailed mechanistic model of the infection process for a pathogen of interest using dose-response data available . However this could be very difficult in practice because a cooperative effect may occur below the range of available data , making it difficult or impossible to parameterize . When the system is known to use quorum sensing , to freely release a toxin , or to exhibit another cooperative behavior , a first-level approximation may be to view the independent action calculation of infection risk as an upper ceiling . However , this should only be done with great caution because it is possible that a cooperative trait may inflate risk above that predicted by an independent action model; this could occur if the marginal effect of each released molecule showed diminishing returns across all biological concentrations rather than a threshold-like effect as we see here . In such a case with a high initial increase in mortality followed by saturation , some low doses could in principle cause a higher risk than predicted by an IAH model . A better alternative may be to utilize engineered knockout mutants of cooperative genes in order to test the impact of the specific bacterial processes on infection . Then by studying the effect of supplemented cooperative secretions , more predictive , system-specific models may be constructed . In conclusion , our data show that cooperation of B . thuringiensis during infection of the diamondback moth Plutella xylostella causes the failure of the independent action hypothesis , and as a result , commonly used models overestimate disease probability at low doses . Because cooperation is a common feature of many bacteria , it is likely this overestimation extends to important human pathogens , potentially causing a misallocation of public health resources . The same biological assumptions and models have been recommended for assessing risk in bioterrorism attacks [9 , 39] , and a criticism of the response to the 2001 anthrax attacks in the United States was that risk was grossly overestimated , costing millions in unnecessary sterilization , due to not incorporating threshold-like effects into the dose-response models [40] . Besides its implications for risk assessment , a lack of linearity in infection among low doses can significantly alter standard epidemiological assumptions for disease transmission [41] and also our understanding of genetic drift [2] and the evolution of cooperation [42] among pathogens . The articulation of the independent action hypothesis more than fifty years ago has been helpful to clarify thoughts on infection biology and risk . But the biology that has been uncovered in subsequent years questions its generality . The commonly used dose response models are used because they are simple , but given the extent of social interactions now known to occur between bacteria , the independent action hypothesis and models based on it are no longer tenable in many bacterial pathogens .
Spontaneous antibiotic resistance mutants of B . thuringienis kurstaki HD-1 , were isolated from the commercial biopesticide preparation , DiPel WP ( Valent Biosciences ) , by plating high densities of cells ( 108 + ) on 15 μg ml-1 nalidixic acid . An antibiotic resistant mutant with reduced fitness cost ( 6G NalR ) was isolated after a round of host passage in P . xylostella [43] , and identified by rapid growth on selective plates . This strain was cured of its Cry toxin producing plasmid by growth at high temperature ( 42°C ) and isolating colonies with unusual morphology at sporulation and in order to produce the isolate Cry null 6 . 20 NalR . Absence of Cry toxin production was confirmed by microscopy and bioassays with P . xylostella , which confirmed that this mutant was not infectious at very high doses ( >105 cfu ) . Sporulated cultures of all strains were produced by growing dense lawns of bacteria on HCO sporulation media [44] at 30°C for 1 week . Spores and Cry toxins were recovered from plates and washed twice in sterile saline ( 0 . 85% NaCl ) , before being diluted into 10ml of saline and stored at -20°C in 0 . 5 ml aliquots for up to 8 weeks . Defrosted spores were enumerated by plating serial dilutions; replicated counts were made within 48 hours of infecting insects . Exogenous B . thuringiensis Cry toxin ( Cry1Ac ) was produced in E . coli JM109 cells carrying the plasmid pGem1Ac , a gift of Dr Neil Crickmore ( University of Sussex ) . Cells were grown in 500ml of double strength LB for 3 days at 37°C with 100 μg ml-1 ampicillin . After centrifugation ( 6000 g ) pellets were suspended in 30 ml sterile de-ionized water and sonicated in 15ml aliquots using a Branson sonicator at 25% amplitude with four bursts of 40s with 40s rests on ice between each burst . Cells were centrifuged at 5000 g before being resuspended in water with 0 . 5% Triton X-100 before an additional minute of sonication . Cells were then centrifuged , and resuspended one more time before stored at -20°C in 1ml aliquots . Total Cry toxin production was estimated using SDS-PAGE and densitometry with BSA as standard using the Biorad Image Lab 4 . 01 software . Cry1Ac forms a strong band of 130kDa , facilitating ready quantification . There was approximately 0 . 6 pg of toxin per exogenous inclusion body . Toxin aliquots were pasteurized ( heat treated at 65°C for 20 minutes ) before use in bioassays in order to kill any remaining E . coli cells . An inbred population of P . xylostella larvae ( Geneva 88 ) were reared on artificial diet as described previously [45] , this population has been in continuous culture for at least 20 years [46] . The parents of larvae used in bioassays were reared on artificial diet containing streptomycin and chlortetracycline . Eggs produced by these individuals were surface sterlilized with sodium hypochlorite prior to use , as described previously [45] , these methods ensure that insects are largely free of enteric bacteria [45] . Larvae for assays were reared from eggs laid in standard cohorts ( i . e . from the peak oviposition period 2–3 days post-mating ) on antibiotic-free diet . All insects emerged from eggs onto diet within a 24 hour window . Early third instars ( 4–5 days old ) were infected with Bt in droplet assays . We further limited size/environmental variation by only using larvae 4–5 mm in length and by excluding late second instars . Instars that are about to moult can be recognized by the dark band ( the new head capsule ) immediately behind the head . The final droplet mix contained 10mM sucrose , 7 . 5% v/v green food dye ( Dr Oetker , www . oetker . co . uk ) and 0 . 4% w/v agar ( Oxoid Bacteriological ) , and 40% v/v cabbage extract ( filtered liquid from boiled cabbages ) . The cabbage juice , sucrose and food dye were filter sterilized before being used to dilute the spores; this mixture was then combined ( 50:50 ) with molten 0 . 8% agar ( at 60°C ) . The resultant inoculum was briefly held at 50°C in heat block while 1μl droplets were dispensed into each well of 48 well plates using pre-warmed pipette tips . A single larva was added to each well , and plates were tightly sealed with damp tissue paper: larvae were allowed to feed for up to 18 hours . After feeding , larvae that had consumed at least 75% of their droplets , and which had visible green dye throughout their intestinal tract , were transferred to artificial diet for 5 days . Successful infections were classed as larvae that died and produced the strongly melanized cadavers indicative of Bt infection . We carried out three sets of droplet bioassays in order to test the IAH . The first set of assays measured the response of mortality to variation in spore dose ( using Cry null 6 . 20 NalR ) while holding the dose of exogenous Cry1Ac constant . This experiment was carried out at two doses of exogenous toxin ( 60 or 180 pg Cry1Ac ) and was set up with 48 larvae per dose . The second group of assays aimed to explore the effect of increasing concentrations of the public good virulence factor ( exogenous Cry1Ac toxin ) while holding the spore dose constant ( using Cry null 6 . 20 NalR ) . These experiments used a constant spore dose of 900 CFU , while toxin dose varied from 5 pg to 6 ng; this assay was repeated and set up with 48 larvae per dose in each replicate . The final set aimed to accurately establish the shape of the dose response curve to wild-type B . t . kurstaki HD-1 spores and toxins using 6G NalR , and we carried out two independent experiments with these wild-type spores . The first experiment used 12 doses based on a two-fold dilution series with an additional saline control , with 45–90 insects per dose . It was carried out in two blocks that were pooled into a single data set without loss of explanatory power ( F1 , 28 = 0 . 5 , P = 0 . 48 ) . The second wild-type experiment was carried out to give finer resolution over a slightly higher dose range ( 4800–130 spores ) and used ten doses diluted in a 2:1 series with 90–110 insects per dose . Statistical analysis was performed in R v3 . 0 . 2 . GLMs were calculated using the package glm2 [47] . Figures were produced using ggplot2 [48] . All nonlinear fits were using the R package bbmle ( function mle2 ) , excluding the zero dose points in the fixed spore dose-responses since the models examined assign zero likelihood at dose of zero [49] . Data deposited in the Dryad repository: http://dx . doi . org/10 . 5061/dryad . 72f4s [50] .
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The Independent Action Hypothesis ( IAH ) is a basic claim in pathogen biology that underlies risk analysis for various national and international health organizations . It states that infecting pathogens act independently of one another and has proven difficult to test directly . Here we demonstrate that cooperation between infecting bacteria causes the IAH to fail in a model host-pathogen system . As a result , standard mathematical risk-assessment models , typically based on the IAH , can overestimate mortality risk at low doses . Cooperation is widespread in micro-organisms , and our results indicate that unjustified reliance on the IAH will lead to inaccurate risk assessment . Our results suggest a re-appraisal of how we assess risk from infectious agents , and for the development of mechanistic , pathogen-specific models .
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2015
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Bacterial Cooperation Causes Systematic Errors in Pathogen Risk Assessment due to the Failure of the Independent Action Hypothesis
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Simultaneous changes in ion concentrations , glutamate , and cell volume together with exchange of matter between cell network and vasculature are ubiquitous in numerous brain pathologies . A complete understanding of pathological conditions as well as normal brain function , therefore , hinges on elucidating the molecular and cellular pathways involved in these mostly interdependent variations . In this paper , we develop the first computational framework that combines the Hodgkin–Huxley type spiking dynamics , dynamic ion concentrations and glutamate homeostasis , neuronal and astroglial volume changes , and ion exchange with vasculature into a comprehensive model to elucidate the role of glutamate uptake in the dynamics of spreading depolarization ( SD ) —the electrophysiological event underlying numerous pathologies including migraine , ischemic stroke , aneurysmal subarachnoid hemorrhage , intracerebral hematoma , and trauma . We are particularly interested in investigating the role of glutamate in the duration and termination of SD caused by K+ perfusion and oxygen-glucose deprivation . Our results demonstrate that glutamate signaling plays a key role in the dynamics of SD , and that impaired glutamate uptake leads to recovery failure of neurons from SD . We confirm predictions from our model experimentally by showing that inhibiting astrocytic glutamate uptake using TFB-TBOA nearly quadruples the duration of SD in layers 2-3 of visual cortical slices from juvenile rats . The model equations are either derived purely from first physical principles of electroneutrality , osmosis , and conservation of particles or a combination of these principles and known physiological facts . Accordingly , we claim that our approach can be used as a future guide to investigate the role of glutamate , ion concentrations , and dynamics cell volume in other brain pathologies and normal brain function .
Spreading depolarization ( SD ) is a self-propagating wave characterized by a near-complete breakdown of transmembrane ion gradients in cells , sustained depolarization in individual neurons , and swelling of neuronal and glia cells [1 , 2 , 3 , 4 , 5] . It is now well accepted that SD is relevant to many neurological disorders . Several studies have shown that SD is the pathophysiological correlate of the symptoms of migraine aura [6 , 7 , 8 , 9 , 10 , 11] , and occurs frequently in acutely injured brain caused , for example , by ischemic stroke , aneurysmal subarachnoid hemorrhage , and trauma [12 , 3 , 13 , 14 , 4 , 5] . Several clinical studies by COSBID group [15] and others suggest that SD mediates cortical lesion development and secondary brain damage in patients with acute brain injury , impairs clinical recovery , and triggers new deficits [12 , 13 , 3] . Furthermore , significant evidence indicates that SD and epileptic seizures might have some shared mechanisms [16 , 17 , 18 , 19] . The local processes during SD are understood as the interplay of neurons , astrocytes , and the vascular system . The neuron releases large amounts of K+ and glutamate into the extracellular space ( ECS ) together with significant drop in extracellular Ca2+ , Na+ , Cl− , and pH when it depolarizes . Consequently , SD is accompanied by significant extracellular K+ and glutamate accumulation , activation of N-methyl-D-aspartate ( NMDA ) receptors , a general loss of ion homeostasis , and cytotoxic edema [20 , 21 , 22 , 23 , 4 , 5 , 24 , 25 , 26 , 27] . Excitotoxicity and SD are largely overlapping phenomena . Glutamate is of particular interest because of its role in excitotoxicity and its synchronous extracellular rise with the onset of SD [13 , 28 , 27] . Activation of NMDA receptors by glutamate triggers the release of further glutamate and K+ that will diffuse to neighboring cells thus causing the propagation and sustainment of SD . This hypothesis is backed by significant evidence of glutamate receptors antagonists inhibiting SD . Slices experiments showed that ischemic cells with NMDA and non-NMDA receptors blocked , did not exhibit the fatal form of SD [26 , 29] . An NMDA receptor antagonist , Ketamine was shown to inhibit SD in swine cortex [30] and reduced the number of SD incidences in patients [29 , 31] . Astrocytes and vasculature are other key players regulating many aspects of SD [4 , 14] . In addition to coordinating matter transport between vasculature and neurons and playing a major role in the observed metabolic and hemodynamics effects that are key to our understanding of numerous neurovascular diseases , astrocytes protect against SD initiation due to their high capacity for K+ and glutamate uptake [14] . Increasing the expression of astrocytic glutamate transporters reduces the infarct volumes following ischemia [32] and protects against the onset of ischemia-induced SD [33] . Astrocytic swelling together with changes in neuronal volume can exacerbate SD and may lead to severe brain damage [34 , 35] . In astrocytes , volume–activated anion channels may release large amounts of glutamate leading to excitotoxic damage [36] . The knockouts of aquaporin 4 channels that mediate astrocytic swelling [37] , protect against ischemia [38] . To summarize , SD is accompanied by an array of immense changes from molecular to network level . A better understanding of SD and a spectrum of related pathologies , therefore , hinges on elucidating the pathways involved in these changes . However , existing techniques are too limited to investigate all these pathways . To overcome this void , we develop a comprehensive model that takes into account these key variables to quantify the role of glutamate dynamics in SD . We are particularly interested in SD caused by K+ perfusion and oxygen glucose–deprivation ( OGD ) . The model equations are either derived purely from first physical principles of electroneutrality , osmosis , and conservation of particles , or by a phenomenological combination of these principles and known physiological facts . Our model is successful in explaining experimental results about the role of glutamate in SD . We confirm the predictions of our model by showing that astrocytic glutamate transporters blocker ( 2S , 3S ) -3-[3-[4- ( trifluoromethyl ) benzoylamino]benzyloxy]aspartate ( TFB-TBOA ) significantly elongates the duration of SD in cortical slices from 15-24 days old rats . While our discussion is focussed on glutamate , the model can be used to explore the role of other key pathways and swelling in the dynamics of SD . Furthermore , the framework can be applied to investigate the role of ion concentrations , glutamate , and cellular volume dynamics in other pathological conditions and normal brain function . Numerous single neuron models for investigating SD have been developed . The phenomenon is rather generic and is found in models with great physiological details [39 , 40 , 41 , 42] as well as in simplified HH based descriptions of the neuron [43 , 44 , 45 , 46 , 47 , 39 , 48 , 49] . With the help of these models , thresholds for SD ignition and recovery can be assessed . In particular , it can be analyzed how energy and oxygen supply , morphological parameters , and blood pressure affect the course of SD , how SD can be prevented , and when it is non–recoverable [50 , 44 , 51 , 52 , 43 , 53] . Only few of these models deal with swelling . Some incorporate neuronal swelling alone [54 , 44 , 43 , 55] , while one model [49] deals only with the astrocytic volume . Only two models Ref . [40 , 56] include neuronal and astrocytic swelling simultaneously . The models in Refs . [57] and [58] for regular neuronal spiking and epileptic seizures respectively deal with astrocytic glutamate uptake with no ion concentration dynamics or swelling . The model in Ref . [58] does not include glutamate release from neurons during spiking . To our knowledge , no neuronal model ( SD or otherwise ) deals simultaneously with ion concentrations , neuronal and glial volume changes , and glutamate dynamics . As discussed above , the extreme changes and interdependence of these pathways during SD warrants a comprehensive computational framework encompassing all these key pathways—the subject of this paper .
Rate equations for the membrane potential of the neuron , gating variables for K+ and Na+ channels , ion concentrations inside the neuron , glia , and ECS , and volumes of the neuron , glia cell , and ECS are based on our previous work [45 , 46 , 53 , 56 , 59 , 43 , 60 , 61 , 44 , 51 , 63 , 64] . These equations together with the modifications due to the inclusion of glutamate dynamics , and the morphology used in this model are described in S1 Text . Here we outline the details about modeling the glutamate homeostasis . Glutamate is a neurotransmitter that is released into the cleft of a synaptic connection when the presynaptic , i . e . signal–sending , neuron depolarizes . Glutamate binds to the NMDA and AMPA receptors of the postsynaptic neuron and can thereby initiate an action potential ( AP ) . After binding to a receptor the transmitter is free again and can bind another time or diffuse into the ECS . Neurons and glia cells clear glutamate by taking it up from the cleft or from the ECS . For an overview of glutamate–related processes we refer the reader to reviews by Benarroch [66] , and Kandel et al . [67] ( see part III ) . Several components of the computational model presented in this section are adapted from various computational studies [68 , 69 , 70 , 58 , 71] and have been modified or extended for the application to SD .
We look at SD caused by perfusion of brain slices with high K+ and SD caused by OGD . The first case is modeled by increasing Kbath ( K+ in the bath ) from 4 mM to 15 mM at the start of simulation and stays elevated throughout the experiment . For the second situation all pump and glia functions slowly cease within 15 sec , remain interrupted for 40 sec , and are then slowly reactivated within 15 sec . Fig 2 shows the evolution of the membrane potential , Nernst potentials , ion concentrations , and the volumes . The OGD protocol is indicated by the orange bar in Fig 2b and 2d . The light parts at the beginning and end of the bar mark the smooth cessation and reactivation , respectively . In OGD , the uptake parameter v c → n m a x is also slowly set to zero and glutamate clearance is interrupted as well . The normal value of v c → n m a x is 0 . 03 mM/msec . This is in the range of values suggested by Rusakov , and Slichenko and Tass in Refs . [70 , 58] . The main result of Fig 2 is that our addition of glutamate–related processes does not change the familiar course of events of SD . In both cases SD begins with a short burst of spikes driving the neuron into depolarization block . The burst causes huge changes in ion concentrations , most prominently a drop in extracellular Na+ and a huge rise in extracellular K+ ( see curves for Nae and Ke in Fig 2c and 2d ) . The extracellular space is also rapidly flooded with high concentration of glutamate that prohibits the ion channels from closing . As a consequence of these ion changes , the membrane potential differences become very small ( see Fig 2c and 2d ) and the neuron enters a phase of sustained depolarization very quickly . The time at which depolarization begins is marked by a red triangle pointing upwards . Thereafter the neuron remains depolarized for about 60 sec before it suddenly repolarizes and ion concentration begin to recover . Note that this abrupt transition leads to a brief overshoot into hyperpolarization . The time of this potential drop is marked by a green triangle pointing downwards . The main difference between SD caused by K+ perfusion and OGD in the model is that in the perfusion experiments extracellular K+ ( and other concentrations because of it ) builds slowly as K+ diffuses from bath to ECS till it reaches a point where the cell starts spiking and enters SD . The cell comes out of SD spontaneously as the K+ clearance mechanism overcomes the release processes . If the simulation is allowed to run for longer time , the cell repeats this process till Kbath is reduced back to physiological values . Another difference between SD caused by K+ perfusion and OGD is that in the latter case there is no spiking in the beginning of SD . The insets of Fig 2c and 2d show how the volumes of the neuron , the glia cell , and the ECS change during these ion fluxes . The ECS shrinks dramatically while the neuron is depolarized . During OGD , ECS shrinkage gets much faster when ion pumps and glial functions are slowly reactivated . The reason is that glia swelling is blocked during OGD since there is no particle uptake . After reactivation of regulatory functions , the glia cell starts swelling because of K+ uptake and ECS shrinkage is accelerated immediately . The potential and ion dynamics for high K+ perfusion and OGD are very similar . However , as Fig 3 shows the glutamate dynamics clearly differ . Fig 3a and 3b show extracellular glutamate concentrations in the synaptic cleft and in the ECS . De– and repolarization times are indicated by triangles again . We note that depolarization increases the glutamate concentration in the cleft to more than 10 mM in both cases . The insets of Fig 3a and 3b show more details on finer scales . The sharp peak of the concentration in the cleft decays within 2 to 3 sec ( upper insets ) . For OGD , this goes along with a rise in glutamate in the ECS due to diffusion . The two concentrations are equal after 3 sec . For K+ perfusion , Ge remains practically zero at all times . In the lower inset of Fig 3a , we see that after the peak Gc goes to values between 10 and 20 μM , while in the OGD simulation , the level remains 5 mM in the cleft and the ECS . Concentrations go back to zero with repolarization . The lower inset of Fig 3b shows that the neurotransmitter is cleared quickly when neuronal and glial uptake functions are reactivated . Recall that both are impaired during OGD . Concentrations are reduced to values in the range of 10 to 20 μM within a few seconds before repolarization takes them to zero . Fig 3c and 3d show the different uptake pathways . Main plots present uptake from all of the synaptic clefts of the neuron . The total height of the colored region is total uptake , the yellow and the orange portions are the specific contributions of the neuron and the glia cell . By buffered glutamate we mean molecules that have been taken up by the cells , but have not been recycled . Vesicle reproduction reduces N u p G , which is why the amounts of buffered glutamate do not strictly grow , but can also shrink ( see Eq ( 30 ) ) . In the OGD run , uptake dynamics only start with uptake reactivation by the end of OGD , so the different time signature is simply dictated by the OGD protocol . What these uptake plots show us is that glial uptake is dominant for clearance from both the clefts and the ECS ( see insets ) . Moreover , more glutamate is cleared from the ECS than from the cleft in OGD–caused SD . The reason is that the amount of glutamate that diffuses into the ECS is larger than the amount that remains in the clefts . Also in perfusion–caused SD , large amounts of the neurotransmitter are taken up from the ECS as well . This type of uptake is fast enough to maintain a concentration Ge that is nearly zero at all times . We like to stress that Fig 3 should only be seen as an overview of the glutamate dynamics that our model provides . Details like the concentration plateau that follows the depolarization peak depend on the fine balance of release , uptake , and glutamate recycling . A different release function and a more detailed incorporation of the recycling process could lead to a different plateau . The most reliable aspects of our model are the amount of glutamate release during the depolarization burst , the diffusion process , and cellular glutamate uptake , since these parts of the model have been developed based on experimental data [58 , 70 , 67] . As pointed out above , increasing the expression of astrocytic glutamate transporters has been shown to reduce the infarct volumes following ischemia [32] and protects against the onset of ischemia-induced SD [33] . A recent study shows reduced rates of glutamate and K+ clearance by cortical astrocytes during neural activity and reduced density of excitatory amino acid transporters 1a ( EAAT-1a ) in cortical perisynaptic astrocytes in heterozygous FHM type 2-knockin mice [91] . By partial inhibition of glutamate transporters in wild-type mice , this study provides clear evidence that defective glutamate clearance can account for most of the facilitation of SD initiation in FHM type 2-knockin mice [91] . In vitro studies showed impaired glutamate uptake in hippocampal mixed astrocyte-neuron cultures from mice expressing FHM type 2-causing α2 Na+/K+ ATPase . Induction of SD in these animals resulted in reduced recovery [92] . In the following , we provide a complete understanding of the glutamate uptake processes and its role in SD by reducing v c → n m a x ( rates for other uptake pathways are implied ) . The simulations are shown in Fig 4 and they are consistent with our experimental observations ( see below ) . Fig 4a and 4b show the membrane potential dynamics for maximal uptake rates of 20% and 18% of the v c → n m a x–value used in Figs 2 and 3 . The repolarization time for normal uptake is about 143 sec as indicated by the vertical dashed line . With the lower uptake rates , recovery is delayed by about 65 and 105 sec , respectively . Before repolarizing , the membrane potential oscillates with a low amplitude . We note that the potentials for the first 143 sec into SD are the same in Figs 2a and 4a and 4b . This is also true for the ion concentrations , and we conclude that for the first 143 sec the ion fluxes are almost not affected by the smaller vmax–values . This implies that the contribution of the cotransport currents I i o n c o from Eq . ( 32S ) – ( 34S ) is negligible . The only difference for the first 143 sec between normal and impaired uptake lies in the much higher glutamate concentrations Gc ( see Fig 4c vs the lower inset of Fig 3a ) . In other words , too much glutamate in the cleft prevents recovery . The neuron only repolarizes when the concentration is small enough . An uptake rate of 20% achieves this sooner than the one at 18% . In Fig 4d , we look at this delaying effect systematically and compare SD durations for a range of uptake rates between 100% ( normal value ) and 16% . The effect becomes noticeable at 35% and lower with a maximal duration of almost 500 sec for 16% uptake . Below 16% recovery fails . For uptake rates between 100% and 50% the duration of SD is nearly constant . In this range of uptake rates the evolution of Gc is comparable to Fig 3a . The initial jump in Gc is quickly reversed and recovery is unaffected by glutamate . Only for uptake rates of 35% and less do we see a slow down–regulation of Gc as in Fig 4c . We expect critical uptake rates to depend very sensitively on all processes of the glutamate cycle . The numerical values in Fig 4d are likely to differ between models and should not be assumed for a real system . However , we believe that the basic correlation between the duration of SD and glutamate regulation is a real effect that explains the delayed recovery from perfusion-induced SD in tissues that are exposed to TBOA in our experiments ( see below ) . A recent study showed that 0 . 5 and 1 mM TBOA prolonged SD by 148% and 426% respectively [28] . A respective increase of 167% and 374% in glutamate concentration was observed in the same experiments . We can understand delayed recovery and recovery failure in SD from a phase space perspective . Let us briefly review the general method for the case of intact glutamate uptake ( see Figs 2 and 3 ) . The role of glutamate clearance is addressed in a second step . One can determine the timescales of the different processes in the model by a dimensionality analysis , and it turns out that Δ N b a t h K and Δ N g l i a K are the slowest variables ( see Ref . [46] for a complete timescale analysis of a very similar model ) . Please note that in our model presentation it is not possible to read off the timescales from the coupling parameters . They have different units and consequently different orders of magnitude . A dimensionless presentation can be obtained by appropriate rescaling of the dynamical variables , but this is not our focus here . The timescales of vascular coupling and glial buffering have been derived rigorously in the above mentioned study [46] . These processes are the slowest ones in the system and they occur on similar timescales . This can be used to apply a so–called slow–fast analysis which allows us to derive the threshold condition for repolarization . Let us combine the two quantities to define a single slow variable: Δ N K ≔ Δ N g l i a K + Δ N b a t h K ( 31 ) In a slow–fast analysis , we ask which states of the system are possible when a certain value of the slowest variable is given . To find these states we treat this variable as a parameter , which formally defines a subsystem of only the fast dynamics . The processes of the fast subsystem are membrane dynamics and ion fluxes across the neural membrane . We refer to them as transmembrane dynamics . For the fast subsystem we also treat glutamate as a parameter . This is not an approximation , because glutamate is fast . However , it is the only way to systematically study how elevated glutamate levels affect the other processes . For now we set the level extremely low ( 0 . 0001 mM ) , which practically means we ignore glutamate . This assumption is good enough at the de– and repolarization points . Depending on our choice of ΔNK , the fast subsystem has a certain number of stable and unstable fixed points . With the help of the software tool AUTO [83] , these fixed points can be found and followed , while ΔNK is varied within a certain range . Every stable fixed point of the ‘fast subsystem’ is stable in the full system except for dynamics of ΔNK . When the timescale separation is sufficiently large the fast variables equilibrate to one of these stable fixed points , and this way the dynamics of the whole system will be guided by the fixed point structure of the subsystem . Formally , this concept is known as a ‘quasi–steady–state reduction’ [93] . In Fig 5 , we see how this works . The left panel ( Fig 5a ) shows the evolution of ΔNK over 200 sec . It is indeed slower than the other variables ( see Fig 2 ) with the exception of Cli/e—a detail that has been addressed previously and can be neglected here [56] . Note that Δ N g l i a K and Δ N b a t h K are two independent dynamical variables , they affect volumes differently . So knowing ΔNK alone is not enough information and we need to specify Δ N g l i a K too . That is why , Δ N g l i a K is also included in Fig 5a . We have marked the crucial values at the de– and repolarization points by a pink and a turquoise X respectively . We see that the neuron depolarizes at the minimal value and repolarizes at the maximal value of ΔNK . This means that SD starts when the K+ content of the neuron and its ECS is elevated , and that recovery is on the other hand only possible when enough K+ is taken away ( recall the sign of the difference terms in Eq . ( 22S ) ) . The relation between ΔNK and these events becomes more clear in Fig 5b , which shows the location of the fixed points of the fast subsystem in the ( ΔNK , V ) –plane and how the trajectory of the full system is guided by them . Near the depolarization point , we use the fixed point curves for Δ N g l i a K ≈ 129 fmol and near the repolarization point we use Δ N g l i a K ≈ 226 fmol . Note that we do not show the complete fixed point curves . The entire curves are both z–shaped and overlap strongly . Hence for clarity , we only show two disconnected portions of these curves . The trajectory of the full system is a closed loop in the phase space and the transition points can now be understood through the fixed point structure . Let us focus on the repolarization process . Depolarization can be explained analogously , and we refer the reader to a previous study for more details on both transitions [46] . As the trajectory approaches the repolarization point , it is closely guided by the stable depolarized fixed point curve . The potassium content decreases until the curve becomes unstable . Stability changes of fixed points generically occur in Hopf bifurcations or limit point bifurcations . The AUTO software provides this information and shows that the above change of stability is due to a Hopf bifurcation . For completeness , we mention that around a Hopf bifurcation there are always stable or unstable limit cycles . These limit cycles imply oscillation , which we often see shortly before repolarization . Typically , stable limit cycles in SD models only exist in a narrow range of ΔNK values around the Hopf bifurcation before they change stability in a limit point bifurcation of limit cycles ( see Figs 2 and 3 in Ref . [46] where K ˜ e = - Δ N K / ω e ) . When there is no stable upper fixed point or limit cycle the trajectory drops back onto the polarized stable fixed point branch . Since the transition from depolarization to repolarization happens at or very close to the Hopf bifurcation , the two can be related to each other . We remark that the situation will be different when the upper and lower fixed point branches do not overlap ( see below ) . The tracking of limit cycles in our model is numerically very involved , because of the many different timescales . The continuation of limit cycles is hence beyond the scope of this study , but a complete analysis for a similar model has been performed before [46] . Note that our bifurcation diagrams only contain the bifurcations that change the stability of the fully stable fixed points . Subsequent bifurcations that change the degree of instability are not relevant to our analysis here and are omitted . From the phase space perspective , we understand that recovery from SD relies on the existence of a stable repolarized state in the fast subsystem . In Fig 5b , such a state is available and also seems to exist with higher glutamate concentrations as in Fig 4a and 4b . We now increase Gc and see how the fixed point curve changes . Unlike the treatment of ΔNK as a parameter , this is not an approximation in the sense of a slow–fast analysis . Glutamate–related processes happen on fast timescales ( see Fig 3a and 3b ) . We rather treat Gc as a parameter to study the effect of glutamate in some extreme scenarios . For example , how does high glutamate concentration in the cleft—that may occur with impaired clearance—affect the dynamics of the system ? By fixing Gc , we can obtain qualitative insights and answers to such questions . To discuss recovery , we look at the fixed point curves for high Δ N g l i a K . Fig 6a shows these curves for three different values of Gc . The two lower values 0 . 02 mM and 0 . 05 mM are chosen to show that the fixed point structure is very sensitive to Gc . The highest value 0 . 3 mM is included because it is near the glutamate levels that prevent early recovery in Fig 4c ( see values at the dashed vertical line ) . The fixed point curves have a stable depolarized ( upper ) branch that ends at a maximal value of ΔNK and a stable polarized ( lower ) branch that begins at a minimal value of ΔNK . These points are defined by Hopf bifurcations . The red line contains all the lower branch Hopf bifurcations for Gc–values between 0 mM and 0 . 35 mM , the green line contains the upper branch Hopf bifurcations . The polarized branch shifts towards higher ΔNK–values as Gc increases . For the depolarized branch , this shift is much smaller and the green bifurcation line is consequently shorter in the ( ΔNK , V ) –plane . For low Gc , the upper and lower fixed point branch overlap , and the sharp transition from a depolarized state to a polarized state is possible . For higher values there is a gap between the branches and instead of repolarization the system goes into persistent low amplitude oscillations after the upper branch becomes unstable . We have not included the corresponding limit cycles in Fig 6a , however , the time series in Fig 4 shows such oscillations . In Fig 4 , the oscillations are unstable , causing the cell to transition to the polarized state . In case of recovery-failure , these small amplitude oscillations persist for the duration of simulations . Since we understand repolarization as the transition between overlapping fixed point branches , the interesting question is , at what glutamate level this overlap disappears . Above this level , recovery is no longer possible . In Fig 6b , the end of the upper stable fixed point branch and the beginning of the lower branch are shown in the ( Gc , ΔNK ) –plane . As long as the depolarized branch ends after the polarized branch begins , recovery is possible . The critical Gc–value is hence at the intersection of the two lines , which occurs near 0 . 035 mM . If Gc was not a dynamical variable , but a system parameter , this value would be the threshold for recovery failure in SD . In our simulation of the full system , however , we cannot separate the dynamics of V and Gc . As V decreases , glutamate release slows down and consequently Gc decreases too . On the other hand , an increasing glutamate level depolarizes the neuron , and accordingly the two effects amplify each other . This leads to the glutamate drop from about 0 . 1 mM to nearly zero at the repolarization point ( see Fig 4c ) . Because of this fast interplay of V and Gc , the critical value derived in Fig 6b is not an obvious threshold in the full system , but only gives us a rough idea about glutamate levels near the repolarization point . The fixed point curves in Fig 6a do not approximate the dynamics of the whole system . Moreover , even the fast scale glutamate dynamics of the whole system are only a rough approximation of a real system . Nevertheless , we have learned that critical glutamate levels exist , beyond which the neuron will not repolarize . This observation is based on a model with a parametrical glutamate concentration in the cleft and glutamate coupling through NMDA and AMPA receptors . These parts of our model are based on a more accurate biophysical description than the release and uptake mechanisms of glutamate . Accordingly , we are confident that the effect we have found in Fig 6 is relevant: too much glutamate prevents recovery from SD . In the next section we will provide more insights into this effect . To understand how glutamate interferes with recovery , it is helpful to look at the membrane model , because the first step towards recovery is a change in the membrane state . A given set of ion concentrations determines the reversal potentials Eion and the pump current Ip . These quantities define the membrane model belonging to this ion configuration . We are now interested in the membrane models of the ion configurations around the repolarization point . That means that ion concentrations are now model parameters and we vary them such that we obtain the ion configurations on the depolarized fixed point branch near the repolarization point in Fig 5b . This parameter variation is naturally parametrized by ΔNK . The result of this continuation is shown in Fig 7a . There are two fixed points in the membrane model . One is depolarized and coincides with the fixed point of the whole transmembrane model . The other fixed point is in fact hyperpolarized . That is , it is more strongly polarized than EK . At the repolarization point , the depolarized state becomes unstable , while the hyperpolarized state continues to exist . The membrane potential drops very close to this point before ion concentrations and Nernst potentials re–adjust and bring the system to a slightly higher potential . The existence of a stable hyperpolarized membrane state is what initially drags the membrane potential down and is crucial to the neuron’s recovery . Let us have a closer look at the membrane states and compare the two shortly before the repolarization point . In Fig 7 , we have marked the two stable membrane states that exist for ΔNK = 285 fmol . Some quantities that characterize the membrane state are listed in Table D in S1 Text . The conductances in the depolarized state are dominated by the gated channels which can be seen from g K ≫ g K l ( and similar for Na+ ) . For the hyperpolarized state , the opposite is true and we have g K ≈ g K l instead . Accordingly , the hyperpolarized fixed point condition can be approximated as g K l ( V - E K ) + g N a l ( V - E N a ) + g C l l ( V - E C l ) = - I p . ( 32 ) We can draw the following conclusion from this relation . The pump current Ip is nearly maximal , because we assume a high concentration of extracellular K+ . Since the leak conductances are rather small , we conclude that the potential difference terms ( V − Eion ) must be sufficiently negative in the polarized fixed point . In particular , the large pump current forces the membrane potential below the K+ Nernst potential , which is what we call hyperpolarization . In summary , hyperpolarization is the result of a large pump current and very small conductances . If the leak conductances were not as small , the potential difference terms in Eq ( 32 ) would be less negative and depolarization would become weaker . In turn less depolarization will violate the approximation g K ≈ g K l and instead gK would be larger than g K l , which leads to even less depolarization . So we have an understanding of repolarization that demonstrates how important it is that the conductances of the neuron collapse strongly enough . The above consideration only took into account leak conductances and the normal gated ion channels . In addition to that , an elevated glutamate concentration in the cleft implies increased conductances of the NMDA and AMPA receptors as well and the same argument as above holds—the increased conductance implies a less polarized lower fixed point . Again , we increase Gc as a parameter and study its effect . In Fig 7b , we track the hyperpolarized state as we follow the end of the depolarized fixed point branch . For values from 0 mM to 0 . 0336 mM , there is a stable polarized membrane state when the upper fixed point branch ends . It becomes less polarized with increasing Gc which is consistent with the above reasoning on the importance of collapsing conductances . For very high Gc values , a hyperpolarized membrane state no longer exists and repolarization becomes impossible . The critical value is consistent with the value we derived from the transmembrane model in Fig 6b . In summary , the membrane model teaches us two things . First , a breakdown of neural conductances is needed for the neuron to repolarize sufficiently . Second , over-stimulated synapses can prevent this process and lead to recovery failure . To confirm model predictions , we recorded SD episodes in layers 2-3 of visual cortex slices from 15-24 days old , male wild type Sprague Dawley rats , both at individual neuron and network levels . To initiate SD , we replaced control ACSF with high KCl ( 26mM ) ACSF ( see Methods section ) . EC and IC electrodes were placed about 500μm apart . In high K+ ACSF , SD typically occurred 30-45s after high KCl application . SD in the single cells in control groups started with a rapid depolarization followed by several spikes and a slow return to the resting membrane potential . The majority of cell spiking occurred before maximum depolarization was reached . SD in the EC recording was typically noticeable a few seconds later . SD in single cells typically lasted for 30-180 seconds . To evaluate the effect of impaired astrocytic and higher glutamate concentration in the ECS on SD , slices were incubated with 50nM astrocytic glutamate transporter blocker TFB-TBOA for 20 minutes . Fig 8 shows a summary of these experiments . Example traces representing the membrane potential of individual pyramidal cell ( bottom traces ) and EC recording at the network level ( top traces ) during SD from control ( n = 9 ) and TFB-TBOA-treated ( n = 9 ) slices are shown in Fig 8a–8c . Unlike control slices where several spikes were observed before the cell entered a depolarization block , TFB-TBOA completely blocked action potentials at the single cell level . The resting membrane potential was not affected by the application of TFB-TBOA . The mean duration of SD averaged over many events at the network and single cell level is shown in Fig 8d and 8e respectively . SD duration was defined and measured as the time from the initiation of rapid depolarization in individual neurons and network to the time when the membrane potential repolarized to its pre-SD potential value ( dashed lines in Fig 8a–8c ) . In line with model predictions , blocking astrocytic glutamate transporters almost quadruples the duration of SD . It is also worth noticing that single neurons exhibit a stronger depolarization as compared to neurons in control slices . Furthermore , some neurons in control slices exhibit a behavior that is termed as mixed seizure and SD state [94 , 44 , 43] ( see for example lower trace for control cell in Fig 8c ) , while strong SD was observed in almost all single neurons in the slices treated with TFB-TBOA . We would like to remark that , although not exactly the same , the average SD duration given by the model is comparable to experimentally observed values . A 100% glutamate uptake in the model gives over a minute long SD as compared to ∼ 2 . 5 minutes long SD in control slices . This difference could be due to the higher K+ in the perfusion solution used in the experiment as compared to the model . It could also be due to our overestimation of the glutamate uptake ( or underestimation of glutamate release ) as lower uptake leads to longer SDs . Application of TFB-TBOA increases the duration of SD to almost 10 minutes . Decreasing the glutamate uptake by transporters to about 17 . 5% of the control value in the model leads to a comparable ( about 4 times ) increase in the duration of SD as compared to control simulation . Reducing the uptake to 16% of the control value will lead to SD duration comparable to the observed values . We would like to point out that TFB-TBOA targets only astrocytic glutamate transporters , while the reduction in glutamate uptake in the model applies to both neuron and astrocyte . So , the application of TFB-TBOA does not necessarily mean the complete inhibition of glutamate uptake . Given that glia cell has about eight times more binding sites for glutamate and has about half the surface area available for uptake ( see Diffusion and Glutamate Uptake section ) as compared to neuron , there would still be roughly one-fourth glutamate transporters intact ( ignoring other complications due to morphology , ion concentration dynamics etc . ) even in the presence of TFB-TBOA . We also observed time-dependent changes in the neuronal properties due to TFB-TBOA . Action potentials were evoked by current pulses and studied under the current-clamp conditions ( n = 9 ) . TFB-TBOA ( 50nM ) increased the cell membrane resistance from 150MΩ to 260MΩ ( p<0 . 05 ) after 10 minutes . Current pulses evoked fewer APs ( 7 versus 11 on average ) in slices pretreated with 50nM TFB-TBOA ( p<0 . 05 ) ( 0-100pA current injection ) . After 20min treatment with 50nM TFB-TBOA , the AP threshold increased from -45mV to -32mV ( p<0 . 05 ) , while the amplitude of AP decreased from 80mV to 30mV ( p<0 . 001 ) . AP amplitude was measured as the voltage difference between the threshold and the peak value of the membrane potential during the AP . We believe that these changes in the neuronal properties could be due to the reduction in Na+ influx due to TFB-TBOA . In line with this argument , Bozzo et al . [95] demonstrated the inhibitory effects of TFB-TBOA on astrocytic Na+ responses to glutamate . They also claimed that TFB-TBOA has no effect on the membrane properties of cultured cortical neurons recorded in the whole-cell patch clam . Recently , Hosseini-Zare et al [90] on the other hand , claimed that fast voltage-gated Na+ currents are reduced by TFB-TBOA . Whether the reduction in Na+ currents is caused by a direct interaction of TFB-TBOA with fast voltage-gated Na+ channels , through inhibition of Na+ cotransport ( notice that three Na+ are cotransported with one glutamate molecule ) , or through some other mechanism is not entirely clear . While our model includes the inhibition of Na+ cotransport through glutamate transporters , incorporating the effect of TFB-TBOA on fast voltage-gated Na+ currents , if proven unequivocally , in the model is beyond the scope of this study and will be investigated in the future .
Significant experimental and clinical data suggest that SD is involved in numerous brain pathologies including migraine , stroke , subarachnoid hemorrhage , and traumatic brain injury [13 , 12 , 3 , 4] . The initiation , propagation , sustainment , and termination of SD involve immense changes in many molecular and cellular pathways shaping the interplay between neurons , extracellular space , glial cells , and vasculature . What complicates things further is that most of these modifications are dependent on each other , and some may have biphasic role [96] . For example , over-activation of NMDA receptors in the early phase of stroke is detrimental , but in delayed phase , they might mediate neuroprotection through neuroplasticity [97] . That is probably why all trials testing NMDA antagonists for stroke treatment have failed [96 , 98] . In clinical trials , SD episodes were discontinued in two patients treated with ketamine on one hand [29] , while on the other hand , a cluster of SD occurred in another patient despite the presence of ketamine [99] . Thus a complete understanding of SD and finding clinically useful therapeutic interventions for the related pathologies hinge on elucidating this wide array of changes . However , the current experimental and clinical tools are too limited to simultaneous investigate all these changes , which necessitates physiologically relevant detailed computational models . In this paper , we developed a comprehensive model that incorporates many key elements involved in the dynamics of SD including: neuronal membrane potential dynamics , ion concentration dynamics in neuron , extracellular space , and glial cell , ion exchange with vasculature , swelling of neuron and glia , and detailed formalism of glutamate release and uptake processes . Although , we explore the effect of glutamate uptake and extracellular levels on the initiation , sustainment , and termination of SD , our model allows us to investigate the role of all these factors in the dynamics of SD simultaneously or one by one . Our results show that glutamate signaling plays a key role in the dynamics of SD since impaired glutamate uptake prolongs the duration of SD and leads to significant neuronal and glial swelling . Reducing glutamate uptake by transporters below 16% of the control value leads to the failure of cell’s recovery from SD . We verified this prediction experimentally by showing that SD in layers 2-3 of visual cortex from 15-24 days old rats are significantly prolonged by inhibiting glial glutamate uptake using TFB-TBOA . Our computational results are also consistent with a recent study , which showed that 0 . 5 and 1 mM TBOA prolonged SD by 148% and 426% respectively [28] . A respective increase of 167% and 374% in glutamate concentration was observed in the same experiments . Our result is also in line with conclusions from in vivo and in vitro studies in Ref . [80] , where the elimination of glial glutamate transporters were shown to lead to tonic increase in extracellular glutamate , resulting in widespread swelling and neuronal degeneration . Furthermore , increasing the expression of glial glutamate transporter EAAT2 through application of β-lactam antibodies significantly reduced extracellular glutamate in animal studies and protected against ischemic injury and neurodegeneration [33 , 32] . We would like to remark that the pattern of neuronal and glial swelling , and the dynamics of various ion concentrations observed in our model explains several experimental observations . We skip such details here and refer the interested reader to our recent work for a detailed discussion about these observations and the role of swelling in ischemic injury [56 , 53] . Furthermore , the dysfunction of glial glutamate transporters is also implicated in other acute and chronic neurological disorders including amyotrophic lateral sclerosis [100] , brain tumors [101] , epilepsy [102 , 103] , Alzheimer’s disease [104] , and motor discoordination [105] . Our approach can be adopted to quantify the role of different pathways involved in glutamate dynamics in these conditions . As mentioned above , our model skips several factors that could be key for glutamate homeostasis . For example , glutamate flux through astrocytic glutamate transporters reverses direction in the presence of high EC K+ or high IC glutamate concentration [106 , 107] . Thus , during SD where K+ and glutamate are both high , reversal of glutamate transport in astrocytes could become an additional source of EC glutamate build-up . The blockade of GLT transporters during SD in our experiments may have prevented glutamate transport reversal and caused glutamate to be trapped inside the glia , decreasing EC glutamate levels . Under different experimental conditions , however , it was observed that a different glutamate transporter blocker ( D , L-threo-beta-hydroxyaspartate ( THA ) ) increased the amount of depolarization and duration of SD in the presence of high K+ [108] . The changes in the single cell properties observed in our experiments are also ignored in our model . Furthermore , our study is concerned mainly with the behavior of a single neuron during SD . Investigating the effect of changes in glutamate homeostasis on the spatial spread of SD will require a network model . Incorporating these key factors in the model is beyond the scope of the current manuscript and is the subject of our future studies . We would also like to point out that there are different types of SDs with different features and probably different mechanisms for induction and propagation [109 , 3] . Our study focuses on SD caused by K+ perfusion and OGD . SD due to OGD is particularly relevant for stroke . Whether glutamate is necessary for propagation of all kinds of SDs is still debated . Interestingly , very little glutamate diffuses out of the cleft when uptake is not impaired in our model , consistent with a glutamate-independent propagation of SD in our modeling conditions . To conclude , by combining an established semi–phenomenological neuron–glia description and first physical principles in a consistent way , we have developed a physiologically relevant , comprehensive model that incorporates many key components involved in the dynamics of SD . This new mathematical framework describes many aspects of neuronal membrane , ion concentration dynamics , cell swelling , and glutamate dynamics during SD accurately and provides deep insights into the mechanisms through which glutamate interferes with neuronal recovery from SD . We present strong experimental evidence in support of our study , and emphasize that most of our explanations come from general physical principles and biophysical reasoning . The theory is general and the components included are key to both normal and pathological brain function . Accordingly , we claim that our approach can be used as a future guide to investigate the role of ion concentrations , ion exchange with glia and blood vessels , cell swelling , and glutamate dynamics in other brain pathologies and normal brain function . For example , to investigate glutamate homeostasis in regular neuronal firing , one would assume that only a few synapses are involved in the release of glutamate unlike SD where we assume all 10 , 000 synapses releasing glutamate . Similarly , to investigate these variables in seizures induced by high K+ , higher K+ concentration in the bath , Kbath ( typically 8mM ) should be used . Furthermore , the additional currents involved in the specific neuronal behavior in question should be included in the membrane potential ion concentrations dynamics .
|
Pathological conditions such as seizure , migraine , traumatic brain injury , and stroke are associated with extreme changes in ion concentrations and glutamate , cell swelling , and heavy exchange of matter between neurons , glia , and vasculature . However , current experimental tools are capable of measuring only a few of these variables , which necessitates the development of biophysically relevant models . This study provides a comprehensive computational framework derived from first physical principles and physiological facts that enables us to investigate a wide range of key variables in SD caused by K+ perfusion and oxygen-glucose deprivation . While we use SD as an example , our approach can be extended to other pathological conditions and normal brain function .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
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] |
2017
|
The role of glutamate in neuronal ion homeostasis: A case study of spreading depolarization
|
African animal trypanosomosis ( AAT ) , transmitted by tsetse flies and tick-borne diseases are the main constraints to livestock production in sub-Saharan Africa . Vector control methods such as pour-on offer individual protection against ticks but not against tsetse so far , for which protection has always been communal , through a reduction of their density . The latter requires the treatment of a large part of the herd in a given landscape and is not instantaneous . Two prospective surveys were conducted to evaluate the efficacy and persistence of a pour-on formulation composed of cypermetrhin , chlorpyrifos , piperonyl butoxid and citronella ( Vectoclor , CEVA Santé Animal ) . In experimental conditions , tsetse flies were exposed to treated and control cattle . Flies knockdown and engorgement rates were determined and the product persistence was assessed as the time for these parameters to drop below 50% ( T50 ) . T50 was 37 days ( 95%CI: [33–41] days ) and 46 days ( 95%CI: [39–56] days ) for the knockdown and engorgement rates respectively . In field conditions , two cattle herds were monitored following a case-control experimental design , in the Adamaoua region of Cameroon . One herd was treated once with Vectoclor pour-on ( treated group ) and the second used as a control group ( not treated ) . Ticks infestation rate , trypanosomosis prevalence and packed-cell volume were measured over the two months following treatment . The treatment was highly effective against ticks with a complete elimination three days after application in the treated group . Trypanosomosis prevalence was also significantly reduced during the study ( by 4 , P<0 . 001 ) and PCV of the treated group increased significantly in the same time ( P<0 . 001 ) , contrary to the control group . The protection of this new pour-on against tsetse bites and trypanosomosis is demonstrated here for the first time . Moreover , this insecticide and repellent mixture offer a longer persistence of the efficacy against both tsetse and ticks than similar products currently on the market . It offers a great new opportunity for an integrated AAT control strategy including the treatment of residual cases with trypanocides . It might also allow controlling the spread of resistance against these trypanocides .
Ticks and tsetse are the main vectors of diseases of economic importance to the livestock industry in Africa [1] . The economic cost of ticks borne diseases , mainly babesiosis , cowdriosis and anaplasmosis has been estimated between US$ 13 . 9 and 18 . 7 billion [2] . Ticks also cause direct injuries associated to strong economical loses , especially Amblyomma variegatum [3] . Also , African animal trypanosomosis ( AAT ) is one of the major constraints to livestock production in many sub-Saharan African countries infested with tsetse flies [4] . The economic cost of AAT in Africa has been estimated at USD 4 . 75 billion per year [5] . Together , ticks and tsetse are major constraints to the development and intensification of cattle rearing systems in Africa . So far , trypanosomosis is mainly controlled through prophylactic and curative drugs . This approach is no longer sustainable , because of the increasing development of drug resistance [6] . Alternatively , different vector control methods are available to reduce infestation impact such as insecticide-treated targets ( ITT ) for tsetse ( e . g . traps and screens impregnated with insecticides ) and insecticide-treated cattle ( ITC ) for ticks and tsetse ( e . g . pour-on , spray and dip ) [7] . ITT are highly effective to control tsetse and are probably the most cost-effective technique but are difficult to be maintained by farmers since they often consider insecticide traps and targets as public goods which are generally not maintained after the end of government programs [8] . ITC act as a very attractive lethal trap for tsetse due to their odor , movement and size and have also a wider spectrum of action , especially against ticks , stomoxines , tabanids and in some cases , mosquitoes [9–11] . Pour-ons are easy and convenient to use when compared with dipping and spraying technics . There are ready-to-use liquid formulations applied along the cattle backline . They are also more costly than other insecticide treatment strategies but do not require to be mixed with water , a great advantage in some situations , particularly in the case of transhumant herds . As many other control means , limits in pour-ons use are their persistence and efficiency , mainly against insecticide resistant species , such as the Asian cattle tick Rhipicephalus microplus that is currently invading Africa [12] . This species is resistant to most of acaricides families such as organophosphates , pyrethroids , amitraze and ivermectin [13 , 14] leading to major economic losses to cattle producers through direct and indirect effects as blood sucking and transmission of infectious disease agents . Therefore , there is a need to develop new vector control tools such as more persistent and efficient formulations against these important vectors that are ticks and tsetse flies . CEVA-Santé Animale recently developed a new pour-on product , so-called Vectoclor , which is composed of two insecticides , cypermethrin ( 5g/l ) and chlorpyrifos ( 7g/l ) , mixed with piperonyl butoxide ( 5g/l ) , a pyrethroids synergist , and citronella that acts as a repellent ( 0 . 5g/l ) . This mixture has been previously tested in South America and seemed to be the most efficient formulation against the multi-resistant tick R . microplus [15] . Indeed , one of the most promising strategies to prevent or delay the development of resistance in vectors is the use of products that combine at least two molecules having unrelated modes of action [16] . Theoretically , pests that are resistant to one insecticide should be killed by the other component . This product is encountering a great commercial success in Africa but persistence and efficiency of this new formulation has not been yet tested against tsetse flies and ticks in Africa . Therefore , the aim of this study was to evaluate the protective effect of this new product against trypanosomosis vectors and ticks in experimental and field trials .
This first experiment was conducted from August to November 2009 at the Centre International de Recherche-Développement sur l’Elevage en zone Subhumide ( CIRDES ) , Burkina Faso , in an experimental stable covered by a metal screen . Six crossbred cattle ( Zebu/Baoule , the most frequently encountered cattle breed in Burkina Faso ) of comparable size ( 150-225kg ) were used for these trials . Before the study and for each repeat , each animal was presented before treatment to tsetse flies and the engorgment rate was measured to assess any differences of attractiveness or defense reactions . Cattle were treated either with Vectoclor pour-on or with Cypertraz pour-on , the latter considered as positive control . Cypertraz formulation is also based on a mixture of two insecticides ( amitraze , 17 . 5 g/l and cypermethrin 15 g / l ) without any repellent or synergist . The effect of amitraze could be neglected against tsetse flies because it has been highlighted to be ineffective and none-persistent ( less than one week ) . For that reason , it has been used as control in previous studies [17] . Two replicates were conducted starting on 23 August and 27 October 2009 , and animals used as controls ( not receiving any treatment ) in the first trial were treated in the second . Therefore , for each replicate , four animals were used: 1 treated with Vectoclor pour-on , 1 treated with Cypertraz , and 2 untreated control cattle . For each replicate , cattle were exposed 10 to 12 successive times to tsetse flies after treatment . The Table 1 shows the number of exposures and the type of treatment carried out on each animal . The animals were exposed to sunlight for 3 hours and watered entirely with 50 liters of water every other day to mimic natural conditions in the rainy season . All along the study , cattle were housed separately in a stable . Trials started the day after the treatment of cattle with Vectoclor and Cypertraz . Tsetse flies were exposed to treated-cattle every 5 days and the experiment ended when tsetse flies knockdown rate was below 50% for 5 successive sessions . The stables were routinely washed after each session and humidified 1 hour before each release to ensure a humidity rate above 75% . The temperature and humidity were measured during all release sessions . To assess the impact of insecticide treatments on flies , 100 males of Glossina palpalis gambiensis were released on an animal placed in a stable covered with a metal net for two hours , from 8:00 to 10:00 AM . After exposure , tsetse flies were collected and classified according to their engorgement state ( blood feed or not ) and knockdown state . Tsetse flies that knocked-down after 2 hours were considered dead because it is assumed from previous work that the majority of paralyzed tsetse flies will perish in field conditions [18] . The protocol strictly adhered to the guidelines of the national ethical committee and general direction of veterinary services of the Ministry of Animal Resources of Burkina Faso . Animals used as baits were the property of CIRDES and were no longer exposed to tsetse bites than other animals in the natural environment . They also received veterinary care as much as required during the whole experiment . Experiment 2 was approved by the Department of Parasitology and Parasitological Diseases of the School of Veterinary Medicine and Sciences of the University of Ngaoundere , Cameroon , which give necessary permissions . Cattle belonged to a local farmer and informed consent was obtained from him before insecticide treatments and blood sampling were carried out in his herd .
Cattle attractiveness was similar between the animals used in each repetition . For the first trial , the engorgement rate before any treatment was 0 . 58 ( SD 0 . 03 ) and similar between animals ( X-squared = 1 . 3422 , df = 3 , p-value = 0 . 7191 ) . For the second one , it was 0 . 89 ( SD 0 . 04 ) ( X-squared = 4 . 2749 , df = 3 , p-value = 0 . 2333 ) . The best model ( lowest AICc ) selected for the knockdown rate analysis retained the type of treatment and the duration since impregnation as variables that fitted well the data with no significant differences between the two trials session ( P = 0 . 98 ) . Fig 1 presents the KD rates of flies exposed to control or treated cattle ( Vectoclor and Cypertraz ) . The Vectoclor insecticide activity was longer than Cypertraz . The time taken to knockdown 50% of the exposed flies was 37 days ( 95%CI: [33–41] days ) for Vectoclor and 28 days ( 95%CI: [24–32] days ) for Cypertraz . No KDR effect was observed in the control all along the experiment . Results of the tsetse flies engorgment rate model ( here considered as proxy of the protective effect of the treatments against tsetse bites ) retained the type of treatment and time since impregnation . Data fitted well with no significant differences between the two trials session ( P = 0 . 22 ) . Fig 2 presents the protective effects of treatments over time . The Cypertraz showed an irregular protective effect , especially during the second trial session with an average T50 of 21 days ( 95%CI: [15–28] days ) whereas the Vectoclor was more regular and persistent with 46 days ( 95%CI: [39–56] days ) .
The objective of this study was to evaluate a new pour-on formulation against trypanosomosis vectors in experimental conditions in Burkina Faso , and ticks and trypanosomosis infection in cattle in Adamaoua region , Cameroon . Comparative results obtained between Vectoclor and Cypertraz highlighted that the first one has a longer protective effect against tsetse bites ( i . e . repellency ) and a longer insecticide effect . Although Cypertraz protection was acceptable , the observed differences were probably due to the repellent effect of the Vectoclor formulation . In field conditions , Vectoclor application was highly effective against ticks with a complete elimination , three days after application . Trypanosomosis prevalence was reduced by 4 during the study and PCV of the treated group increased in the same time . The experimental trial compared Vectocolor formulation to Cypertraz with the latter considered as a positive control . These two products are based on a mix of insecticides and share the same synthetic pyrethroid , cypermethrin . Vectoclor also contains a pyrethroid synergist and a repellent and their additive effects resulted in a more effective and persistent formulation than Cypertraz ( cypermethrin mixed with amitraze ) . Nonetheless , these two products present a good protection against tsetse bites in comparison to other epicutaneous treatments [25 , 31] . Indeed , the T50 for the tsetse KD rate measured in this study was much higher than previous study conducted under the same conditions at CIRDES for animals entirely sprayed with a 0 . 005% solution of deltamethrin ( T50 3 days , 95%CI = [0–5] days for Vectocid ) [25] or with a 0 . 005% solution of alpha-cypermethrin ( T50 3 days , 95%CI = [0–5] days for Dominex ) [31] . Results obtained for Cypertraz was comparable to a flumethrin pour-on ( 1 mg of active ingredient/kg ) with a T50 for KD rate of the exposed flies of 28 days ( 95%C . I . [24–32] days ) [32 , 33] , but the Vectoclor persistence was better ( T50 , 37 days , 95%CI = [33–41] days ) . The combination of a repellent to several insecticides in Vectoclor appeared highly interesting since it is currently the most effective and persistent product against tsetse flies . This repellent effect probably had a positive effect on the knockdown rate since flies that cannot feed are more sensitive to the exposure to a given dose of insecticide and thus more likely to die [34 , 35] . Field trial of Vectoclor proved that it was very effective against ticks and tsetse . Separately , Vectoclor active ingredients have been proven effectives against tick and tsetse [25 , 36] but some resistance was reported , especially against the ticks such as Rhipicephalus ( Boophilus ) microplus [37] . In the study area , three species of ticks have been found: Amblyomma variegatum , Rhipicephalus ( Boophilus ) annulatus and Rhipicephalus ( Boophilus ) decoloratus . This result are in accordance to previous studies [22] , although species relative abundance was not the same probably due to seasonal effect . Few males of the genus Boophilus were collected . It was difficult to collect them due to their small size and location below the females . Although an alarming spread of the Asian cattle tick R . microplus in West and Central Africa is currently ongoing , this species was not found in our study and a previous one in the same area [22] . Impregnation of cattle with Vectoclor pour-on was efficient to control ticks in field conditions . Indeed , despite a higher initial infestation intensity in the treated herd , ticks were eliminated three days after treatment in the treated herd and one month after treatment , the observed infestation rate remained three times lower than the control herd . Previous studies using different pour-on formulations of pyrethroids or organophosphate reached this low ticks burden 2 to 3 weeks after pour-on treatment [38] and sometimes after repeated applications . This same observation was made for footbath acaricide treatment where repeated applications were needed to reduce ticks infestation near to zero [39] . Moreover , we observed an overall significant difference of cattle trypanosomosis prevalence between the control and treated herds over the two months study period , with no positive animal until day 7 in the treated group , probably due to the important effect of citronella repellent and pyrethroids volatiles against tsetse flies that are highly susceptible [25] . Although the trypanosomosis prevalence observed at the end of the study was higher than the one observed at the beginning ( 44 . 8% and 27 . 8% respectively ) , these data are in accordance with the annual prevalence of trypanosomosis in this region ( 55 . 2% ) [40] . This result may probably not reflect the exact parasitological status of cattle because the buffy coat method is not as sensitive as the PCR methods to detect trypanosome infections [41] . However , the significant increase in PCV observed all along the study period in the treated herd suggests that parasite load was significantly reduced , on the opposite to the control herd where PCV slowly decreased . Although entomological data on tsetse apparent density was not available , parasitological results showed that treated cattle experienced a significant reduction in the host vector contact , with a trypanosomosis prevalence rate below 10% in two months . The two main tsetse species present in the Adamaoua region are G . morsitans submorsitans and G . tachinoides and both species are susceptible to cypermethrin [25] . However , since the two herds grazed and drank water in the same areas , the observed difference in prevalence was probably due to the partial individual protection observed during the experimental trial than a communal protection linked to a reduction of tsetse densities in the area but this need to be confirmed by field entomological studies . This also could explain why the protective effect was so fast to appear in the treated herd ( < 10 days ) . This new pour-on formulation presents several advantages especially its immediate effect on ticks and the low treatment frequency needed to maintain a low ticks infestation and trypanosomosis prevalence . In the study area , only one treatment per month is advisable to maintain a tick infestation rate below 10 ticks/animals and a two month frequency seems sufficient to maintain a trypanosomosis prevalence below 10% whereas the parasitological prevalence reached very high levels in the control herd ( up to 46% ) . This is probably the result of combining two insecticide molecules ( cypermethrin and chlorpiryphos ) with a synergist and a repellent . Combinations are particularly interesting when there is potentiation between the two insecticides as this would make it possible to lower the dosage of each , as demonstrated under laboratory conditions [42] . This is the case for Vectoclor that contained three times less cypermethrin than Cypertraz ( i . e 5g/l against 15g/l ) . Insecticides and repellent mixtures have also been successfully used in public health against mosquitoes that present increasing levels of insecticide resistance , and this combination proved highly effective [43 , 44] . This is also the case for the Vectoclor combination that has been shown to be effective against the invasive and multi resistant ticks Rhipicephalus ( Boophilus ) microplus [15 , 45] . One drawback of this product could be its cost , especially for farmers from developing countries . Actually , comparing with the Vectoclor emulsifiable concentrate ( EC ) formulation , the cost of treating one animal with the Vectoclor pour-on formulation is 2 . 3 times higher . It is noteworthy however that the treatment cost per one animal using Vectoclor EC formulation is only 40% more expensive than an EC formulation containing deltamethrin only and provided by the same manufacturer ( Vectocid ) . This might represent a valuable investment , although the individual protection against trypanosomoses by the Vectoclor EC formulation has still to be confirmed . In conclusion , the insecticide mixture of Vectoclor was highly effective against AAT and ticks and to our knowledge is the most persistent pour-on on the market . However , this does not mean that the use of Vectoclor will fully prevent cattle trypanosomosis and ticks infestation everywhere , in any conditions , especially in the presence of mechanical vectors such as tabanides . The efficacy of vector control tools are context dependent and their effect could be different according to different vector species or environment [46] . They however represent a necessary tool to combine with trypanocide treatment of the remaining clinical cases within an integrated management vision . The use of Vectoclor pour-on will allow reducing the number of trypanocide treatments and thus the selection pressure which should eventually result in a reduced spread of resistance [47] . According to our results , this new insecticide formulation represents a partial individual protection in addition to a collective control method against trypanosomosis vectors and ticks .
|
In sub-Saharan Africa , tsetse and tick borne disease are the main constraints to livestock production . Providing farmers effective products to control animal pests is a challenging task in the context of increasing resistance to insecticide in many vectors and reduction of available insecticide molecules . Moreover , the spread of invasive species of high economic importance such as the tick Rhipicephalus microplus stress the development of new tools . In this study , we evaluated the protective effect of a new pour-on formulation against tsetse , trypanosomosis and ticks in experimental and field trials . This product based on a mix of two insecticides , a repellent and a synergist prove to be very effective with an immediate effect on ticks and tsetse and low treatment frequency to maintain a low ticks infestation and trypanosomosis prevalence . This new insecticide formulation represents an important innovation in the field of vector control , offering a partial individual protection in addition to a collective control method against trypanosomosis vectors and ticks .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion",
"and",
"Conclusion"
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] |
2016
|
Insecticide and Repellent Mixture Pour-On Protects Cattle against Animal Trypanosomosis
|
Bacteria constantly face stress conditions and therefore mount specific responses to ensure adaptation and survival . Stress responses were believed to be predominantly regulated at the transcriptional level . In the phototrophic bacterium Rhodobacter sphaeroides the response to singlet oxygen is initiated by alternative sigma factors . Further adaptive mechanisms include post-transcriptional and post-translational events , which have to be considered to gain a deeper understanding of how sophisticated regulation networks operate . To address this issue , we integrated three layers of regulation: ( 1 ) total mRNA levels at different time-points revealed dynamics of the transcriptome , ( 2 ) mRNAs in polysome fractions reported on translational regulation ( translatome ) , and ( 3 ) SILAC-based mass spectrometry was used to quantify protein abundances ( proteome ) . The singlet oxygen stress response exhibited highly dynamic features regarding short-term effects and late adaptation , which could in part be assigned to the sigma factors RpoE and RpoH2 generating distinct expression kinetics of corresponding regulons . The occurrence of polar expression patterns of genes within stress-inducible operons pointed to an alternative of dynamic fine-tuning upon stress . In addition to transcriptional activation , we observed significant induction of genes at the post-transcriptional level ( translatome ) , which identified new putative regulators and assigned genes of quorum sensing to the singlet oxygen stress response . Intriguingly , the SILAC approach explored the stress-dependent decline of photosynthetic proteins , but also identified 19 new open reading frames , which were partly validated by RNA-seq . We propose that comparative approaches as presented here will help to create multi-layered expression maps on the system level ( “expressome” ) . Finally , intense mass spectrometry combined with RNA-seq might be the future tool of choice to re-annotate genomes in various organisms and will help to understand how they adapt to alternating conditions .
All living organisms constantly remodel mRNA and protein abundances as a response to environmental factors or in the course of development and differentiation . In order to realize adequate responses , gene expression has to be controlled by sophisticated regulation networks . Besides transcriptional regulation , it is now broadly appreciated that post-transcriptional and post-translational events have a non-negligible importance and help to explain the discrepancy between mRNA and protein levels regularly observed in biological systems [1] . Interestingly , mRNA-protein correlations might be fairly high , with Pearson coefficients ranging between 0 . 66 and 0 . 76 as measured for the budding yeast Saccharomyces cerevisiae [2] , [3] . Weaker correlations are assumed to be partly biased by methodological constraints , and technical improvements therefore tend to increase the measured correlations [4] . However , a significant portion of all genes are obviously subject to post-transcriptional regulation , as demonstrated for one-third of all genes in Saccharomyces cerevisiae , exhibiting an altered translational efficiency upon starvation [5] . In the genome-reduced bacterium Mycoplasma pneumoniae it was recently shown that translational control has a stronger regulatory influence on protein levels than protein turnover [6] , which clearly underlines the importance of assessing the translatome for gene regulation studies . The translatome is defined as the sum of mRNAs captured in ribosomes for translation . Several studies employed ribosome profiling combined with microarray-based methods to calculate changes in actively translated mRNAs . For example , in the haloarchaeal model species Halobacterium salinarum and Haloferax volcanii translational efficiency was monitored at different growth stages [7] , while in the gram-positive bacterium Lactococcus lactis and in the fission yeast Schizosaccharomyces pombe ribosome occupancy and ribosome density were assigned [8] , [9] . Other attempts included ribosome footprinting together with deep sequencing of RNA or purification of affinity-tagged ribosomes followed by microarray analysis in Saccharomyces cerevisiae [5] , [10] . However , none of these studies directly compared translatomic data to both transcriptomic and proteomic data . Quantitative proteomics is still the bottleneck of comparative approaches , since the numbers of identified proteins regularly drag behind expected numbers . In yeast extensive mass spectrometry ( MS ) -based proteomics was applied to overcome this problem and coverage of the entire proteome was finally claimed by identification of ∼4 . 400 proteins [11] , and recently , a stunning number of nearly 12 . 000 proteins were identified in human cell lines [12] . However , these might be outstanding cases , even though advances in MS-based proteomics are steadily increasing . One of the main tasks is the accurate quantification of changes in protein abundance between different cellular states . The SILAC method ( stable isotope labeling of amino acids in cell culture ) addresses this problem by the use of heavy amino acids [13] . Peptides either contain the heavy or the light form of amino acids – usually arginine and lysine – and therefore give a distinct mass difference which enables quantification by direct comparison of peptide peak intensities . SILAC is the method of choice for mammalian systems , but has also been applied to newts , nematodes , yeast , and bacteria like Escherichia coli and Bacillus subtilis [11] , [14]–[19] . However , in bacteria SILAC-based proteomics and the use of translatomics as described above are highly under-represented when compared to transcriptome experiments . In order to gain a comprehensive picture of bacterial regulation , several “omics” should be applied simultaneously in an integrative approach . To follow up such a strategy , Rhodobacter sphaeroides was chosen as model for the investigation of bacterial stress responses . Rhodobacter species are well investigated with regard to regulation of photosynthesis genes [20]–[22] , and in particular R . sphaeroides has been established for studying the photo-oxidative stress response in anoxygenic phototrophs [23] , [24] . Photo-oxidative stress occurs whenever singlet oxygen is generated , which mainly happens during photosynthesis [25] . We systematically investigated the transcriptome at early and late time-points of the stress response by microarray analysis , which revealed expression dynamics for stress-dependent mRNAs but also small regulatory RNAs ( sRNAs ) . In addition , stress-specific sigma factor regulons were analyzed . The proteome was assessed by SILAC-based MS , using an indirect quantification approach by applying a heavy standard consisting of different culture conditions . Finally , changes of mRNAs in polysome fractions ( translatome ) were measured by microarray analysis to investigate translational regulation , which closed the gap between mRNA and protein levels . This is one of the most comprehensive studies on bacterial stress responses reported so far , combining several “omics” for genome-wide applications , and will serve as an example for future perspectives in bacterial system biology .
It is regularly assumed that most of regulation is accomplished on transcript level . Up to now , no global transcriptome analysis of the photo-oxidative stress response in R . sphaeroides was conducted and there are only two studies referring to this topic indirectly [24] , [26] . Here , total RNA , isolated before stress ( reference ) and at several time-points thereafter , was applied to microarray analysis to calculate relative changes in mRNA abundance ( Figure 1 , transcriptome ) . Data were collected for the short-term response ( 7 min ) and for two later time-points ( 45 and 90 min ) in biological duplicates . Reproducibility of experiments was high , as reflected by Pearson correlation r ranging between 0 . 80 and 0 . 98 , and ratios showed a typical Gaussian distribution ( Figure S1 ) . A number of 65 mRNAs ( 1 . 5% ) was significantly induced in expression after 7 min of stress ( log2 ratio ≥0 . 8 and p-value <0 . 05; Table 1 ) . At 45 min a higher portion of mRNAs was up-regulated ( 158 mRNAs ( 3 . 7% ) ) , which was followed by a slight drop at 90 min ( 115 mRNAs ( 2 . 7% ) ) . The photo-oxidative stress response obviously exhibited a peak of induction at time-point 45 min . Moreover , up-regulated mRNAs of the two late time-points showed a bigger overlap to each other than to mRNAs of the 7 min time-point ( Figure S2 ) , which was further confirmed by hierarchical clustering ( data not shown ) . Therefore , mRNAs belonging to the short-term and/or the late stress response could be distinguished . However , the existence of a core set , comprising 51 mRNAs up-regulated at all time-points , was emerging ( Figure S2 ) . This applied to , e . g . , the master regulators RpoE and RpoH2 , photolyase PhrA , and the detoxifying glyoxalase II ( GloB , RSP_0799 ) . Numbers of down-regulated mRNAs ( log2 ratio ≤−0 . 8 ) showed a similar trend as observed for up-regulated mRNAs , that is , most changes occurring at 45 min ( Table 1 ) . In a former study , expression kinetics of selected genes , measured by qRT-PCR , indicated that levels of individual mRNAs may peak at different time-points of the stress response [27] . In our study , we identified 173 mRNAs , showing significant induction at one of the three time-points , which could be grouped into three dynamic expression clusters according to k-means using the TM4 Microarray Software Suite ( Figure 2A and Table S1 ) . The biggest cluster was further divided into three sub-clusters ( 2a–2c ) . mRNAs within clusters 1 , 2a , and 2b had an increased induction throughout the time-course , albeit induction was most pronounced at the two later time-points in several cases . This trend was even more obvious in cluster 2c . In contrast , the smaller cluster 3 comprises mRNAs which had a peak expression at 7 min . Functional groups were formed according to published data on the R . sphaeroides singlet oxygen stress response [27]–[31] , combined with KEGG database searches ( http://www . genome . jp/kegg/ ) . Cluster 1 contains several genes with a function in stress defense or iron metabolism , like phrA , gloB , or bacterioferritin encoding bfr ( Figure 2B ) . Interestingly , several genes for regulatory factors with a known or hypothesized role in the photo-oxidative stress response ( namely rpoE-chrR , rpoH2 , rpoH1 , ompR ) are found in cluster 1 , which differs from cluster 2 in a stronger induction of corresponding mRNAs ( several log2 ratios >1 . 5 ) . Functional groups in cluster 2 relate to stress defense , chaperones , proteases , redox reaction , transport process , porphyrin and carbohydrate metabolism ( Figures 2C–E ) . Several genes in cluster 2 are described for the first time to be part of the singlet oxygen response . They encode , e . g . , the chaperones MoxR ( RSP_1024 ) , ClpA ( RSP_2293 ) , and GroES ( RSP_2310 ) , a thioredoxin ( RSP_0725 ) , and DNA ligase Lig2 ( RSP_2413 ) . In addition , genes for the transcriptional regulator Lrp ( RSP_2719 ) and a TetR family regulator ( RSP_2853 ) were newly identified as stress-responsive ( see Table S5 ) . In contrast to cluster 1 and 2 , genes of cluster 3 clearly belong to the short-term response and their products mainly have a function in amino acid and sulfur metabolism , like phosphoglycerate dehydrogenase SerA ( RSP_1352 ) and sulfite reductase CysI ( RSP_1942 ) . In addition , several chaperones can be found in cluster 3 ( Figure 2F ) . Many genes of the photo-oxidative stress response are transcriptionally regulated by the two alternative sigma factors RpoE and RpoH2 . Upon stress , RpoE is ultimately activated by release from its anti-sigma factor ChrR , while RpoH2 is produced downstream of RpoE [27] , [29] , [32]–[34] . We therefore assumed that members of the two sigma factor regulons exhibit different kinetics . Indeed , genes within the RpoE regulon were strongly up-regulated already after 7 min of stress , while RpoH2-dependent genes showed a delayed induction and full expression was not observed before 45 min ( Figure 2H ) . Assignment of the RpoH2 regulon was based on genome-wide predictions for conserved promoter sequences [28] , [29] . Very recently , an alternative RpoH2 regulon was defined according to expression profiles and ChIP-chip experiments [35] , which we used to compile expression kinetics from our data sets . Intriguingly , results for both RpoH2 regulons were nearly congruent with each other ( Figure S2 ) . We were also interested in expression patterns of sRNAs , which are important post-transcriptional regulators in bacteria . Microarrays designed for this study included probes for 144 verified and potential sRNAs that have been identified in our group [36] , [37] . sRNA expression kinetics resembled those described above for mRNAs and similar dynamic clusters could have been formed ( Figure S2 ) . Northern blot validation clearly demonstrated , that the RpoE-dependent RSs0019 sRNA is highly expressed during the whole time-course of the experiment , while expression of the RpoH2-dependent RSs0680a sRNA exhibited a peak at 7 min ( Figure 2G ) . Investigation of late time-points revealed that RSs0827 is strongly induced only after prolonged singlet oxygen stress ( Figure 2G ) . RSs0827 was recently shown to respond to iron limitation [38] and can now be placed on the growing list of ( photo- ) oxidative stress inducible sRNAs in bacteria . The SILAC method was invented to enable quantitative proteomics of complex protein samples by MS [13] . For the photo-oxidative stress response of R . sphaeroides , we made use of an indirect quantification approach by applying a heavy standard generated by SILAC-labeling . R . sphaeroides cultures were supplemented with the heavy amino acid 13C6-lysine ( Lys6 ) , allowed to grow , and subsequently diluted several times into fresh Lys6-containing medium to achieve complete labeling of proteins ( incorporation rate of 96% , Figure S3 and Dataset S2 ) . The heavy standard represents a protein mixture obtained from fully labeled cultures grown under semi-aerobic , aerobic , and singlet oxygen stress conditions to cover a broad protein pattern from various physiological states . Therefore the heavy standard was referred to as “bacterial SILAC standard” according to the super-SILAC mix of human breast cancer cells [39] . The bacterial SILAC standard was mixed in a 1∶1 ratio with protein samples from unstressed ( reference ) and stressed cultures , which were grown in presence of the light amino acid 12C6-lysine ( Lys0 ) . Based on intensity differences between heavy and light peptide peaks derived from LC-MS/MS analysis , SILAC protein ratios were calculated . To verify SILAC protein quantification , two protein digestions ( insol and ingel digestion ) were performed in biological duplicates ( n = 4 ) . Pearson correlation r between replicates ranged between 0 . 86 and 0 . 94 , which reflects the high reproducibility of the SILAC approach ( Figure S4 ) . Next , mean SILAC ratios of the quadruplicates were divided to calculate direct protein ratios between stressed and unstressed cultures ( ( heavy standard/reference ) / ( heavy standard/90 min 1O2 ) ) , which were statistically verified by determining p-values ( Figure 1 , proteome ) . The SILAC approach identified 1538 proteins with at least two peptides , from which 1214 proteins were quantified ( Figure 3 and Dataset S2 ) . The distribution of the log2 protein ratios exhibited a Gaussian-like curve , which underlines the reliability of the approach . At time-point 90 min of the photo-oxidative stress response , 68 proteins ( 5 . 6% ) were significantly up-regulated ( log2 ratio ≥0 . 8 , p<0 . 05; Table 1 and S3 ) , while 45 proteins ( 3 . 7% ) were significantly down-regulated ( log2 ratio ≤−0 . 8 , p<0 . 05; Table 1 and S3 ) . Several of the up-regulated proteins have a function in stress defense , redox reactions , carbohydrate metabolism , and transport processes or are acting as proteases ( Figure 3 ) , which corresponds to observations made for mRNAs changed during the photo-oxidative stress response ( Figure 2 ) . Among the down-regulated proteins , two major groups relate to photosynthesis as well as motility/chemotaxis ( Figure 3 ) . Altogether , these data demonstrate that the SILAC approach highlights proteins with altered abundances upon stress with high confidence . In contrast to microarrays , which are designed according to the available information of the annotated genome , the SILAC approach presented here is annotation-independent . Consequently , several peptides were identified which potentially represent new open reading frames ( ORFs ) ( Table S4 ) . In order to validate these putative ORFs on RNA level , we inspected RNA-sequencing ( RNA-seq ) data available in our group ( these data are based on deep-sequencing of RNA from exponentially growing wild-type cultures under semi-aerobic conditions and will be published elsewhere ) . First , RNA-seq data were screened for the presence of cDNA reads at the particular position of a new ORF . When cDNA reads gave reliable coverage for the gene locus , apparent transcriptional start sites were related to potential translational starts . In 13 out of 19 cases , RNA-seq strongly supports the SILAC data , while for the remaining examples sequencing coverage was not sufficient ( Table S4 ) . For some new ORFs it is very likely that they represent stand-alone genes ( Figure 4A–D ) , while others are located between or in front of genes and might therefore be part of operons ( Figure 4E–F ) . BLAST searches suggested functions in , e . g . , transcription regulation ( ID-19ORF-14558 and ID-29ORF-1154 ) or translation ( ID-24ORF-1183 ) . Intriguingly , two proteins derived from potentially new ORFs were up-regulated under singlet oxygen stress . This applies to gene ID-41ORF-21 ( log2 protein ratio 0 . 85 ) , which shows homology to uridylate kinases and might therefore have a role in cell division [40] . RNA-seq data suggested ID-41ORF-21 to be transcribed from its own promoter internal to RSP_4289 ( Figure 4G ) . The new ORF ID-30ORF-1184 ( log2 protein ratio 0 . 78 ) is located downstream of a groEL gene and might be functionally related to chaperone functions . Unfortunately , cDNA read coverage is too low to assume co-regulation of ID-30ORF-1184 with groEL ( Figure 4H ) . As a conclusion , our SILAC approach is a powerful tool for both the identification of unknown ORFs and simultaneous quantification of corresponding protein levels . Its application to any stress response in bacteria will give valuable and new insights . One of the main issues to be addressed in this study is the question of how and to which extent changes at transcriptome level impact protein abundances . In this context , the translatome was of major interest since it represents the mRNA-protein interface . The translatome was assessed by microarray analysis of mRNAs in polysome fractions with high reproducibility between biological duplicates ( Pearson correlation r = 0 . 91 , Figure S1 ) . Polysomes were enriched by sucrose density gradient centrifugation of crude extracts after chloramphenicol treatment of cells ( Figure 1 and S5 ) . As for total RNA and proteins , changes of polysomal mRNAs after 90 min of stress were calculated relative to the reference ( no stress ) . Transcriptomic , translatomic , and proteomic data sets at time-point 90 min were correlated to each other and visualized as scatter-plots ( Figure 5 ) . The transcriptome and translatome showed a fairly high correlation ( r = 0 . 64; n = 4251 ) with a major distribution of data-points in the middle of the plot , representing genes that exhibited no or only minor changes . However , 98 genes were up-regulated on both transcriptional and translational level . Besides well-known genes , several candidates were newly identified for the photo-oxidative stress response , which is exemplified by moxR and clpA , encoding chaperones , and thioredoxin RSP_0725 ( Figure 5A ) . In contrast , 51 genes were translationally triggered without showing a comparable increase on transcriptome level ( log2 ratio difference of at least 0 . 4; Figure 5A ) . In this group several genes could be linked to the photo-oxidative stress response for the first time , as shown for regulators ( lexA and the sigma factor/anti-sigma factor operon RSP_3095-94 ) , chaperones ( groES and groEL ) , as well as quorum sensing ( cerI and cerA ) . A different picture emerged when correlating the proteome data; overall correlation to the transcriptome was fairly high ( r = 0 . 63; n = 1199 ) , as indicated by genes that were up-regulated in both approaches . However , a group of 43 genes was decreased in protein abundance ( log2 ratio ≤−0 . 8 ) , while not changed on transcriptome level to the same extent . A major portion of this group has a function in photosynthesis ( 12 genes; Figure 5B ) . A similar observation was made when comparing the proteome and translatome ( r = 0 . 55; n = 1194 ) ; 13 photosynthesis genes with decreased protein levels were not depleted in polysomes . Interestingly , 31 genes showed an increased emergence in the polysome fraction after stress without changing protein abundance , as observed for the regulator LexA , the autoinducer synthesis protein CerI , and the chaperone GroES ( Figure 5C ) . In general , it emerged that ∼41% of mRNA variance within polysomes ( translational effects ) can be explained by changes in the transcriptome ( r2 = 0 . 41 , Figure 5A ) . However , only ∼31% of variance in protein levels could be assigned to changes in translation ( r2 = 0 . 31 , Figure 5C ) , although ∼39% of this variance could be explained by mRNA levels ( r2 = 0 . 39 , Figure 5B ) . It appeared that , beside unidirectional effects , substantial regulation occurred separately on all levels . This can be exemplified by expression changes of lig2 , encoding a newly identified stress-related DNA ligase . Protein and mRNA levels are both induced , although polysome association is rarely changed . A possible explanation would point to parallel regulation on both transcriptional and post-translational level without influencing translation itself . Since technical limitations cannot be excluded , these examples need further validation and present interesting subjects for future studies . In the genome-reduced bacterium Mycoplasma pneumoniae almost half of the polycistronic operons show a staircase-like expression [41] . Furthermore , operons can be divided into suboperons with dynamics depending on environmental conditions . It was assumed that this phenomenon is widespread in bacteria , which motivated us to inspect expression patterns of stress-induced operons of R . sphaeroides carefully . Notably , relative changes between time-points rather than relative expression levels at one time-point were matched . When comparing changes of total RNA levels after 90 min of singlet oxygen stress ( transcriptome ) , a staircase-like pattern emerged as a common feature . In most cases , log2 ratios decreased from the first to the last gene in an operon ( Figure 6A–F ) , from now on referred to as 5′ polarity . Other operons exhibited no polarity ( Figure 6G ) or featured a 3′ polarity ( Figure 6H ) . At translatome level orientation of polarity and even particular ratios were quite similar to transcriptomic data , indicating that transcriptional polarity impacts translation . There was one exception: genes within the RSP_3164-62 operon were equally regulated at transcriptome level , but exhibited a 5′ polarity at translatome level ( Figure 6G ) . Furthermore , in some cases it was indicated that both 5′ and 3′ polarity are transmitted to protein levels ( Figure 6B , E , F , H ) , for the remaining operons proteome data were not complete enough to give a reliable picture . When comparing expression levels at time-points 0 min and 90 min separately , it emerged that polarity is more pronounced after stress ( Figure S6 ) , which indicates that operon polarity might be inducible .
The simple concept of transcriptional regulation being the key determinant of gene expression fails to explain the observed flexibility of bacterial adaptation . Steadily increasing numbers of investigations reveal that complexity of bacterial regulation nearly resembles that of eukaryotic cells [42] . To fully understand how bacteria explore complex gene expression control in response to stress , changes have to be measured on the system level , and in order to appreciate post-transcriptional and post-translational events , system biology studies need to consider multiple layers of regulation . The “omics” approach presented here compared relative changes in abundance of total mRNAs , mRNAs captured in polysomes for translation , and proteins after sub-lethal stress . The singlet oxygen stress response of R . sphaeroides served as an example to validate our approach . While mRNA levels can be easily quantified by microarray analysis on an almost-global scale , comprehensive proteomics relies on state-of-the-art MS combined with accurate quantification methods . Here we used an indirect quantification approach by applying a heavy standard generated by SILAC . The bacterial SILAC standard represents several physiological states and is therefore similar to the super-SILAC approach applied to mammalian systems [12] , [39] . We successfully identified 1538 proteins , from which 1214 could be accurately quantified with high reproducibility ( Figure 3 ) . In Bacillus subtilis , which has a genome size comparable to R . sphaeroides , 1928 proteins were identified by MS during logarithmic growth , representing more than 75% of genes expected to be expressed [19] . In R . sphaeroides the number of expressed genes under aerobic and singlet oxygen stress conditions is not known , and therefore , estimations of protein coverage are difficult . However , our approach seems to be as comprehensive as comparable studies . Several new open reading frames were identified , which were partly validated by RNA-seq ( Figure 4 ) . Two of the newly identified genes showed stress-dependent expression and thus extend the list of singlet oxygen responsive genes . During the last years several studies applied differential RNA-seq to identify sRNAs and to globally map transcriptional start sites in bacteria [43] , [44] . In the near future , MS-based protein identification/quantification together with RNA-seq in a combinatory approach may be the experimental tool of choice to re-annotate genomes and to generate expression maps in various organisms . Bacterial stress responses are highly dynamic in many ways . Up to date no studies comparing the transcriptome of R . sphaeroides in presence of singlet oxygen to non-stress conditions were presented and kinetics for mRNA levels or protein synthesis rates after onset of singlet oxygen stress were available only for few examples [25] . With the data provided here , stress-related genes could be globally identified and grouped according to their expression patterns , which also allowed us to distinguish short-term responses from late adaptation processes . For example , some genes involved in sulfur metabolism exhibit a pulse expression during the first minutes of the singlet oxygen stress response in R . sphaeroides ( Figure 2F ) . This pulse expression might be explained by an incoherent feed-forward loop [45] , which either consists of protein regulators or also includes sRNAs , as recently described for regulation of photosynthesis genes in R . sphaeroides [46] . Vice versa , regulators may not only cause pulse-expression but are subject to pulse-expression themselves , as monitored for the RSs0680a sRNA ( Figure 2G ) . Quantification of transcripts from alternative sigma factor regulons revealed that individual activation characteristics of the sigma factors under stress will ultimately determine expression dynamics of downstream genes ( Figure 2H ) . While RpoE-dependent genes are constantly induced , RpoH2-dependent genes show a delayed induction and are most likely important for late adaptation processes . Furthermore , our study revealed transcripts/proteins with singlet oxygen-dependent abundance , which were not recognized as stress-dependent before ( Figure 5D and Table S5 ) . Interestingly , the new members of singlet oxygen-dependent genes comprise several transcriptional regulators including a sigma factor/anti-sigma factor system and the cerI and cerA genes [47] suggesting that singlet oxygen affects quorum sensing . Our study also extends the list of genes for chaperons , iron metabolism , and sulfur metabolism which respond to singlet oxygen stress . Another dynamic expression feature in bacteria applies to operon polarity , which was described for Mycoplasma pneumoniae [41] . The observation of a staircase-like expression pattern , with the first gene in an operon showing the highest expression , might be explained by transcriptional-translational coupling and cross-talk between the responsible machineries in such a way , that transcription is interrupted whenever ribosomes hit a stop codon [42] . Our results reveal that the particular position of a gene within a stress-inducible operon not only impacts expression but also the degree of induction , with the first gene mainly be induced to the highest degree , what we refer to as 5′ polarity ( Figure 6 ) . Several of the stress-inducible operons in R . sphaeroides exhibit a clear 5′ polarity on both transcriptome and translatome level . Interestingly , polarity of transcript levels is clearly more pronounced after stress indicating that polar expression is stress-induced ( Figure S6 ) . Unidirectional behavior of transcriptional and translational effects was regularly observed for large sets of genes , as e . g . under severe stress in yeast or in halophilic archaea [7] , [10] . However , 5′ polarity may only be achieved by post-transcriptional mechanisms , including sRNAs or RNA secondary structures , leading to non-correlated induction ( Figure 6G ) . It was assumed that expression polarity is compensated for on protein level [6] . For the stress-inducible operons presented here it appears that polarity on transcriptome/translatome level also entails polar changes of protein abundance , which argues against compensation in these particular examples . Finally , the occurrence of 3′ polarity ( Figure 6H ) implies that several distinct mechanisms underlie the polarity phenomenon . Differential stability of polycistronic mRNAs and other post-transcriptional regulation events might explain the observed diversity . However , bacteria obviously exploit the order of genes within operons to fine-tune gene expression . We systematically investigated the transcriptome , translatome , and proteome to explore global correlations and estimate the importance of particular modes of regulation upon stress in bacteria . Direct dependence between transcript levels and translation is observed for ∼41% of all mRNAs ( r2 = 0 . 41 , Figure 5A ) , while ∼59% may be subject to post-transcriptional events which alter translation irrespective of mRNA levels . A predominant class of regulators that act on the post-transcriptional level are trans encoded sRNAs [48] . Regulons controlled by sRNAs may be as large as described for GcvB that impacts on ∼1% of all transcripts through a conserved binding domain [49] . In addition to sRNAs , mRNA stability , mRNA structure , and RNA-binding proteins play important roles for translation . Here we show that translational control is a fundamental way to globally induce genes upon stress in bacteria , which is similar to observations in yeast [10] . In R . sphaeroides this is reflected by the fact that after 90 min of singlet oxygen stress 3 . 0% of genes are up-regulated on translatome level compared to only 2 . 7% on transcriptome level ( Table 1 ) . Assessing the translatome should therefore be regularly considered when investigating cellular stress responses , either in addition to or as an alternative to transcriptome studies . The SILAC approach delivers further valuable insights into global regulatory events . The observation that only ∼31% of protein changes can be matched to translational changes ( r2 = 0 . 31 , Figure 5C ) is unexpected , but can be explained by altered turn-over and degradation of proteins upon stress . Proteins are a major target for singlet oxygen in cells and damaged or fragmented proteins need to be removed [50]–[52] . The stress-dependent induction of several proteases is consistent with increased protein turn-over in presence of singlet oxygen but their particular roles in stress-dependent protein turn-over need experimental verification . The decline of photosynthetic proteins points to post-translational regulation by degradation , which may be achieved by the above-mentioned stress-induced proteases . Since reactions of singlet oxygen with amino acids can lead to depletion of the amino acid pool [53] , selective degradation of photosynthetic proteins would both replenish the amino acid pool and avoid additional singlet oxygen generation in photosynthetic complexes . However , various post-translational events might cause that the ribosome coverage is only poorly correlated to protein abundance and we assume that comparing translation to protein synthesis , e . g . by using pulsed SILAC [54] , would be a valuable experiment to achieve higher correlations . Despite all the challenging open questions , our integrative approach delivered a comprehensive list of genes that are relevant to the singlet oxygen response and provided conclusive evidence for their regulation . For example , for quorum sensing genes ( cerI , cerA ) as well as for several genes encoding regulators ( lexA , RSP_3095-94 ) substantial regulation seems to occur only on the translational level . This might be the reason why these genes have been overseen in former studies . The current data set will encourage detailed studies on newly identified players of the bacterial response to singlet oxygen .
For all experiments conducted in this study Rhodobacter sphaeroides wild-type 2 . 4 . 1 [55] was cultivated at 32°C in minimal salt medium with malate as carbon source [56] . Pre-cultures were grown in Erlenmeyer flasks with continuous shaking at 140 rpm , resulting in a dissolved oxygen concentration of approximately 25 mM ( semi-aerobic conditions ) . The amino acid 12C6-lysine ( Lys0; Sigma-Aldrich ) or its stable isotope counterpart 13C6-lysine ( Lys6; Silantes ) were added to the cultures in a final concentration of 50 µg ml−1 . Pre-cultures were grown until an optical density at 660 nm ( OD660 ) of ∼0 . 8–1 . 0 was reached and subsequently diluted into fresh Lys0/Lys6-containing medium in a concentration of 0 . 5% ( v/v ) , which was repeated two times . Cultivation of R . sphaeroides in the presence of Lys6 over several generations enabled full labeling of proteins with stable isotopes ( Figure S3 ) . Lys0-treated cultures were used for regular stress experiments , while Lys6-labeled cultures were applied to generate a heavy standard for SILAC-based mass spectrometry . For stress experiments , semi-aerobic pre-cultures were diluted with Lys0/Lys6-containing medium to an OD660 of 0 . 2 and methylene blue was added in a final concentration of 0 . 2 µM . Cultures were gassed with air in flat glass bottles , resulting in a dissolved oxygen concentration of approximately 180 mM ( aerobic conditions ) , and grown in the dark to an OD660 of 0 . 4 . Singlet oxygen was generated by applying high light ( 800 W m−2 ) with a white light halogen bulb as described [23] . Samples , collected at the indicated time-points , were rapidly cooled on ice and centrifuged at 10 , 000g for 10 min at 4°C . For polysome preparation , cells were treated with chloramphenicol in a final concentration of 0 . 1 mg ml−1 for 5 min at 32°C before harvesting . Heavy labeled and non-labeled cells were completely lysed in SDS-Buffer ( 4% SDS in 100 mM Tris/HCl pH 7 . 6 ) and shortly heated at 95°C . Sonication was performed for DNA sharing prior to sample centrifugation at 16 , 000g for 5 min . Protein concentration of the clear supernatant was measured by DC protein assay ( Biorad ) to mix labeled and non-labeled protein samples in the same amount . To reduce sample complexity for MS-analysis , proteins were separated by SDS-PAGE ( NuPAGE 4%–12% Bis-Tris gel , Invitrogen ) and stained with Colloidal Blue Staining Kit ( Invitrogen ) . Each lane was cut into 14 gel pieces for in-gel digestion as described [57] . In brief , proteins were reduced by 50 mM dithiothreitol ( DTT ) , alkylated with 550 mM iodoacetamide and digested with the endopeptidase Lys-C ( enzyme to protein ratio 1∶100 , Wako ) . After digestion and elution , peptides were desalted by stop and go extraction ( STAGE ) tips [58] . Next to in-gel digestion , samples were also digested in solution as described [59] . For LC-MS/MS , a nano liquid chromatography ( LC ) system ( Thermo Fisher Scientific ) was coupled to a LTQ-Orbitrap Velos or a Q-Exactive mass spectrometer ( Thermo Fisher Scientific ) via a nanoelectrospray source ( Proxeon ) . Fused silica emitter were packed in-house with C18-AQ RepoSil-Pur ( 3 µm , Dr . Maisch GmbH ) and used as columns for reverse-phase chromatography to separate peptides by a linear gradient of 5–30% acetonitril with 0 . 5% acetic acid for 150 or 240 min at a flow rate of 200 nl min−1 . After elution , peptides were ionized and transferred to gas-phase by electrospray ionization ( ESI ) to enter the mass spectrometer . For measurements with the LTQ-Orbitrap Velos mass spectrometer , full MS scan spectra ( m/z = 300–1650 ) were acquired in the Orbitrap with a resolution of R = 60 , 000 after accumulation of 1 , 000 , 000 ions . The 15 most intense peaks from full MS scan were isolated and fragmented in the linear ion trap after accumulation of 5 , 000 ions . Fragmentation of precursor ions was performed using CID ( 35% normalized collision energy ) prior to acquisition of MS/MS scan spectra . Q-Exactive measurements were performed as described [60] . The 10 most intense peaks were selected and fragmented by higher energy collisional dissociation . Analysis of raw data was performed by the MaxQuant software package ( version 1 . 2 . 2 . 9 ) as described [61] . Database searches were performed with the Andromeda search engine against a house-made R . sphaeroides 2 . 4 . 1 database . The two chromosomes and five plasmids of R . sphaeroides 2 . 4 . 1 were translated into protein sequences using EMBOSS Transeq [62] . Translation was performed for all six reading frames . A unique identifier indicating the sequence position and frame was generated and assigned to each resulting open reading frame ( ORF ) longer than six amino acids . The generated ORF database was combined with all public available protein sequences for R . sphaeroides 2 . 4 . 1 and then used for the peptide identification step . By applying a decoy approach we determined the false discovery rate ( FDR ) to be smaller than 1% . After peptide identification , database entries belonging either to the de novo generated ORF set or to the public available annotated proteins were clustered into protein groups ( MaxQuant ) . Groups lacking an annotated member were assumed to be potentially new coding sequences and were selected for further investigation . Detection and quantification of SILAC pairs was performed by MaxQuant using following parameters: Lys-C as digesting enzyme with a maximum of two missed cleavages , carbamidomethylation of cysteins as fixed modification , oxidation of methionine and acetylation of the protein N-terminus as variable modifications , SILAC amino acid labeling: Lys6 . Maximum mass deviation was set to 7 ppm for the peptide mass and 0 . 5 Da for MS/MS ions . For identification of peptides and proteins a FDR of 1% were used and only peptides with minimum of six amino acids length were considered for identification . For SILAC analysis , two ratio counts were set as a minimum for quantification . Bioinformatic analysis was performed with Perseus ( version 1 . 3 . 0 . 4 ) to calculate p-values with a Benjamini-Hochberg multiple testing correction based on a FDR threshold of 0 . 05 . Polysomes were prepared basically as described elsewhere [63] . Cell pellets derived from 200 ml chloramphenicol-treated cultures were resuspended in 4 ml cold polysome buffer ( P buffer: 10 mM Tris pH 7 . 6 , 60 mM NH4Cl , 3 mM Mg ( CH3COO ) 2 ) and used for lysis by gentle sonication in an ice bath . The cell debris was removed by centrifugation at 15 , 000g for 10 min at 4°C . Three ml supernatant were applied to sucrose density gradients , which were prepared by layering 3 ml 0 . 9 M sucrose on 3 ml 1 . 8 M sucrose ( as solutions in P buffer ) in 13 . 2 ml polyallomer thinwall tubes ( Herolab ) . Ultracentrifugation ( 200 , 000g , 16 hours , 4°C ) was carried out using a SW41-Ti rotor ( Beckman Coulter ) in a Discovery 90 ultracentrifuge ( Sorvall ) . The gradient was divided into nine fractions and used for downstream validation ( Figure S5 ) . The pellet representing the polysome fraction was layered with 100 µl P buffer and incubated on ice for up to 3 hours to enable complete resuspension of polysomes . Polysome fractions were used for RNA isolation . RNA from both crude extracts and polysome fractions was isolated using the hot phenol method [64] , followed by one ( Northern ) or two ( microarray ) chloroform-isoamylalcohol treatments and precipitation with sodium acetate and ethanol . RNA was resolved in RNase-free water ( Roth ) and concentrations were determined at a NanoDrop 1000 Spectrophotometer ( Peqlab ) . RNA for Northern blot detection was directly used after isolation , while RNA for microarray analysis was further processed . Total RNA for microarrays was treated with DNaseI ( Invitrogen ) to remove contaminating DNA , followed by purification using the RNeasy MinElute Cleanup Kit ( Qiagen ) . RNA from polysome fractions was purified accordingly . Absence of DNA was monitored by PCR using Taq DNA Polymerase ( Qiagen ) and primers RSP0799-A ( 5′-GAA CAA TTA CGC CTT CTC ) and RSP0799-B ( 5′-CAT CAG CTG GTA GCT CTC ) [23] . Polyacrylamide-gels ( 10% , v/v ) containing 7 M urea were prepared to assess RNA quality . For gene expression studies , isolated RNA was hybridized to Custom Gene Expression Microarrays from Agilent Technologies ( 8x15K; ID: 027061 ) designed for R . sphaeroides wild-type 2 . 4 . 1 [65] . The arrays contain oligodeoxynucleotide probes ( 60-mers ) for 4304 open reading frames , according to genome annotations available on the IMG server ( Integrated Microbial Genomes; img . jgi . doe . gov/cgi-bin/w/main . cgi ) , and for 144 putative sRNAs identified in our group [36] , [37] . Two µg RNA from reference ( no stress ) and stress samples were chemically labeled with Cy5 and Cy3 , respectively , using the ULS Fluorescent Labeling Kit for Agilent arrays ( Kreatech ) and competitively hybridized to arrays ( two-color microarrays ) . Fragmentation of labeled RNA , hybridization to arrays , and washing was performed using the Gene Expression Hybridization and Wash Buffer Kits according to the specifications of Agilent . Hybridization was performed at 65°C for 17 hours . Read-out files for arrays were generated with the Agilent DNA microarray scanner , followed by compilation of raw median fluorescence values using the Feature Extraction Software ( Agilent ) . Within-array normalization according to LOESS was accomplished with the Bioconductor package Limma for R [66] . Those values were retained that exhibited an average signal intensity ( A-value: 1/2 log2 ( Cy3×Cy5 ) ) above background , as specified by Agilent control probes present on each array ( Poly 90 min A≥10 . 44; Total 7 min A≥10 . 27; Total 45 min A≥10 . 61 , Total 90 min A≥10 . 45 ) . Fold changes were calculated from remaining values as log2 ratios ( Cy3/Cy5 ) . Data shown in this study represent the results from two individual microarrays ( biological replicates ) , each containing a pool of three independent experiments for each sample . Statistical analysis was performed by Perl Statistics modules . Targets having p-values <0 . 05 and log2 ratios ≥0 . 8 or ≤−0 . 8 were assumed to represent deregulated candidates . For expression cluster analysis , log2 ratios were imported to MeV ( Multi Experiment Viewer version 4 . 7 . 4 ) from the TM4 Microarray Software Suite [67] , [68] and visualized as heat-maps . Clustering was based on k-means ( KMC method ) according to Euclidean distance with a maximum of 50 iterations . The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus [69] and are accessible through GEO Series accession number GSE42244 ( http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE42244 ) . For detection of sRNAs , 10 µg of total RNA were separated on 10% ( v/v ) polyacrylamide-gels containing 7 M urea and 1× TBE . Gel runs were performed at 300 V for approximately 3 hours in 1× TBE . RNA was transferred to SensiBlot Plus Nylon Membranes ( Fermentas ) by semi-dry electroblotting in 1× TBE ( 250 mA , 3 hours ) , followed by cross-linking with UV light . 5′ end-labeling of oligodeoxynucleotides with [γ-32P]-ATP as well as hybridization , washing , and documentation of membranes was performed as described elsewhere [36] . Oligodeoxynucleotides for probe generation were: p-0019 ( 5′-GAG ATA GCT CAT CGG TCA GGT CC ) , p-0680a ( 5′-CGT CGC CGC TGC TGC TAC AGG TC ) [36] , and p-0827 ( 5′-GGA CAG TGA AGG TAG AAC GG ) [38] . RNA for sequencing was isolated as described for microarray analysis . R . sphaeroides 2 . 4 . 1 cultures were grown under semi-aerobic conditions to a final OD660 of 0 . 4 . The cDNA libraries were prepared at Vertis Biotechnology AG ( Germany ) . For this , the RNA samples were poly ( A ) -tailed by poly ( A ) polymerase . After that , the 5′-PPP residues were removed using tobacco acid pyrophosphatase ( TAP ) followed by the ligation of the RNA adapter to the 5′-phosphate of the RNA . First-strand cDNA synthesis was performed using an oligo ( dT ) -adapter primer and the M-MLV reverse transcriptase . The resulting cDNAs were PCR-amplified to about 20–30 ng µl−1 using a high fidelity DNA polymerase . The primers used for PCR amplification were designed for TruSeq sequencing according to the instructions of Illumina . The following adapters sequences flank the cDNA inserts ( the NNNNNN indicates the barcode sequence used for multiplexing ) : 5′-end: 5′-AAT GAT ACG GCG ACC ACC GAG ATC TAC ACT CTT TCC CTA CAC GAC GCT CTT CCG ATC T-3′ and 3′-end: 5′-CAA GCA GAA GAC GGC ATA CGA GAT-NNN NNN-GTG ACT GGA GTT CAG ACG TGT GCT CTT CCG ATC TTT TTT TTT TTT TTT TTT TTT TTT T-3′ . The cDNA libraries were purified using the Agencourt AMPure XP kit ( Beckman Coulter Genomics ) , analyzed by capillary electrophoresis and finally sequenced by an Illumina GAIIx machine . The sequences of the obtained sequencing reads were quality trimmed by the program fastq_quality_trimmer from the FASTX program suite with a cut-off phred score of 20 . Poly ( A ) tail sequences were clipped from the 3′ end of the sequences , the resulting sequences were filtered by length and sequences short than 12 nt were discarded . The remaining reads were aligned to the reference genome sequences ( accession numbers: CP000143 . 1 , CP000144 . 1 , CP000145 . 1 , CP000146 . 1 , CP000147 . 1 , DQ232586 . 1 , DQ232587 . 1 ) using the short read mapper segemehl [70] . Based on these read mapping , coverage plots which represent the number of mapped reads per nucleotide were created . Those were visualized and examined in the Integrated Genome Browser [71] .
|
Bacteria are frequently exposed to disadvantageous conditions , like elevated temperatures or nutrient depletion . The ability to maintain viable populations is based on cellular stress responses , which are regulated in a complex manner with different outputs on different regulatory levels . For example , mRNA levels do not ultimately determine protein amounts since translation of mRNAs can be influenced irrespective of mRNA levels . To appreciate nature and frequency of these regulatory events , multi-layered experimental approaches are required on a global scale . The photo-oxidative stress response of the purple bacterium Rhodobacter sphaeroides was chosen as a model . Changes of total mRNAs ( transcriptome ) and ribosomal-bound mRNAs ( translatome ) were monitored by microarrays . The proteome was assessed by mass spectrometry , applying a “bacterial SILAC standard” for indirect quantification , an approach which additionally identified new open reading frames . Integration of the three expression levels provided a comprehensive insight into regulatory events and identified new stress-responsive genes , including genes for transcriptional regulators and for quorum sensing . We found that translational control exceeds simple regulation on the transcriptional level . Furthermore , polar expression patterns within inducible operons point at the possibility of expression fine-tuning by gene positioning .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"systems",
"biology",
"genome",
"expression",
"analysis",
"spectrometric",
"identification",
"of",
"proteins",
"prokaryotic",
"models",
"cellular",
"stress",
"responses",
"model",
"organisms",
"molecular",
"cell",
"biology",
"microbial",
"physiology",
"protein",
"abundance",
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"microbiology",
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] |
2013
|
Integrative “Omics”-Approach Discovers Dynamic and Regulatory Features of Bacterial Stress Responses
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The [Het-s] prion of the fungus Podospora anserina represents a good model system for studying the structure-function relationship in amyloid proteins because a high resolution solid-state NMR structure of the amyloid prion form of the HET-s prion forming domain ( PFD ) is available . The HET-s PFD adopts a specific β-solenoid fold with two rungs of β-strands delimiting a triangular hydrophobic core . A C-terminal loop folds back onto the rigid core region and forms a more dynamic semi-hydrophobic pocket extending the hydrophobic core . Herein , an alanine scanning mutagenesis of the HET-s PFD was conducted . Different structural elements identified in the prion fold such as the triangular hydrophobic core , the salt bridges , the asparagines ladders and the C-terminal loop were altered and the effect of these mutations on prion function , fibril structure and stability was assayed . Prion activity and structure were found to be very robust; only a few key mutations were able to corrupt structure and function . While some mutations strongly destabilize the fold , many substitutions in fact increase stability of the fold . This increase in structural stability did not influence prion formation propensity in vivo . However , if an Ala replacement did alter the structure of the core or did influence the shape of the denaturation curve , the corresponding variant showed a decreased prion efficacy . It is also the finding that in addition to the structural elements of the rigid core region , the aromatic residues in the C-terminal semi-hydrophobic pocket are critical for prion propagation . Mutations in the latter region either positively or negatively affected prion formation . We thus identify a region that modulates prion formation although it is not part of the rigid cross-β core , an observation that might be relevant to other amyloid models .
Amyloids are protein aggregates with a cross-β structure . Amyloid folds are gradually being recognized as important components in the structural landscape of peptides and proteins . The amyloid fold has been envisioned as a generic primordial fold from which globular folds had to emancipate in prebiotic times to attain structural and functional diversification into globular proteins [1] , [2] , [3] , [4] , [5] . Amyloid folds also fulfill a variety of biological functions in relation to their specific structural properties [2] , [6] , [7] . Importantly , amyloids represent the underlying cause of a number of age-related protein deposition diseases which impose a major burden to modern societies [8] , [9] . Yet , the determinants that govern amyloid folding and stability are much less well understood than in the case of globular proteins in part because of the scarcity of available high resolution structures of amyloid proteins . Amyloids have the inherent property of being self-perpetuating and as such can represent the mechanistic basis for prion formation [10] , [11] , [12] , [13] . Many prions are amyloids that are self-perpetuating in vivo . Amyloid prions cause fatal neurodegenerative diseases in mammals and can be encountered as epigenetic elements in fungi [14] , [15] . The [Het-s] prion of the filamentous fungus Podospora anserina represents an example of such fungal prions [16] , [17] , [18] . Highly prevalent in nature , [Het-s] is involved in a non-self recognition process that takes place when cells of unlike genotype undergo fusion [16] , [19] . This process termed heterokaryon incompatibility leads to the cell death of the mixed fusion cells . The het-s gene exists as two incompatible alleles termed het-s and het-S . When a het-s and a het-S strain are confronted , the incompatibility cell death response leads to the formation of a macroscopic demarcation line termed barrage . It is proposed that this cell death reaction might have a more general function in fungal defense [20] , [21] , [22] . Strains of the het-s genotype exist as two alternate epigenetic states: [Het-s*] ( the non-prion-state ) and [Het-s] ( the prion state ) . Transition to the prion state can occur spontaneously at a low rate or can be induced systematically by contact with a prion-infected ( [Het-s] ) strain . The prion form then invades the fungal hyphal network at a rate an order of magnitude higher than the linear growth rate of the fungus [23] . Incompatibility is only triggered when HET-s is in the prion form ( [Het-s] ) . Thus in this system , the prion state corresponds to the active state of the protein . The [Het-s] prion also displays a specific effect in the sexual cycle , where presence of the prion form leads to specific abortion of the het-S spores in a sexual cross , a process designated spore-killing [24] . The HET-s protein displays two distinct domains , an N-terminal globular domain termed HeLo and a C-terminal prion forming domain [25] , [26] . The PFD encompassing residues 218 to 289 is natively unfolded in the soluble form of the proteins and adopts a specific β-solenoid fold in the amyloid prion form of the protein [25] , [27] , [28] , [29] . Cell death is triggered when [Het-s] interacts with HET-S because the HET-s PFD templates the folding of the homologous region of HET-S into the β-solenoid fold which in turn induces a refolding of the globular domain and exposition of a N-terminal hydrophobic helix which targets the cell membrane [30] , [31] , [32] . A second mode of activation of HET-S , apparently involves the NWD2 STAND protein encoded by the gene adjacent to HET-S which contains an N-terminal region homologous to the PFD region [21] , [33] . A high-resolution solid state NMR structure of the amyloid form of the HET-s PFD ( HET-s ( 218–289 ) based on more than 2500 distance constraints has been reported [28] , [29] . This domain adopts a β-solenoid fold in which β-strands delimit a triangular hydrophobic core . The domain is composed of two 21 amino acid long pseudo-repeats each of which forms one layer of β-strand in the β-solenoid structure , the two repeats are connected by a 15 amino acid long flexible loop . At the C-terminus of the domain , a C-terminal loop folds back onto the core region and forms a semi-hydrophobic pocket which can be considered as an extension of the hydrophobic core [28] , ( Figure 1A ) . The β-solenoid fold contains two aspargine ladders at the beginning of the first and last β-strand of each repeat and three salt bridges per monomer . The hydrophobic core contains essentially aliphatic residues and serine and threonine residues residing in different β-strand layers and forming a hydrogen bond within the core . A 8 . 5 Å cryo-EM model of HET-s ( 218–289 ) fibrils has also been reported and largely agrees with the ssNMR data . HET-s ( 218–289 ) are singlet fibrils with a left-handed twist and a helical pitch of 410 Å [34] . Sequence comparisons suggest an evolutionary conservation of the β-solenoid fold [35] , [36] . Characterization of a HET-S homolog of a different fungal species , the plant pathogen Fusarium graminearum revealed a conservation of the β-solenoid fold and of the prion formation ability [37] , [38] . Thus , the HET-s PFD sequence has been evolutionarily shaped to adopt this fold and the overall fold and the prion forming ability have been conserved over an extended evolutionary period . Perhaps , as a consequence of this evolutionary process , the HET-s PFD sequence does not to lead to the formation of amyloid polymorphs in physiological conditions as generally observed for disease causing amyloids or for yeast prions [13] , [39] . HET-s ( 218–289 ) adopts the same structure in vitro and when forming inclusion bodies during heterologous expression in E . coli [40] . Only at highly acidic pH , when the native β-solenoid fold cannot be attained , HET-s ( 218–289 ) adopts an alternate non-infectious amyloid structure [34] , [41] , [42] , [43] , [44] . Alanine scanning approaches have been widely used to analyze folding and stability of globular proteins and such approaches have also been used in the context of amyloid fibril formation and stability [45] , [46] . In the case of yeast prion proteins such as the well studied Sup35 model , the applicability of such approaches is complicated by the absence of a full structure model , the primary sequence independence in prion formation and the existence of multiple prion strains corresponding to different amyloid polymorphs [47] , [48] , [49] . Nevertheless , a recent study has revealed the importance of a glycine pair in [PSI+] prion formation as a structural determinant in the soluble form of the PFD [50] . The HET-s model constitutes a favorable system for such studies because of the lack of prion strain variants and the availability of a high resolution structure . A previous study has revealed the functional importance of the β-strand elements in the β-solenoid structure using a proline mutant approach [27] . Here , we relied on the more subtle alanine scanning approach to analyze the functional and structural role of specific residues and structural components of the HET-s PFD fold .
Previous studies have analyzed the evolutionary conservation of the β-solenoid fold [21] , [35] , [36] , [37] , [38] . Due to the rapid increase in the number of fully sequenced fungal genomes , many additional HET-S homologs sequences have become recently available , we have thus conducted a database search for HET-s/HET-S homologs in current fungal genome sequences . We could identify a total of 51 het-s/het-S homologs ( file S1 ) . Based on residues found at 33 of the Helo domain which is known to define HET-s and HET-S allele specificities [51] , [52] , the homologs found in the other species are of the HET-S , rather than HET-s-type ( Figure S1 ) , as suggested previously [18] , [21] , [30] , [37] . Some species like Nectria haematococca and some strains of the Fusarium oxysporum species complex contained up to 4 het-S paralogs . The majority of the sequences showed conservation of the 21 amino acid repeat regions ( R1 and R2 ) but in a fraction of the sequences there was a two amino acid deletion in the first repeated motif ( R1 ) . Figure 1B shows an alignment of the 22 non redundant sequences for which both 21-amino acid repeats are conserved . The level of identity between the sequences is in the range of 30% which is about the level of identity between the P . anserina HET-s ( 218–289 ) and F . graminearum FgHET-s ( 218–289 ) sequences that were previously found to display closely related structures [38] . It can thus be reasonably inferred that the sequences presented in the alignment share similar folds , as previously suggested by homology modeling approaches [35] , [36] . The positions showing strict conservation are two of glycines ( G242/G278 , position 17 of the 21 aa repeat ) located in the arc between the third and fourth β-strand of each rung , ( Figure 1A and B ) . The N226/N262 asparagine pair ( forming the asparagine ladder at the start of the first β-strand , position 1 ) was also conserved although some exceptions occurred . The second asparagine ladder ( N243/N279 , position 18 ) is not as strongly conserved as the first ladder . There is also a strong conservation of the hydrophobic residues in the core , large hydrophobic residues are found at core positions 231/267 ( position 6 ) , 239/275 ( position 14 ) and 241/277 ( position 16 ) . There is an interesting trend of mutual exclusion of large hydrophobic side chains at position 228 and 264 ( position 3 ) , suggesting that the presence of large residues at both positions might lead to sterical hindrance . Interestingly , two sequences ( Fo_4287-2 and Cg_HET-S ) constitute exceptions in that regard with an isoleucine found both at 228 and 264 . But in these sequences , an Ala residue is found in 231 which might allow for accommodation of the larger residues in position 3 ( i . e . residue 231 in Figure 1A ) . Although the residues forming the three salt bridges identified in HET-s ( in positions 4 , 9 and 11 ) are not strictly conserved , there is an overall preference for favorable charge interactions at these positions . In the 22 listed sequences , favorable charge combination occurs in 13 and 15 sequences at position 4 and 9 respectively while repulsive interactions are never found . At position 11 , favorable charge interactions are less conserved and occur in five sequences . Also of note , is the conservation of the glycine-rich C-terminal loop region encompassing the W287 and F286 aromatic residues . Conservation occurs not only on the aromatic positions but also the glycines and the charged residues ( K284 and D288 ) are strongly conserved . This region is however entirely missing in a fraction of the sequences . Of note is the fact that the sequences missing this region are found in species that contain other HET-S paralogs that do display the loop region . The conservation is poor in the central loop region between R1 and R2 . This region generally contains glycine residues and the length of the loop region varies from 12 to 18 residues . It was shown experimentally that three to five amino acid deletions in the loop region could be tolerated without affecting [Het-s] and HET-S function [26] , [27] . In order to achieve an overall comparison of the two repeats , we devised a separate consensus sequence for repeats R1 and R2 using MEME [53] ( Figure 1C ) . The consensus sequence reveals some marked differences . In the turn at position 10 , there is a preference for a Glu in R1 and a Gly residue in R2 . In position 12 , there is a preference for an Ala and Ser in R1 and R2 respectively , which is reflected by the reciprocal distribution in position 8 . Also , there is a strong conservation of the Gly at the end of R2 in the region leading into the C-terminal semi-hydrophobic pocket . The charge complementary mentioned above is also apparent in these consensus sequences; with a preference in R1 for positive charges in position 4 and negative charges in 9 and the opposite preference in R2 . This differential conservation of specific residues in the first and the second repeat suggest that both repeats are not strictly functionally equivalent . Globally , many aspects of the sequence conservation and variation in HET-s homologs are well explained by the structural information available . Yet , the role of certain features such as for instance the conservation of the Arg residue at position 13 is not directly apparent . We set out to analyze the structure-function relation in the HET-s PFD by generating a series of 32 alanine mutants along the HET-s PFD and by testing their prion activity in vivo in Podospora anserina . Based on the sequence conservation described above the β-strand region and the C-terminal pocket were specifically targeted ( Figure 1A ) . The mutation coverage amounts to one mutation every 1 . 8 residue in these regions . Mutations were introduced in a plasmid expressing full length HET-s and the corresponding plasmid was introduced into a Δhet-s strain as previously reported for proline and deletion mutants of the PFD [27] . For each mutation , at least 24 individual transformants were assayed for [Het-s] incompatibility function and for their ability to infect a [Het-s*] strain . In this experimental setting , transformants express the transgene from ectopic integration sites and in multiple copies . This approach allows a rough categorization of the mutants . Results are presented in Figure 2 . In most cases and as previously observed , the number of transformants able to infect a [Het-s*] strain is equivalent or higher than those producing a barrage reaction to [Het-S] [27] . This is likely due to the fact , that higher prion titers are required to produce an incompatibility reaction than to infect a [Het-s*] strain . We have categorized the mutants into three functional classes: mutations that lead to percentages of active transformants in the range of wild-type ( >60% of active transformants ) , those significantly reducing the number of transformants with [Het-s] activity ( >30%and <60% ) and those strongly affecting the number of active transformants ( <30% ) . The majority of the mutations ( 18/32 ) did not significantly affect [Het-s] activity in this assay . Nine mutants were slightly affected and five were strongly affected . The latter corresponded to two group of mutants , the mutants of the glycine residue at the sharp turn in position 17 ( G242A/G278A ) and the aromatic F286 and W287 residues in the C-terminal loop . The Ala replacement of the strongly conserved Tyr residue ending the second rung ( Y281 ) led also to a marked decrease in the number of active transformants . The other mutations having a lesser effect on activity were the mutation of the Asn pair forming the first asparagines ladder ( N226/N262 , position 1 ) . Mutation of the second Asn pair ( N243/N279 , position 18 ) had no effect in agreement with the lower conservation of this second N-pair . In general , mutation of the core hydrophobic residues did not reduce activity significantly with the exceptions of I231A and I277A . Also the Ala replacement at positions E234 , K270 and R236 affected the prion function . These residues are involved in the second and third salt bridges in the HET-s fold . Mutations in the residues forming the first salt bridge ( K229A/E265A , position 4 ) had no detectable effect on function in this assay . The variant Q240A showed a reduced activity . Q240 is located close to W287 in the structure [28] and it might be that Q240A also affects this pocket region . Indeed , solid state NMR studies of the variants at the C-terminus results in chemical shift changes of Q240 ( as discussed below and Figure S3 and S4 ) . The D288 residue is also located in that spatial neighborhood . The mutational approach points to an instrumental role of this region in prion propagation although this region is not part of the β-solenoid core . Based on this first series of mutants , we generated a second set of mutants to specifically test several hypotheses . First , because our results suggest that the salt bridges are not essential for activity , we generated a triple mutant that leads to inactivation of all three salt bridges ( E234A , E236A , E265A ) and found it to be active ( Figure 2 ) . In respect to this observation it is noteworthy that , there appears to be an overall conservation of the salt-bridges at position 4 and 9 of the repeat motif , but some HET-S orthologs such as Cg_HET-S lack these salt bridges completely ( Figure 1B ) . Next , we have generated a double mutant with both N ladders replaced ( N226A and N243A ) . This mutant lacked activity suggesting that at least one N-ladder is required for function and thus providing evidence for a functional role also for the second N-ladder . In both rungs there is a conservation of an Arg residue ( R238/R274 , position 13 ) . Yet , single Ala replacements at these positions showed normal function . In a R238A/R274A double variant however the prion function is affected . This result could explain conservation at that position . Finally , we reasoned that if a reduced activity of I231A is caused by a reduction of the hydrophobicity of the core , one might be able to introduce a compensatory mutation increasing hydrophobicity elsewhere in the core . We thus changed the A228 residue of the core by a larger residue . We generated a I231A/A228V double variant and found that this double mutant recovered wild-type like activity . We concluded that in this multicopy assay , the function of HET-s appears to be very robust with very few mutations having a very strong effect on prion activity , namely G242A and G278A in the turns after the triangular core and F286A and W287A in the C-terminal pocket . Double mutants could also demonstrate the functional importance of other elements such as the N-ladders , the hydrophobic core ( i . e . I231 ) , and the Arg row at position 238/274 . The multicopy assay allows an initial functional characterization of the mutants but does not allow for a detailed and sensitive analysis of phenotypic differences . We have thus selected a number of mutants for a more detailed functional characterization as single copy integrants . In order to have more subtle and sensitive assays for prion function , we have re-introduced the mutant allele at the resident het-s locus . We chose to further analyze a subset of seven single mutants N226A , Q240A , E272A , K284A , F286 , W287 , D288A and the R238A/R274A and T233A/S273A double mutants and the E234A/R236A/E265A triple mutant . Q240A , K284A , F286A , W287A and D288A were chosen to investigate the role of the C-terminal loop region . The triple E234A/R236A/E265A mutant was chosen to analyze in more detail the effect of the elimination of all three salt-brigdes . R238A/R274A and T233A/S273A double mutants were included to analyze respectively the role of the conserved Arg residues close to the loop and the role of the partner hydroxyl residues in the core . Strains with the corresponding gene replacements were generated and tested for [Het-s] function . The three mutants F286A , W287A and R238A/R274A led to a null phenotype as single copy integrants . This result confirms the functional importance of the aromatic residues of the C-terminal pocket and also stresses the functional importance of the conserved Arg residues at position 13 of each repeat . The remaining mutants were able to express the [Het-s] phenotype ( ie . infect a [Het-s*] strain and produce a barrage reaction to het-S ) and were tested for three other criteria of prion formation and propagation efficacy . First , we determined the spontaneous prion formation rate ( Table 1 ) . Secondly , we measured the rate of spreading of the prion infection in the mycelium , reflecting prion propagation efficiency ( Figure 3 ) . This propagation rate is measured in an experimental setting where the length of the barrage line corresponds to the distance of prion spreading in a given period of time [54] ( Figure S2 ) . Third , the rate of spore-killing in the sexual cycle ( a measure of prion maintenance during the sexual cycle ) was determined [24] , ( Table 2 ) . These three criteria have been compared previously between wt and a ΔPaHsp104 mutant . All three were found to be diminished in this mutant background [55] . In these assays , all mutants were affected for at least one of the measured criteria . There was in general a good correlation between the effect of the mutations on propagation rate and spore killing activity ( Figure 3 and Table 2 ) . Quantitatively the effects of the mutations were however stronger on spore-killing rates than on propagation rates . For instance N226A and Q240A were affected both in propagation rate and spore-killing activity . While E272A , D288A and T233A/S273A showed close to wt propagation and killing rates . K284A was an exception as this mutation affected spore-killing rates although the propagation rate was close to wt . The E234A/R236A/E265A triple mutant lacking all three salt bridges was significantly affected in both assays , suggesting that the salt bridges participate in [Het-s] function although the effect of the mutation went unnoticed in the more basic multicopy assay ( Figure 2 ) . When spontaneous prion formation rates were measured , it appeared that N226A and K284A had significantly reduced spontaneous prion formation rate ( Table 1 ) . For K284A , 700 strains were tested but none had spontaneously acquired [Het-s] . Interestingly , we measured an increase in spontaneous prion formation rate in D288A and the T233A S273A double mutant . These mutants represent to our knowledge the first HET-s mutants with enhanced prion behavior ( Table 1 ) . In order to get insights into the structural consequences of the Ala replacements , 2D 13C-13C dipolar assisted rotational resonance ( DARR ) solid state NMR spectra of amyloid fibrils of the 15N , 13C-labeled HET-s ( 218-289 ) variants K229A , I231A , V239A , Q240A , L241A , N262A , V264A , V267A , E272A , S273A , R274A , I277A , G278A , N279A , F286A , D288A and the double variant F286A/W287A were recorded and compared with the corresponding spectrum of 15N , 13C-labeled wild-type HET-s ( 218-289 ) amyloid fibrils [27] , [56] . Since the chemical shift is a highly sensitive probe of the electronic surrounding structure , the measurement of the 2D 13C-13C DARR spectrum can be regarded as a fingerprint of the 3D structure . Thus , a comparison of the DARR spectrum of wild-type and Ala-variant fibrils enables a straight forward analysis of the preservation or alteration of the β-solenoid fold ( Figure 1A ) . This atomic-resolution analysis is of particular importance in the establishment of a structure-activity relationship of amyloids because many amyloidogenic systems are prone to polymorphism and thus a secondary structure analyses or structural comparisons conducted at the mesoscopic level may not be not sufficient to reveal structural preservation or alterations . As shown in Figure 4 , the 2D DARR spectra of K229A , I231A , V239 , Q240A , L241A , N262A , V264A , E265A , V267A , E272A , S273A , R274A , I277A , E279A , F286A , W288A , and D288A closely resemble the wild-type spectrum . This indicates that the overall structure of the listed variants is conserved . A detailed inspection of the individual spectra shows in general , small chemical shift differences of residues close in space to the replaced amino acid side chain ( in addition to the lack of the signal of the replaced side chain ) , which is attributed to local structural changes . For example , in the 2D DARR spectrum of the N262A variant , cross peak changes of the spatially close residues I231 , T261 , S263 , V264 , and I277 are observed ( Figure 4 , Figure S3 , Figure S4 , Figure 1A ) . In the case of E265A located at the surface , no significant chemical shift changes are evident ( Figure 4 , Figure S3 , Figure S4 , Figure 1A ) . Similar findings are observed for all the other Ala variants ( but G278A , and the double variant F286A/W287A , see below ) as discussed in details in the figure caption of Figure S3 . In the case of I231A , a substantial amount of small local chemical shift perturbations are observed for A228 , K229 , D230 , T233 , V239 , L241 , T266 , V267 , V268 , V275 and I277 as well as for the more remote residues E235 , V244 , A247 , A248 , K270 , R274 , and L276 , hinting that I231 is a structurally important hydrophobic core residue . In addition to these local chemical shift alterations , ( slight ) perturbations of chemical shifts of I277 are found for many Ala variants ( i . e . I231A , Q240A , N262A , V264A , V267A , R274A , F286A , W287A ) indicating that I277 , which is the second Ile side chain in the center of the core of the β-solenoid structure ( Figure 1A ) , responses sensitively to slight structural alterations . In striking contrast to all the variants discussed , the 2D DARR spectrum of the 15N , 13C-labeled variant G278A is reproducibly distinct from the corresponding spectrum of wild-type HET-s ( 218–289 ) amyloids ( Figure 4 ) . The spectrum appears to be less well dispersed and the cross peaks are broad when compared to wild-type HET-s ( 218–289 ) amyloids . The spectrum is actually reminiscent to the spectrum of the non-infectious HET-s ( 218–289 ) amyloid grown at pH 3 which was also found to be less dispersed and to have broad cross peaks [42] . Hence , it is concluded that the G278A variant does form a distinct amyloid structure , which may serve as an explanation of the lack of a prion phenotype ( Figure 2; and see below ) . The nature of the misfolding of the G278A variant may be that the Gly residue is key for the arc between the two β-strands β4a and β4b . In addition , the F286A/W287A double variant shows a 2D DARR NMR spectrum quite different from the wild-type DARR spectrum ( Figure 4 ) . There are major chemical shift perturbations spread over the β-solenoid structure ( i . e . I231 , S273 , E235 , K229 , T233 , N262 , R238 , A247 , V264 , V244 , I277 , and Q240 ) , making it difficult to assess whether the double variant forms the β-solenoid fold ( Figure 4 ) . In conclusion , the comparative analysis of the solid state NMR spectra suggests that all the single Ala variants with the exception of G278A form the HET-s prion β-solenoid fold . With the exception of G278A , all the single Ala variants can therefore be used to explore the detailed structure-infectivity relationship of the HET-s prion . One of the consequences of a side chain replacement with Ala may be a change in the stability of the protein fold , which may influence function [57] , [58] , [59] , [60] . Applied to HET-s this would mean that a change of the stability of the β-solenoid may alter heterokaryon incompatibility , spontaneous prion formation , prion propagation and spore killing ( Figure 2 , Figure 3 , Tables 1 and 2 ) . In order to get insights into the individual contribution of the amino acid side chains to the stability of the HET-s prion , Ala variants K229A , I231A , V239A , Q240A , L241A , N262A , V264A , V267A , E272A , S273A , G278A , F286A , D288A and F286A/W287A were measured by a fibril denaturation assay using GuHCl ( Figure 5 and Table 3 ) following the concept by Santoro and Bolen to study protein folding and unfolding [61] . However , here we studied only the fibril denaturation process . This was monitored by measuring the light scattering at 500 nm ( OD500 ) [62] versus GuHCl concentration ( Figure 5 ) detecting fibril disassembly . Since the β-solenoid amyloid structure is composed of many inter-molecular interactions it is assumed that disassembly and unfolding occur together and that this detection method measures thus both fibril disassembly and β-solenoid unfolding . Such a fibril denaturation reaction mechanism can be of complex nature but here a simplified two-state process was assumed for the quantitative analysis . HET-s fibril denaturation studies have been performed previously using Trp fluorescence as probe for fibril denaturation [41] , because the unique Trp residue ( W286 ) is located in the C-terminal loop rather than in the actual core , we favored the use of light scattering because it might constitute a better probe for the overall unfolding of the fibril since Trp fluorescence might be affected by local unfolding of the loop . The individual denaturation curves in triplicates show a sigmoidal behavior ( Figure 5 ) . [61] . For most of the variants ( i . e . wild-type , K229A , I231A , V239A , Q240A , L241A , N262A , V264A , V267A , E272A , S273A , F286A , D288A ) the measurements appear to be reproducible . Only for G278A and possibly the double variant F286A/W287A a lack of reproducibility is observed ( Figure 5 ) . The lack of reproducibility is attributed to the altered , possibly polymorphic structure as evidenced by the solid state NMR spectra ( Figure 4 ) . A further qualitative inspection of Figure 5 suggests that the variants K229A , V239A , L241A , V264A , V267A , E272A , S273A , and D288A ( labeled group 1 in the following ) show sigmoidal denaturation curves similar to wild-type , while the denaturation curves of the variants I231A , Q240A , N262A , G278A , F286A ( group 2 ) , and the double mutant F286A/W287A have a different form and properties and are sometimes less reproducible ( as just stated ) ( see also Table 3 , fifth column ) . The two-state analysis of the denaturation curves yields two parameters for the unfolding transition: the m-value and the Gibbs free energy ΔG . The m-value is the steepness of the transition and is generally thought of as a measure of the change in solvent accessible surface area upon unfolding and reflects how cooperative the unfolding transition is [63] , [64] , [65] . The change in Gibbs free energy ( ΔΔG ) is a measure of the contribution of the amino acid side chain to the protein unfolding energy ( given that the structures of the folded and unfolded states as well as the unfolding pathway are otherwise unperturbed ) . The variants of group 1 can all be fitted with a two-state model , most of them with an unchanged m-value ( i . e . the m-value of the wildtype HET-s ( 218–289 ) prion ) . All the group 1 variants had an unaltered ΔΔG value or were up to 2 kcal/mol more stable ( Table 3 ) . The positive ΔΔG value for K229A of 1 . 5 kcal/mol indicates that the salt bridge between K229 and E265 does not play a favorable stability effect on the β-solenoid structure . Similarly , the replacement E272A ( removing the E272-R236 salt bridge ) results in a positive stability effect of approximately 2 kcal/mol . The amino acid replacements in the hydrophobic core at position V239 , L241 , V264 also had a 1 kcal/mol positive effect on stability V267 had no effect . This is in line with the in vivo data showing that none of these variants were affected for the prion phenotype ( Figure 2 ) . In combination with the solid state NMR spectra ( Figure 4 and see above ) , these unfolding measurements indicate that the Ala variants of group 1 are composed of the same β-solenoid 3D structure and the amino acid side chain replacement had no negative effect on its unfolding transition . In contrast , the GuHCl denaturation curves of group 2 variants ( Figure 5 ) are distinct from wild-type showing a flattened slope ( lower m-value ) ( i . e . I231A , Q240A , and N262A ) indicative of a decrease in unfolding cooperativity or a change of the surface . The latter potential explanation finds support in the surface location of the Q240 . Some replacements comprise a complex unfolding transition exemplified by an immediate loss of light scattering at low GuHCl concentrations ( i . e . I231A , Q240A , N262A , and F286A ) or poor reproducibility ( i . e . G278A and F286A/W287A ) , thus for these variants a Gibbs free energy calculation has not been considered ( see Table 3 ) . It is worth mentioning , that the less reproducible nature of the denaturation curves of G278A and F286A/W87A is reflected in the less well defined solid state NMR spectra ( Figure 4 ) . Together the data strengthen the notion that these two variants are not able to fold properly into the β-solenoid structure . The effect on the denaturation curve of N262A is attributed to the loss of the conserved Asn ladder . The I231A in the hydrophobic core replacement appears to influence the stability and unfolding pathway ( Table 3 ) . The NMR analyses also indicated an important structural role of this residue as the I231A replacement leads to a substantial amount of chemical shift perturbations in the β-solenoid fold ( Figure 4 , Figure S3 and S4 ) . Most interestingly , with the exception of D288A there appears to be a perfect correlation between the decrease in prion phenotype formation in vivo determined by the heterokaryon incompatibility assay ( Figure 2 ) and the change in the denaturation curve profile ( Table 3 ) . The F286A and W287A mutants affecting the C-terminal semi-hydrophobic pocket stand out in virtue for their drastic effect on [Het-s] activity although the corresponding residues reside outside the rigid β-solenoid core and do not alter the β-solenoid structure significantly ( Figure 4 ) . Only the double variant F286A/W287A may be unable to form the β-solenoid structure as suggested by the solid state NMR studies ( Figure 4 ) and the GuHCl denaturation curves ( Table 3 , Figure 5 ) . Moreover , two further mutants in the same region K284A and D288A also modulate prion behavior in a subtle way as the first mutation decreases prion formation while the other in fact increases the prion formation rate ( Table 2 ) . However , the D288A variant shows a wild-type-like solid state NMR spectrum and is ca . 1 . 2 kcal/mol more stable than wild-type , having otherwise a wild-type-like GuHCl denaturation curve ( Figure 4 and 5 ) . We chose to further analyze the mutants in that region for their ability to form aggregates in vivo as GFP fusion proteins . We found that neither F286A nor W287A led to the formation of dot-like aggregates in vivo , however when the mutant allele as GFP fusion proteins were introduced in a strain expressing wild-type HET-s ( Figure 6 ) , the mutant alleles were found to form dot-like aggregates presumably by being integrated into wild-type aggregates , a situation already described for the K284P mutant residing in the same conserved C-terminal loop region [27] . We also tested infectivity of the variants F286A and W287A fibrils generated in vitro and found them to be infectious ( Table 4 ) . The level of infectivity of the recombinant fibrils was at least as elevated as for wild-type fibrils . Infectivity could be detected in the same level of dilutions as for wild-type fibrils . These results indicate that once the amyloid fold is acquired , these mutants display wild-type seeding activity in vivo .
The determination of the HET-s β-solenoid structure exposed the existence of a number of structural elements defining this fold , such as the hydrophobic core , the asparagines ladders , the sharp turns connecting the β-strands termed arches [66] , the salt bridges , the internal hydrogen bond between the Thr and Ser residing in different β-strand layers and the C-terminal loop folding back onto the core [28] , [29] . The functional importance of the individual β-strands was documented before in a mutational approach involving proline substitutions [27] . Here , it is shown that to various degrees , the other described structural determinants also participate in prion function . Ala replacement of the strictly conserved G278 , leads to a null prion phenotype and this variant is unable to form the β-solenoid structure as evidenced by the solid state NMR studies . This effect is very likely due to the importance of the glycine residue in the arc between β-strands β4a and β4b for the β-solenoid fold ( Figure 1 ) . Ala replacements in the asparagine ladder affected function in vivo and the N262A variant shows an altered denaturation curve . The hydrophobic core residues I231 and I277 appear to be important for prion infectivity ( Figure 2 ) and in the case of I231A also for β-solenoid unfolding and structure ( Table 3 , Figure 4 ) . Also , the substitution of the highly conserved Thr/Ser pair leads to a detectable alteration of prion function but only in the spore-killing assay , while the S273A variant is 1 . 4 kcal/mol more stable . For the prion function in vivo , the solvent exposed salt bridges are important determinants , but their contribution to the β-solenoid stability is negative ( i . e . E272A is 1 . 9 kcal/mol more stable than wild-type ) . Sequence comparisons shows a preference for a positively charged residue in position 13 of each repeat ( R238 and R274 in HET-s ) ( Figure 1 ) . The functional significance of these conserved Arg residues is not directly evident . While the single Ala replacement did not change the prion activity , the R238A/R274A double mutant leads to a null phenotype thus providing a functional justification for the sequence conservation . A possible explanation of the role of these positively charged residues could be an interaction with the C-terminal loop and in particular the penultimate conserved Asp residue . In support of this hypothesis , is the fact that the two sequences that are exceptions to the conservation of positively charged residues at these positions also lack the C-terminal glycine-rich loop like Fo_CL52-2 and Nh_mpVI-1 , ( Figure 1B ) . In the presented Ala mutagenesis scan , two mutants were found for which spontaneous prion formation rate was increased rather than decreased , the D288A mutant and the T233A/S273A double mutant . This would indicate that the HET-s PFD does not have an optimal behavior in terms of prion formation rates . It might be that the increased prion formation rate of D288A is due to the higher stability of the variant ( i . e . ΔΔG = +1 . 2 kcal/mol , Table 3 ) or/and a modification of the properties of the C-terminal loop which is functionally important for a prion phenotype ( see below ) . The T233A/S273A double mutation increases the overall hydrophobicity of the core . Possibly , this increase in hydrophobicity favors a hydrophobic collapse and subsequent prion nucleation and/or stabilizes the β-solenoid fold since already the amyloid of S273A alone is 1 . 4 kcal/mol more stable ( Table 3 ) . Interestingly , the D288 and the S273 residues ( and to a lesser extend T233 ) are conserved positions ( Figure 1B ) . Thus , one may hypothesize that conservation at these positions reflects the need to reduce spontaneous β-solenoid folding because a too strong propensity for spontaneous folding might lead to uncontrolled activation of the HET-S pore-forming toxin . Similarly , the fact that several replacements increase rather than decrease stability also suggests that the HET-s PFD sequence is not optimized for highest possible stability . The theoretical model of prion propagation by the Weissman group [67] and others [68] , [69] highlights that for the prion propagation , amyloid fibril fragmentation is necessary . Thus , highest possible amyloid stability may not be compatible with the function of the HET-s fold . One finding of this study is the detection of the functional importance of a 7 amino acid residues C-terminal loop starting at the end of β-strand β4b and ending at the C-terminus of the protein ( GKGFWDN , residue 283 to 289 ) . The only previous indication of the functional importance of this region came for the observation that the K284P mutation affects HET-s aggregate formation in vivo [27] . Structurally , this region forms a semi-hydrophobic pocket that folds back onto the space delimited by the third and fourth strand of each rung [28] . This part of the structure is less well defined and appears to be more dynamic than the core region . The individual mutations of the two aromatic residues F286A and W287A have a very strong effect on activity . Actually , these two mutations are the most severe ones in our set of 32 mutants together with the mutations of the Gly in the arc positions ( i . e . G242/G278 ) . In addition , the mutations of the conserved charged residues in that region K284A , D288A ( and the structurally nearby Q240A ) also affect function . As noted above , the D288A mutation produced an unexpected effect on prion function as this mutation increased spontaneous [Het-s] prion formation . This is the first mutation found to increase [Het-s] prion formation . Of note however is that other criteria such as the spore-killing activity are in fact slightly reduced in that mutant . A recent molecular dynamics simulation study of the HET-s ( 218–289 ) β-solenoid suggested that the F286A and W287A mutations reduce flexibility at the fibril ends and that this flexibility is an important determinant for prion fibril infectivity [70] . However , F286A and W287A are able to form fibrils in vitro that display the same levels of infectivity as wild-type fibrils suggesting that mutation of the aromatic residues does not critically affect templating activity and the 3D structure of the β-solenoid fold . Alternatively , the hydrophobic nature of F286 and W287 may be important for an initial non specific hydrophobic interaction of the incoming molecule with the prion template . This hypothesis is supported by the solid state NMR spectra of both variants indicating no significant structural changes of the β-solenoid fold . Rather it appears that this region might be required for the prion formation process without actually being part of the cross-β part of the fold . These interpretation illustrates that amyloid folding can be dramatically modulated in a functionally relevant way by a sequence stretch that is not actually part of the cross-β structure per se . Strikingly , this part of the HET-s PFD sequence turns out to be the region of the protein with the highest level of sequence conservation . While , the repeat region can accommodate a significant level of divergence and still retain the ability to adopt the β-solenoid fold , the ability for this short C-terminal region to act possibly as a folding inducer of the repeat region appears highly constrained at the sequence level . The β-solenoid forming region of HET-s/HET-S is thus located between the HeLo domain which can have a prion inhibitory function and the C-terminal pocket region which acts as a prion promoting region [26] . These results revealing the existence of a short amyloid folding modulator stretch that is not directly involved in the cross-β core could be of general importance in the identification of sequence determinants for relevant amyloid formation and in efforts to control amyloid aggregation in disease related systems . Since many proteins are able to aggregate into amyloid-like entities and since the inter-molecular β-sheet formation with its many hydrogen bonds is an essential determinant of the cross-β-sheet motif , it has been suggested that the formation of amyloid fibrils is mainly a generic property of the polypeptide backbone and that the side chains play a minor role [71] . On the contrary , amyloid aggregation is highly amino-acid sequence specific as demonstrated by the intermolecular side chain interactions observed in the crystal structures [72] , [73] , [74] and the essential involvement of side chain interactions in the aggregation process as evidenced for example by the experimentally-derived scale of amino-acid aggregation-propensities ( ranging from the aggregation-prone hydrophobic residues to the aggregation-interfering charged side chain [75] , [76] , [77] ) . These observations accompanied with the predictive power of several algorithms for the cross-β aggregation propensity of polypeptide sequences [78] , [79] , [80] suggests that the cross-β state is less complex than most structures of soluble proteins , indicating that the complexity of the cross-β fold may fall somewhere between a secondary and a tertiary structure [2] . This notion of intermediate folding complexity of amyloids is now supported by the stability measurements of the HET-s Ala variants . Indeed , our study reveals both the absence of destabilizing effect for many replacements but also the critical importance of certain key residues . While the loss of side chain hydrogen bonds , salt bridges and hydrophobic interactions in soluble proteins yields in general a ΔΔG loss of ∼−2 kcal/mol [58] , [59] , [60] , in the case of the HET-s prion , the ΔΔG for the group 1 variants with K229A and E272A deleting a salt bridge , S273A getting rid of a side chain hydrogen bond , and V239A , L241A , V264A , and V267A reducing the hydrophobicity of the core , is either insignificant or with a positive value of up to +2 kcal/mol ( Table 3 ) . In particular , hydrophobic core residue replacements have little effect ( +0 . 6 kcal/mol in average; Table 3 ) compared to the average ΔΔG of ∼−2 kcal/mol predicted from data of soluble proteins . Only a few side chains appear to be crucial for the formation of the β-solenoid structure ( such as for instance I231 and N262 ) . Side chain replacements of such residues lead to in vivo effects but also to noticeable changes of the GuHCl denaturation curve . Thus , it appears that alteration of the β-solenoid structure can be achieved either by Pro insertions [27] having a drastic consequences on the backbone structure or by replacing side chains of a limited number of key residues that appear critical for directing the peptide backbone into the β-solenoid fold .
P . anserina strains used in this study were wild-type het-s , het-S , Δhet-s strains and the ΔPaHsp104 strain [55] . Growth medium for barrage assays and prion transmission assays was standard corn meal agar DO medium . The Δhet-s strain was constructed by inserting the nat1 cassette from the pAPI508 plasmid [81] in place of the het-s ORF . The nat1 cassette was amplified with oligonucleotides 5′ CTTCCCTTCCACTTCTTCACAC 3′ and 5′ ATCCTAGATGACTTAAGACGACAGG 3′ . The sequences upstram and downstream of the het-s ORF were amplified respectiviely with oligonucleotide pairs 5′ AAGCTTTTCGAATTGGTCTCTCAG 3′ and 5′GGGCAGTTTGAGGGGAAAGCGAAG 3′ and 5′ GGGACTAGTACCCTCCAGCAAGGATAGC 3′ and 5′ GCGGCCGCCATGGGCACTGCATCTGGG 3′ . The fragments were ligated to create the het-s::nat-1 cassette cloned as a HindIII-NcoI fragment in a pSK plasmid . This cassette was used to transform a het-s ΔPaKu70 strain [81] . Nourseothricine resistant transformants compatible with a het-S tester were selected . Inactivation of het-s was verified by PCR using oligonucleotides 5′ CGACGATCACAGCTATAGCGTGGTG 3′ and 5′ ATCCGGCTTCCCTGGACCTGCTTC 3′ . The strain was then backcrossed once to a het-s wild-type strains and Δhet-s ( NourR and het-S compatible ) Δhet-s ΔKu70 strains ( NourR , PhleoR and het-S compatible ) were selected in the progeny . Methods for determination of incompatibility phenotypes , prion formation and prion propagation were as previously described [54] . In brief , incompatibility phenotypes were determined by confronting strains of solid corn meal agar medium to [Het-s] and [Het-S] tester strains and visualizing the formation of barrages ( abnormal contact lines forming upon confrontation of incompatible strains ) . [Het-s] prion propagation was assayed as the ability to transmit the [Het-s] prion from a [Het-s]-donor strain to a [Het-s*] prion-free tester strain after confrontation on solid medium . Prion formation rates of [Het-s] were determined by measuring the fraction of [Het-s*] or subcultures that spontaneously acquired the prion phenotype after 5 days of growth at 26°C . For fluorescence microscopy , synthetic medium containing 2% ( wt/vol ) agarose was poured as two 10 ml layers of medium . P . anserina hyphae were inoculated on this medium and cultivated for 16 to 24 h at 26°C . The top layer of the medium was then cut out and the mycelium was examined with a Leica DMRXA microscope equipped with a Micromax CCD ( Princeton Instruments ) controlled by the Metamorph 5 . 06 software ( Roper Scientific ) . The microscope was fitted with a Leica PL APO 100X immersion lens . HET-s ( 218–289 ) , HET-s ( 218–289 ) Ala variants proteins were expressed in E . coli and purified as previously described [27] . Both proteins had a C-terminal 6 histidine tag and expressed as insoluble proteins and purified under denaturing conditions using Qiagen columns . Yields were in the range of 10 mg/L of culture . Proteins were eluted in 6 M GuHCl 50 mM Tris–HCl pH 8 , 150 mM NaCl , 200 mM imidazole . Elution buffer was replaced by 175 mM acetic acid by passage on a 5 ml Hitrap column ( Amersham ) . After lyophilisation the samples were stored at −20°C . The protein variants were dissolved in diluted HCl ( 45 mM ) and the concentration adjusted to 90 µM which was affirmed by UV absorption at 280 nm . The solution was filtered through a 0 . 2 µM filter and Buffer E ( Tris [3 M]; NaCl [1 M] HCl [45 mM] 1/19 V ) was added such that the end concentration of NaCl and Tris were 150 and 50 mM respectively . If necessary pH was adjusted to 7 . 4 using NaOH ( 4 M ) . Samples were rotated at 37°C on a rotator at constant speed for at least one week . Protein transfection experiments with amyloid fibrils of recombinant HET-s ( 218–289 ) variants were performed using a cell disruptor ( Fast-prep FP120 , Bio101 , Qbiogen Inc . ) . For each test , ∼0 . 5 cm3 of [Het-s*] mycelium grown on solid medium is sheared ( run time 30 s , speed 6 ) in 500 µl of STC50 buffer ( 0 . 8 M sorbitol , 50 mM CaCl2 , 100 mM Tris HCl pH 7 . 5 ) and the sonicated HET-s ( 218–289 ) amyloids assembled at pH 7 ( 20 µl at 1 mM ) in a 2 ml screw cap tube . The sheared mycelium is then diluted with 600 µl of STC50 buffer and then plated onto DO−0 . 8 M sorbitol medium and incubated at 26°C until being confluent ( 7–8 days ) . Several implants ( at least two per mycelium ) are checked for the [Het-s] phenotype in barrage tests . The fibril containing samples of the various HET-s ( 218–289 ) variants were concentrated after centrifugation ( 20000 g; 10 min ) to 500 µM . The fibrils were then diluted in the different GuHCl stock solutions ( between 0–8 M in 0 . 5 M steps ) to an end concentration of 20 µM . It is important to note that the fibril samples were added to the GuHCl solution by an Eppendorf pipette with a single extrusion . The samples were incubated at room temperature without shaking for one day or if stated for one week , respectively . The extent of amyloid disassembly was monitored by the measurement of the optical density at 500 nm ( OD500 ) on a JASCO V-650 spectrophotometer with quartz cuvettes and an adaptor from Eppendorf . The OD500 is believed to be an indirect measure of the amount of protein aggregation . Each experiment has been measured three times starting in part from different sample preparations . The analysis and extraction of the ΔG and m-value was done by the software package MATLAB following the mathematical formula and procedures of Santoro and Bolen [61] . As stated above the samples were incubated before the OD500 measurement at the various concentrations of GuHCl for one day in order to reach equilibrium conditions . Longer incubation periods such as one week did either not alter the measurements as demonstrated for the V267A variant in Figure S5 , or a flattening of the denaturation curve was observed as shown for the V264A variant ( Figure S5 ) . The flattening of the curve is interpreted as a restructuring of the amyloid aggregate during the long incubation time of one week , which let us concentrate on the use of the denaturation measurements with one day incubation for the quantitative analysis . Although in the case of V264A the extracted ΔG value did not alter between the two type of measurements ( Figure S5 ) , the change of the denaturation curve indicates that in GuHCl only a pseudo-equilibrium is obtained . Another point worth mentioning is that highly reproducible denaturation curves were obtained by incubating the protein in GuHCl solution without shaking , while in presence of shaking the reproducibility was only moderate ( data not shown ) . All the solid-state NMR experiments were conducted on a AVANCE II Bruker 600 MHz ( 14 . 1 T ) spectrometer using a Bruker 3 . 2 mm probe . DARR spectra with 20 ms mixing time were obtained from 15N , 13C-labeled HET-s ( 218–289 ) variants [82] . All the spectra were processed using Topspin 2 . 0 ( Bruker Biospin ) , using a shifted cosine squared window function as indicated in the Figure captions and so called zero-filling was done to the next power of two in both dimensions . Automated baseline correction was applied in the direct dimension . For spectral analysis the software package CcpNMR Analysis [83] was used and the chemical shift list was taken from [56] . HET-S homologs were recovered from available genome databases at ncbi ( http://blast . ncbi . nlm . nih . gov/Blast . cgi ) and jgi ( http://genome . jgi-psf . org/ ) using BLAST searches . Multiple alignments were performed with CLUSTALW2 at http://www . ebi . ac . uk/Tools/msa/clustalw2/ . The consensus sequence of the R1 and R2 repeats were generated using MEME at meme . nbcr . net/ , [53] . The MEME output gives a graphical representation of the consensus sequence as a weighted consensus in which the size of the letter designating a given amino acid is proportional to the conservation of the residue in the different sequences used to generate the motif . The size of the character reflects the information content measured in bits .
|
Prions are infectious protein particles causing fatal diseases in mammals . Prions correspond to self-perpetuating amyloid protein polymers . Prions also exist in fungi where they behave as cytoplasmic infectious elements . The [Het-s] prion of the fungus Podospora anserina constitutes a favorable model for the analysis of the structural basis of prion propagation because a high resolution structure of the prion form of [Het-s] is available , a situation so far unique to this prion model . We have analyzed the relation between [Het-s] structure and function using alanine scanning mutagenesis . We have generated 32 single amino acid variants of the prion forming domain and analyzed their prion function in vivo and structure by solid-state NMR . We find that the PFD structure is very robust and that only a few key mutations affect prion structure and function . In addition , we find that a C-terminal semi-flexible loop plays a critical role in prion propagation although it is not part of rigid amyloid core . This study offers insights on the structural basis of prion propagation and illustrates that accessory regions outside of the amyloid core can critically participate in prion function , an observation that could be relevant to other amyloid models .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biochemistry",
"mycology",
"microbial",
"physiology",
"proteins",
"prions",
"protein",
"structure",
"biology",
"and",
"life",
"sciences",
"microbiology",
"fungal",
"physiology",
"defense",
"proteins"
] |
2014
|
Contribution of Specific Residues of the β-Solenoid Fold to HET-s Prion Function, Amyloid Structure and Stability
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In most species , males do not abandon offspring or reduce paternal care when they are cuckolded by other males . This apparent lack of adjustment of paternal investment with the likelihood of paternity presents a potential challenge to our understanding of what drives selection for paternal care . In a comparative analysis across birds , fish , mammals , and insects we identify key factors that explain why cuckolded males in many species do not reduce paternal care . Specifically , we show that cuckolded males only reduce paternal investment if both the costs of caring are relatively high and there is a high risk of cuckoldry . Under these circumstances , selection is expected to favour males that reduce paternal effort in response to cuckoldry . In many species , however , these conditions are not satisfied and tolerant males have outcompeted males that abandon young .
Parental care is demanding: the effort it takes a typical garden bird to raise a clutch of chicks to adulthood is equivalent , in human terms , to cycling the Tour de France [1] . Intuition suggests that a male should only embark on this feat if he is the father of the chicks in his nest—if he has been cuckolded , he should avoid wasting resources on enhancing the reproductive success of his rivals and reduce paternal effort . Forty years of empirical research , however , have failed to provide consistent support for this prediction [2] . While there are exceptions , in the majority of species , it is reported that males do not significantly decrease care when cuckolded [2]–[5] , challenging our understanding of how natural selection favours males that provide parental care . If information about paternity is unavailable or unreliable , selection may favour males that continue to provide care to avoid potential costs of abandoning their own offspring [6] , [7] . Consequently , attempts to test explanations for the lack of paternal care adjustment have focussed on the ability of males to accurately assess paternity , but this has yielded abundant unexplained variation between species [2] . Another explanation is that variation in adjustment reflects differences in the strength of selection on males to reduce paternal care in response to loss of paternity . Firstly , theory predicts that the behaviour of males should optimise lifetime reproductive success rather than just paternity [8] , [9] . This is formalised in Hamilton's Rule , rb−c>0 , where r is the relatedness between the caring male and offspring in this context , b is the fitness benefit of care to offspring , and c is the cost of care to future reproductive success of the male [10] . Cuckolded males are , therefore , predicted to be relatively tolerant of cuckoldry if paternal care does not reduce future reproductive success ( low c ) [8] , [11] , [12] , and/or paternal care has little effect on offspring fitness ( low b ) [10] , [13] . When b is low , variation in r has relatively weak effect on variation in selection ( if we substitute b = 0 into rb−c>0 , rb is always 0; if we substitute b = 1 into rb−c>0 , rb varies from −1 to 1 depending on the value of r ) . Secondly , when there is little variation in the risk of cuckoldry between breeding attempts , or cuckoldry is rare , there will be weak selection for adjustment [11] . This is because rare or low variation in cuckoldry within males reduces the opportunity for selection to favour individuals that adjust paternal care [14] . Despite well-developed predictions about when cuckolded males should reduce care , our understanding of this problem remains limited because empirical studies have focused on reporting the presence or absence of adjustment without further formal analyses of causation .
We conducted a series of comparative meta-analyses to characterise the evolution of paternal care adjustment across species . We first quantified the strength of adjustment by calculating a standardised effect size ( Pearson's correlation coefficient: r ) from the statistics reported in 62 studies that measured the relationship between paternity and paternal care across 48 species of fish , insects , birds , and mammals . This effect size , rAdjust , is the correlation coefficient between paternal investment and paternity: positive values of rAdjust indicate that reductions in paternal care are associated with loss of paternity; a value of zero indicates that investment in paternal care is independent of paternity; and negative values indicate paternal effort decreases with higher paternity . In contrast to the prevailing consensus across empirical studies , we found that males show a significant reduction in paternal care in response to female promiscuity across species . We found that rAdjust was positive in 81% of species and was significantly greater than zero overall ( rAdjust mean effect = 0 . 35 , 95% credible interval ( CI ) = 0 . 10–0 . 68 , p = 0 . 02; Figure 1; Table 1 ) , despite the fact that only 44% of studies in our dataset report a significant effect of paternity on paternal care ( Table S1 ) . The difference between our meta-analysis of the strength of adjustment and “vote-counting” of significant results , indicates that failure to detect weak effects ( type II error ) and large variation between studies ( Table 1 ) may have contributed to an underestimate of the extent to which males respond to cuckoldry . We also found that experimental manipulation of a male's certainty of paternity did not cause greater adjustment in paternal care compared to observational studies ( Table 1 ) . Furthermore , rAdjust was significantly greater than 0 across experimental and observational studies and across studies that had measured different cues and used different methodology ( Table S6 ) . Together these results suggest that the ability of females to conceal promiscuous mating from males has been overestimated and the reported failure of studies to detect male adjustment cannot solely be explained by inaccuracy of cues . In addition to the significant overall effect of paternity on paternal care , our analysis revealed high variation in the strength of adjustment among species ( Figure 1; Table 1 ) , even after accounting for the possible confounding effects of phylogenetic history and differences in methodology ( Tables 1 and 6 ) . We tested predictions a priori that males of some species have stronger paternal care adjustment than others because of differences in the costs and benefits of paternal care and the risk of cuckoldry . We estimated the cost of care ( c ) by calculating the effect size ( Pearson's correlation coefficient: r ) of paternal investment in a current breeding attempt on the probability of success in future breeding attempts ( rCost ) from published test statistics ( Table S2 ) . A positive value of rCost indicates that the level of investment in care for offspring in a current breeding attempt results in a correlated reduction in future breeding success . We estimated the benefits of paternal care ( b ) by measuring both the effect size ( Pearson's correlation coefficient: r ) of male care on offspring fitness ( rBenefit ) from published test statistics and by obtaining data on the proportion of parental feeding visits performed by males from the literature ( Table S3 ) . A positive value for rBenefit indicates that more paternal care results in higher offspring survival . Finally , we estimated variation in female promiscuity ( r ) from the proportion of broods containing offspring fathered by more than one male ( Table S1 ) . We found that cuckolded males are significantly more likely to provide care when both the cost to future reproductive success is relatively low and cuckoldry is relatively rare ( interaction between multiple paternity and rCost explained 13% of variation in rAdjust across species ( reduction in residual variation when interaction was included in model ) : parameter estimate ( β ) = 0 . 11 , CI = 0 . 01–0 . 22 , p = 0 . 02; Figures 2 and 3 , Table 1 ) . The importance of this interaction is evident from the fact that a failure to reduce care in response to cuckoldry may be favoured even in species with a high risk of cuckoldry if there is relatively little cost to future reproductive success ( Figure 2 ) . In this case , the advantage of saving resources from withholding care are less likely to outweigh the costs of abandoning a brood where the male may have achieved some paternity success . Conversely , males also continue to care where the costs to future reproductive success are relatively high if female promiscuity is rarely or never encountered ( Figure 2 ) . This is because selection will not have had the opportunity to equip males with the ability to detect loss of paternity accurately and/or respond appropriately . For most species , however , some degree of adjustment has been favoured ( Figure 1 ) and our results suggest that this is because caring for current offspring is generally costly to future reproductive success ( rCost is significantly above zero across species – rCost mean effect size and CI = 0 . 26 [0 . 02–0 . 50] , p = 0 . 04; Table 1 ) and the chance of cuckoldry is sufficient to drive the evolution of a response . In contrast , we found no evidence that variation in the benefit of paternal care on offspring fitness explains differences between species in adjustment . Males were no more likely to adjust in species with high rBenefit relative to species with low rBenefit ( mean effect and CI = −0 . 03 [−0 . 12 to 0 . 07 , p = 0 . 57; Table 1 ) and males that provided a greater proportion of total parental care were not more sensitive to paternity ( mean effect and CI = −0 . 003 [−0 . 15 to 0 . 16] , p = 0 . 95; Table S7 ) . The effect of male care on offspring survival is relatively high across species ( mean rBenefit = 0 . 47 ( −0 . 27 to 0 . 87; Table 1 ) and we speculate that within biparental or male-only care systems the effect of male care may be difficult to detect and measure ( Table S4; Text S1 ) . For example , the effect of male desertion is often not documented if offspring always die without male care . The data we collected on rAdjust comes from studies that measured the relationship between paternal care and paternity certainty both within and across males . It is therefore possible that our results are not only due to facultative adjustment of care , but also intrinsic differences between males . For example , a positive relationship between paternity and paternal care ( positive rAdjust ) may arise because poor quality males care less and are more often cuckolded [15] , or because individual males adjust care in response to perceived paternity . To examine this possibility , we tested if our results differed according to whether rAdjust was measured across males or within males across breeding attempts . Values of rAdjust were significantly higher when changes within males were examined ( Table 1 ) , but the effect of rCost and rates of cuckoldry on rAdjust were consistent across studies using between and within subject designs ( Table S7 ( i ) ) . Furthermore , estimates of rAdjust were highly positively correlated across species where the relationship between paternity and paternal care had been measured both across and within males ( Spearman's Rank Correlation: Rs = 1 . 00 , n = 4 , p<0 . 001 , Pearson's correlation coefficient: R = 0 . 90 ) . Together these results suggest that examining changes within males may facilitate detection of paternal care adjustment and verifies that males facultatively reduce care in response to lowered paternity confidence , especially in species where high rates of cuckoldry are combined with high costs of caring to future reproductive success .
In this study , we address problems arising from the lack of empirical tests of theoretical predictions about the evolution of paternal care adjustment . In particular , measuring variation in paternity and the costs and benefits of paternal care within species is a major undertaking and it remains a challenge to think of ways how these factors can be experimentally manipulated to test existing theoretical predictions . By adopting a comparative approach , we have been able to exploit variation between species and our results suggest several important considerations for future studies of paternal care adjustment . Firstly , our study provides some guidance about the characteristics of species best suited to studies of paternal care adjustment . Specifically , the expectation of adjustment should be lowered in species with either low costs of care or low promiscuity . Secondly , we suggest that studies more closely address theoretical predictions by linking measures of adjustment with measures of costs of care ( none of the measures in our adjustment dataset were directly linked with the measures of costs [Tables S1 and S2] ) . Thirdly , the interaction between promiscuity and cost reported here could be tested empirically within a single species by characterising the relationship between the strength of adjustment and the residual reproductive value of males . This could be achieved by exploiting individual variation in factors such as male age , timing of breeding or quality , which are expected to correlate with residual reproductive value . When we see males caring for the offspring of another male , it is possible to assume that they are doing so simply because of a failure to accurately determine paternity success . Although our analysis shows that males are not as constrained by lack of reliable cues to paternity as often thought , it remains the case that females seem to get away with promiscuous mating in some species , with cuckolded males continuing to provide care . Are cuckolded males that maintain care blissfully ignorant or selfless dupes ? Our analyses suggest they are neither; instead , whether or not cuckolded males reduce paternal care is readily explained by the cost of paternal care and the risks of cuckoldry . More generally , cuckolded males provide a good example of how selection favouring tolerance can lead to the appearance of losing in an evolutionary arms race with cheats [16] . The finding that males show some degree of response to cuckoldry in the majority of species studied has implications for the evolution of paternal care more generally: we estimate that the extent to which females reduce paternal investment by engaging in promiscuous copulations is 12% on average within-species . By changing relatedness between nest mates , it has been shown that female promiscuity can drive transitions to and from cooperative breeding in birds [17] . By lowering relatedness between males and offspring , female promiscuity also has the potential to drive selection for reduced levels of paternal investment [18]–[20] and may ultimately cause the breakdown of biparental breeding systems [21] , [22] .
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In most species where it has been studied , males do not abandon or reduce paternal care when they are cuckolded by other males . These observations have presented a long-standing challenge to our understanding of what drives selection for paternal care . Our analysis of cuckolded fathers from 50 species of birds , fish , mammals , and insects , however , shows that sometimes it pays for males to stick around . In the case of humans and burying beetles , this is because females are relatively monogamous—by deserting , it is most likely the case that fathers will be deserting their own young . In species such as the chacma baboon , males face a significant risk of cuckoldry , and face potentially high penalties in terms of future breeding success by wasting precious resources on the young of other males . Unlike in humans , promiscuous females in these species will almost certainly lose the support of her mate in the effort to raise her young to adulthood .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion"
] |
[
"evolutionary",
"ecology",
"animal",
"behavior",
"biology",
"evolutionary",
"biology",
"behavioral",
"ecology"
] |
2013
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Why Do Cuckolded Males Provide Paternal Care?
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HIV-1 replicates via a low-fidelity polymerase with a high mutation rate; strong conservation of individual nucleotides is highly indicative of the presence of critical structural or functional properties . Identifying such conservation can reveal novel insights into viral behaviour . We analysed 3651 publicly available sequences for the presence of nucleic acid conservation beyond that required by amino acid constraints , using a novel scale-free method that identifies regions of outlying score together with a codon scoring algorithm . Sequences with outlying score were further analysed using an algorithm for producing local RNA folds whilst accounting for alignment properties . 11 different conserved regions were identified , some corresponding to well-known cis-acting functions of the HIV-1 genome but also others whose conservation has not previously been noted . We identify rational causes for many of these , including cis functions , possible additional reading frame usage , a plausible mechanism by which the central polypurine tract primes second-strand DNA synthesis and a conformational stabilising function of a region at the 5′ end of env .
Human Immunodeficiency Virus ( HIV ) infection remains a significant global health burden , with an estimated 36 . 7 million people worldwide living with the virus and 1 . 0 million AIDS-related deaths in 2016 [1] . Despite the success of existing interventions in reducing the annual incidence of HIV , there is still substantial scope for further progress in limiting new infections and optimising diagnosis and treatment of those with HIV . Improving our knowledge of the viral genomic structure and function permits better understanding of the viral lifecycle and host/virus interactions and may suggest interventions to target viral replication and survival . The HIV-1 genome is one of the most intensively studied genetic sequences . It contains a large number of cis-acting regions whose function depends on the structures into which the RNA folds . Many of these have been studied and solved at a secondary structure level and for some there are three-dimensional data . The whole genome has been analysed biochemically at a secondary structure level and many of the known functional regions have been mapped [2] . Much of the genome , however , has been determined to possess conserved structure by such techniques , but with no functional data . Similarly , there are regions where the function of the individual sequence nucleotides , either directly or through coding function , is the main constraint on the sequence and primary structure dominates over secondary structural requirements . HIV-1 has a high mutation rate , with genome-wide estimates varying from 5 . 3 × 10−5 to 5 . 8 × 10−3 per base per cycle , values of estimates depending upon a number of factors including how reverse transcriptase variability is measured , estimates of recombination rates , and the contribution of cellular apolipoprotein B messenger RNA-editing enzyme-catalytic polypeptide-like 3 ( APOBEC3 ) proteins to variability [3–7] . Notwithstanding variations in the mutation rate from base to base , this number may be sufficiently high to expect every possible point mutation to occur every day in an individual infected with HIV-1 [8] . Conservation within observed sequences is therefore significant and strongly indicative of evolutionary selection pressure , and points towards the presence of structures or functional regions necessary for viral survival . Detection of such conservation can help direct experimental searches to reveal novel structural and/or functional aspects . Without a priori knowledge of the dimensions of individual structural or functional elements , analytical techniques need to be able to detect conserved elements regardless of scale . To investigate further the possible structural form of the viral RNA and to seek evidence of conservation within the genome that might reveal previously unidentified cis-acting regions and functions , we performed an intensive bioinformatic analysis of HIV-1 genetic data . We analysed sequence variation , controlling for codon usage , using a novel scale-free method we have developed for finding regions of conservation when the underlying driver of conservation and hence the order of magnitude of the size of conserved regions is not known . This method differs from previous analyses in that it makes no assumptions about the scale of features of interest . As such it will reveal regions that are missed by previous methodologies . Our analysis reveals eleven regions of sequence conservation . Some of these coincide with clusters of established important cis-acting functions such as splice sites and their regulatory regions but for others there is no existing explanation; however , some highly likely causes can be attributed to a number of these . Analysing the secondary structure of these sequences reveals some known , but also a number of novel highly conserved structures including a previously undescribed highly stable structure around the central polypurine tract , with clear parallels between this and the structure we identify for the 3′ polypurine tract . Sequence preserved regions such as these represent promising targets for design of HIV therapeutics .
For each gene in the HIV-1 genome , as well as for the entire genome , non-recombinant B-subtype sequence data collated between 2009 and 2015 were downloaded from the Los Alamos National Laboratory Filtered Web Alignments database [9] . This database ensures only one sequence is included per patient , that a single representative is included of very similar sequences , and that sequences unlikely to represent natural viable viruses are excluded . Sequence data were further filtered to exclude sequences with uncertain or missing nucleotides in the region being studied , and to exclude sequences whose length indicated that they were incomplete or contained large insertions . A full listing of the sequences used can be found in the supporting information ( S1–S10 Tables ) . Sequences for each gene were aligned at amino acid level using MUSCLE [10] . Each gene was analysed at codon level with Mathematica [11] using the normalised mean pairwise distance ( nMPD ) method , as previously described [12] , subject to the modification that positions with invariant tryptophan and methionine codons were recorded as uninformative rather than having nMPD set to 1 . The nMPD method scores each codon locus by summing the pairwise Hamming distances between all codons . It then normalises to take into account amino acid usage and codon bias , by dividing by the expected distance sum taking into account the amino acids used at that locus and the distribution of codons encoding those amino acids across the genome . At each codon locus a non-negative nMPD score is produced , with low scores representing unexpected conservation of nucleotides after amino acid conservation and codon bias have been taken into account . Such unexpected conservation may represent important structural elements or important functional elements ( or both ) . The analysis of HIV alignments using the nMPD method raised an additional issue that the gap percentage is higher than that found in alignments of pathogens previously studied using this method , meaning that the way in which gaps are handled can have a substantial effect upon nMPD scores and consequently on the presence or absence of runs of low scores . Gaps were handled by defining the Hamming distance between a gap and any other nucleotide ( including a gap ) to be zero , but by labelling as uninformative codons where the gap percentage level exceeded 10% . This choice ensures that a minority variant in which a small motif is absent from or present in an otherwise highly conserved region does not result in the entire region being excluded from recognition . The original nMPD analysis [12] used sliding windows to identify runs of unexpectedly low scores , corresponding to regions of amino acid conservation . Here we instead used the novel scale-free analysis method we recently described [13] . This method is more versatile in that it more easily allows agnostic treatment of uninformative regions and it makes no a priori assumptions about the length of conserved motifs , although it still requires conserved regions to be relatively contiguous . The method converts nMPD data to have the appearance of a series of random walks , where at each stage of analysis the presence of an unusually steep descent corresponds to a region of unexpected nucleotide conservation . Regions determined by the above algorithm to be significantly conserved were aligned to the HXB2 ( [14] , GenBank accession K03455 ) and NL4-3 ( [15] , GenBank accession AF324493 ) sequences for reporting and analysis . Regions determined by the algorithm to be significantly conserved were further analysed by taking the relevant section of the whole-genome alignment and folding the region using RNAalifold [16 , 17] , with default parameters except for using ribosum scoring and disallowing helices of length 1 ( lonely pairs ) . RNAalifold combines phylogenetic information from an input sequence alignment with thermodynamic parameter calculation to determine a consensus secondary structure . The output allows local structures to be analysed for conserved features of interest and conserved loci and reciprocal mutations . Structures are presented in their original RNAalifold format and not modified on the basis of other published structural data as both our novel nMPD analysis and RNAalifold are designed to generate data from multiple sequences , whereas structural prediction from biochemical analyses such as selective 2′-hydroxyl acylation analyzed by primer extension ( SHAPE ) use only a single sequence as input . Generating a hybrid approach to constrain secondary structure using primary structure data from multiple sequences and e . g . SHAPE data from a single separate sequence would be of uncertain validity . As an additional validation step , we acquired sequences and undertook the nMPD analysis step for A-subtype sequence data ( including sequences labelled with sub-subtypes within the A-subtype ) , in the same way as described for the B-subtype sequence data , mutatis mutandis . A full listing of the sequences used can be found in the supporting information ( S11–S20 Tables ) . The purpose of this step was to provide additional validation of the main B-subtype nMPD analysis results and we did not perform further structural analysis of the A-subtype data .
nMPD analysis was performed for each gene , using between 309 and 1236 sequences ( in total 3651 distinct sequences were used ) . The output of nMPD analysis is shown in S1–S9 Figs . Five genes ( pol , tat , vpu , env , nef ) contained regions that were deemed significantly conserved ( Fig 1 and Table 1 for summaries of regions and features of interest found; Figs 2–4 for plots of cumulative analysis for genes in which significantly conserved regions were found; plots of cumulative analysis for remaining genes are in S10–S13 Figs ) . The conserved region found first in env , and the fourth region identified reading from its 5′ end—HXB2 nucleotide reference 7662–8051 ( NL4-3 7652–8041 ) —corresponds well with the well-known highly conserved Rev-response element ( RRE ) [21–23] . Our method clearly picks up how strongly conserved this region is and delineates its location ( see Table 1 for a summary of the statistics , and Fig 2 for a visual representation ) . Successful detection of this region serves as a positive control for our method . A local fold of this region is shown in Fig 5 . The RNAalifold representation is very similar , albeit not identical , to other predictions for folding of the RRE generated by other methods ( e . g . reference [2] ) . The conserved region found by the first iteration of our algorithm in pol , and the second region identified reading from its 5′ end ( Fig 2 ) —HXB2 nucleotide reference 4749–5093 ( NL4-3 4749–5093 ) —contains the central polypurine tract and associated G-rich sequences suggested to be important for RNA dimer stabilisation via G-quartet formation [24] . The region also contains a set of structured stem-loops that contain the A1 splice acceptor site and D2 splice donor site , plus associated exon splicing enhancer elements: ESEVif [25] , ESEM [26] , and a novel exon splicing enhancer surrounded by the ESEM1 and ESEM2 domains , which was initially identified with the HEXplorer algorithm [27] . The region also contains a splice silencing element , G4 [25] , which in our fold of the region is located in the stem of the structure containing the D2 splice site ( Fig 6 ) , and a postulated regulatory G run , GI2-1 [28] , located before the vif start site . There is an enhancer motif present between the G4 element and the silencing regulatory G run , termed ESE2b , and directly upstream of this the recently described ESS2b domain; mutations in or changes in accessibility of these domains have been shown to change the proportions of spliced products and affect viral infectivity [29–32] . The region finishes just 3′ of the rarely used D2b splice donor site . In the region is a motif previously identified as evolutionarily conserved , termed stem-loop containing splice acceptor 1 ( SLSA1 ) [33 , 34] , and although we note that the computational structural prediction by the method we have used does not reproduce the stem-loop of the original prediction , this does not detract from the presence of the conserved nucleic acid motif . The region also contains the start of vif ( i . e . there are overlapping open reading frames , resulting in conservation of the wobble position nucleotides ) , and the region ends just 5′ of the pol stop codon . A local fold of the region is shown in Fig 6 . One region of interest is highlighted in vpu ( Fig 4 ) , HXB2 nucleotide reference 6206–6226 ( NL4-3 6202–6222 ) , just 5′ to the start of env . The first region of interest highlighted in env ( Fig 2 ) is nearby , at HXB2 nucleotide reference 6324–6611 ( NL4-3 6320–6607 ) . Not far 5′ of these regions is the region of interest highlighted in tat ( Fig 3 ) , HXB2 nucleotide reference 5951–5998 ( NL4-3 5950–5997 ) , containing the A4a , A4b , A5 , A5a and A5b splice sites , in which possible conserved stem-loop structures have previously been proposed [35 , 36] , as well as the start of rev ( and hence a section with overlapping open reading frames and resultant conserved wobble positions ) and a GAR exon splicing enhancer [37 , 38] . Given the proximity of the regions 6206–6226 and 6324–6611 , we folded the entire region 6206–6611 to look for correspondences between elements within the larger region ( S15 Fig ) . This fold gives a correspondence between the regions 6218–6229 and 6597–6608 , with a helical structure in a long-range interaction . Stem-loop structures have been postulated 3′ to the env signal peptide [2 , 39] , and we considered whether there may be splicing-dependent folding with the potential to affect translation and/or function . To this end we have produced local folds of alignments corresponding to the three regions of interest with a region directly 5′ to them ( S16 Fig ) , and of alignments corresponding to spliced RNA of the 5′ leader region with the donor site D1 spliced to each of the splice acceptor sites A4c , A4a , A4b , A5 , A5a and A5b , and the remainder of the region HXB2 5951–6611 lying 3′ to the respective splice acceptor ( S17–S22 Figs; see S22 Table for additional information on the sequences used to produce these figures ) . These folds indicate that the region HXB2 6206–6226 may have the ability to form stable plausibly functional alternative structures by base pairing intramolecularly in the unspliced RNA or in the chimeric individual spliced species that arise from use of the important A4/5 splice site cluster ( Fig 7 ) . Our folds also suggest that the region HXB2 6324–6611 contains a number of conserved structural motifs . Our method highlights a number of further regions of interest , some corresponding to known features of the HIV-1 genome but others whose conservation we cannot fully explain . We detail these below in the sequence in which they appear in the genome , for ease of reference . The output of nMPD analysis of A-subtype sequences is shown in S28 Fig . S21 Table summarizes regions of interest found and S29 Fig . The validation analysis reproduces all of the regions found in the main analysis with the exception of one region of pol , which can be seen in the plot of cumulative analysis in S29 Fig to be conserved , but is not deemed significant by the algorithm . Conversely , the validation analysis finds two regions in gag that it deems to be significantly conserved , which can be seen to be conserved in the main B-subtype analysis ( S10 Fig ) , but are not deemed significant in the main analysis . We also briefly analysed the coding potential of the possible alternative ORFs we have described in pol and env . For the possible alternative ORF in pol , 95 of the 107 sequences that we studied ( 89% ) contained appropriate initiation codons; for the possible ORF in env , 106 of the 107 sequences that we studied ( 99% ) contained appropriate initiation codons; the spread of stop codons was similar to that in the B-subtype sequences . Again , there is consistent conservation of the appropriate Kozak consensus nucleotides for the possible open reading frame , with high purine conservation at the −3 positions and guanine conservation at the + 4 positions relative to position 1 of the AUG codons ( S30 Fig ) .
We have applied to a large collection of collated , previously experimentally acquired HIV-1 genomic data a novel version of an informatic pipeline for detecting conserved genetic regions . This combines a method for detecting nucleotide conservation independently of amino acid constraints with a method for detecting runs of conservation without requiring prior knowledge of the scale of any feature of interest . Our pipeline reassuringly detects features where it is already known that nucleotide conservation is required—for example , the Rev-response element is readily identified , and there is a striking correlation between regions our pipeline identifies and known clusters of splice sites—but it goes on to detect novel features whose role has not been described previously . By combining this pipeline with structural prediction tools capable of accounting for sequence variation in the prediction made , we propose a series of conserved structures , some with similarity to structures already published , but some that are novel and plausible , including a highly stable structure surrounding the central polypurine tract , which parallels a structure surrounding the 3′ polypurine tract . The structure we show ( Fig 6 ) would permit exposure of the central polypurine tract for priming of second strand synthesis [62 , 63] when it forms a DNA/RNA hybrid: previous work proposes that an oligo-A structure such as this in the context of an inverted repeat would promote reverse transcriptase pausing or even dissociation from the template [64 , 65] . Pausing would allow time for second strand synthesis to start using the central polypurine tract RNA as a template before it is hydrolysed; dissociation may leave the central polypurine tract RNA in situ for use as a template . Formation of G tetrads could provide a switch mechanism to an alternative conformation that would disrupt the structure we propose when the RNA is in the monomeric or dimeric form , and might have roles in obscuring double-stranded RNA runs to evade innate immunity or to foster intergenome dimer formation for encapsidation . In one case ( the tat/vpu/env region ) , the structures we propose , taken in conjunction with the identified regions of nucleotide conservation , immediately lend themselves to plausible functions in alternative splicing . Variable bridging of the HXB2 6206–6226 sequence between the 5′ leader region or the unspliced region upstream of HXB2 6206 might either promote translation of the partially spliced env transcripts from the immediately downstream env translational initiation codon , or stabilise part of the structure located 5′ of this region that contains the splice donor site D4 , which is used in conjunction with downstream splice acceptor sites to generate completely spliced mRNA transcripts . Additionally , the positioning of the conserved regions of interest we have identified within the spliced folds , together with the folds themselves , raises the possibility that splicing from the D1 donor site to the acceptor sites in the A4c to A5b region may result in a conformational stabilisation of the 5′ cap , possibly contributing to the extensive utilisation of the A4/5 splice acceptor cluster for over 90% of env , vpu , rev and nef transcripts [66] . The leader region can adopt two alternating structures [67] , and stabilisation of the leader region in the long distance interaction ( LDI ) conformation , rather than the branched multiple hairpins ( BMH ) conformation [68] , may imply a function for this elongated helix loop in the spliced RNA . The LDI conformation is proposed to reduce inter-molecular genome dimerisation and this would be a valuable property to preserve in spliced mRNA to prevent dimerisation using the high-affinity SL1 dimer linkage site , restricting this property to unspliced viral RNA . Although a previous in vivo analysis of RNA structure in this region did not identify the LDI conformation [69] , detection of this in spliced RNA may have been compromised by use of a primer downstream of the gag start site . The key to generating these structures—and the novel step we have undertaken—is priming the folding algorithm with just the conserved region , identified by our scale-free analysis of nMPDs , that may form a conserved structure , to increase the algorithm’s ability to select and predict the locally conserved secondary structure . To prime in this way cannot be achieved without knowledge of the extent of the conserved region , which is a novel feature of the detection pipeline we have used . Whilst we have strong mathematical evidence for the existence of conserved regions we rely on prior publications to support the structural predictions we have made , where such evidence exists . The largest and most striking region of conservation we identify is the Rev-response element and this is powerful validation of the predictive accuracy of our modelling . Other regions we show are based on what are highly accurate structural prediction programs but some need functional and structural validation and must be accepted as putative at present . We have mentioned two regions where one explanation of genetic conservation would be hithertofore undescribed open reading frames . We caution that we are not aware of any empirical evidence of translation of these putative frames . The lack of strong database search hits against peptides of known function indicates that putative products of these frames are not directly analagous to already-characterised peptides . Some regions of the genome where ovelapping reading frames are known to exist are not identified as having a high degree of conservation by our method , indicating that the degree of variability of some regions of HIV-1 without wobble positions is comparable with the background degree of variability . Our method relies upon what is in effect a signal processing algorithm that most easily picks up relatively long runs of conserved genetic elements in a background where there is less conservation . The algorithm is applied gene-by-gene . This explains why , for example , the algorithm does well at detecting conserved elements within the env gene , where the overall high degree of variability makes it easier to identify conserved elements ( in signal processing terms , the signal-to-noise ratio is high ) . It also explains why , for example , the algorithm fails to detect the well-characterised frameshift structure towards the end of gag , which has only a relatively short run of highly conserved nucleotides . It is important to understand that this algorithm is optimised for identifying longer contiguous conserved genetic elements such as those where RNA structure needs to be conserved and it may miss short or non-contiguous motifs , especially in genes with relatively lower nucleic acid variability overall . Whilst there is no minimum run length the algorithm can detect ( it can pick up any sufficiently strong signal of length at least 2 ) , the degree of conservation of a region versus background variability needs to be much higher for detection of shorter conserved regions . This is illustrated in Fig 5 , left panel of our paper describing the algorithm [13] , and to aid understanding of the degree of conservation required to detect much shorter regions using this algorithm , we have extended our benchmarking simulation to illustrate results for signal lengths down to 2 ( S31 Fig ) . Our validation step using A-subtype sequences shows consistency of the method’s performance between related datasets , whilst illustrating a feature of our algorithm that minor differences between datasets may lead to minor differences between the start and end positions of regions deemed significantly conserved . The nMPD algorithm is not applied to non-coding regions at all and will not pick up conserved elements in non-coding regions . Thus overall our method cannot detect known microRNAs or long non-coding RNAs [70] . This novel approach to detection of nucleotide conservation in HIV-1 has revealed unexpected insights into the viral genome and can be applied similarly to other RNA viral sequences . The identification of unsuspected regions of conservation is of importance as it directs experimental approaches for investigation into regions of conserved structure or function . In conjunction with further experimental validation , it can suggest loci that are likely to represent stable drug targets .
|
HIV-1 is a very rapidly mutating organism , however some parts of its genetic material change more than others . We looked for coding regions of HIV-1 that change relatively little , by turning the problem of finding such regions into a problem in signal processing , and solving this using a novel analytical approach that we recently described . We investigated why the regions we identified change less , including using the genetic code in the regions we found to prime an algorithm to predict their structures . In some cases there are already known functions for the features we found , in others they provide new insights into the properties of known regions , and in some cases we identify new regions that vary less for as yet unknown functional reasons .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] |
[
"sequencing",
"techniques",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"microbiology",
"retroviruses",
"viruses",
"immunodeficiency",
"viruses",
"rna",
"viruses",
"genome",
"analysis",
"sequence",
"motif",
"analysis",
"molecular",
"biology",
"techniques",
"research",
"and",
"analysis",
"methods",
"sequence",
"analysis",
"rna",
"structure",
"genomics",
"lentivirus",
"sequence",
"alignment",
"bioinformatics",
"medical",
"microbiology",
"hiv",
"microbial",
"pathogens",
"hiv-1",
"molecular",
"biology",
"nucleotide",
"sequencing",
"biochemistry",
"rna",
"rna",
"folding",
"dna",
"sequence",
"analysis",
"nucleic",
"acids",
"database",
"and",
"informatics",
"methods",
"viral",
"pathogens",
"genetics",
"biology",
"and",
"life",
"sciences",
"computational",
"biology",
"gene",
"prediction",
"organisms",
"macromolecular",
"structure",
"analysis"
] |
2019
|
A scale-free analysis of the HIV-1 genome demonstrates multiple conserved regions of structural and functional importance
|
The host response to mycobacterial infection depends on host and pathogen genetic factors . Recent studies in human populations suggest a strain specific genetic control of tuberculosis . To test for mycobacterial-strain specific genetic control of susceptibility to infection under highly controlled experimental conditions , we performed a comparative genetic analysis using the A/J- and C57BL/6J-derived recombinant congenic ( RC ) mouse panel infected with the Russia and Pasteur strains of Mycobacterium bovis Bacille Calmette Guérin ( BCG ) . Bacillary counts in the lung and spleen at weeks 1 and 6 post infection were used as a measure of susceptibility . By performing genome-wide linkage analyses of loci that impact on tissue-specific bacillary burden , we were able to show the importance of correcting for strain background effects in the RC panel . When linkage analysis was adjusted on strain background , we detected a single locus on chromosome 11 that impacted on pulmonary counts of BCG Russia but not Pasteur . The same locus also controlled the splenic counts of BCG Russia but not Pasteur . By contrast , a locus on chromosome 1 which was indistinguishable from Nramp1 impacted on splenic bacillary counts of both BCG Russia and Pasteur . Additionally , dependent upon BCG strain , tissue and time post infection , we detected 9 distinct loci associated with bacillary counts . Hence , the ensemble of genetic loci impacting on BCG infection revealed a highly dynamic picture of genetic control that reflected both the course of infection and the infecting strain . This high degree of adaptation of host genetics to strain-specific pathogenesis is expected to provide a suitable framework for the selection of specific host-mycobacteria combinations during co-evolution of mycobacteria with humans .
The primary cause of tuberculosis is the human pathogenic bacterium Mycobacterium tuberculosis . The host cells of M . tuberculosis are macrophages and the bacilli have developed numerous adaptations to survive within these powerful immune effector cells . For example , human pathogenic strains of M . tuberculosis inactivate microbicidal superoxide via katalase [1] , avoid the detrimental effects of iNOS products [2] , skew the anti-mycobacterial response in macrophages towards production of anti-inflammatory molecules [3] , [4] , and favour necrosis over apoptosis [5] , [6] , [7] . Interestingly , circulating strains of M . tuberculosis may differ in their pathogenic potential [8] , [9] . Since humans and M . tuberculosis have co-evolved over millennia , a question remains if and to what extent M . tuberculosis has adapted to genetically distinct hosts . Indeed , two studies conducted in ethnically mixed samples detected a non-random association of M . tuberculosis strains with distinct ethnic populations [10] , [11] . These observations are supported by the results of several genetic association studies that detected preferential associations between a Toll-like receptor 2 ( TLR2 ) polymorphism and tuberculosis meningitis caused by Beijing strains [12] , as well as between variants of 5′-lipoxygenase ( ALOX5 ) and pulmonary tuberculosis caused by M . africanum , but not M . tuberculosis [13] . In addition , variants of the immunity-related GTPase M ( IRGM ) were associated with protection from pulmonary tuberculosis due to Euro-American strains of M . tuberculosis [14] . Due to the complex interactions of M . tuberculosis and humans in exposed populations , it is possible that those results may have been confounded by unrecognized factors . In the absence of independent replication studies , the question of strain specific genetic effects as a consequence of M . tuberculosis human co-evolution still awaits testing under carefully controlled conditions . M . bovis Bacille Calmette-Guerin ( BCG ) strains are phylogenetic descendants of an ancestral BCG stock originally derived from virulent M . bovis through in vitro propagation [15] , [16] , [17] . Attenuation of the original BCG stock occurred as a result of deletions in the M . bovis genome , specifically the region of difference 1 ( RD1 ) [18] , [19] . Loss of RD1 is common across all BCG strains , although additional genetic alterations have been identified for each strain . BCG Russia and BCG Pasteur are among the most phylogenetically distant BCG strains [15] . Genetic events identified in BCG Russia include the deletion of RD Russia ( Rv3698 ) [20] , an insertion mutation in the recA gene ( recA_D140* ) [21] , and the presence of an IS6110 element in the promoter region of the phoP gene [15] , [22] . BCG Pasteur is characterized by the loss of RD2 , nRD18 , and RD14 [23] , [24] , [25] as well as a number of single point mutations and duplication events [22] , [23] , [26] , [27] . Phenotypic differences between BCG Pasteur and BCG Russia can therefore be tentatively linked to these known changes in gene content and an unknown number of point mutations . A number of unresolved questions surround the BCG host interplay which is characterized by highly variable host responsiveness . For example , the immunogenicity of the same strain of BCG given to vaccinees of different genetic background can vary tremendously [28] , [29] while host responses triggered by different strains of BCG are equally divergent [30] . On a population scale , BCG strains differ in the adverse reactions they trigger [31] and there is evidence that the protective effect of BCG vaccination against tuberculosis meningitis varies among ethnically divergent population groups [32] . Taken together , these data suggest that , similar to tuberculosis susceptibility , host responsiveness may reflect specific host-BCG strain interactions . To test this possibility , we compared the genetic control of closely related strains of BCG in a mouse model of infection . Recombinant congenic ( RC ) strains are a set of genetically related inbred strains . In RC strains , discrete chromosomal segments of donor genome ( 12 . 5% ) are transferred onto a recipient genetic background ( 87 . 5% ) through a double backcross and corresponding strains are derived by subsequent inbreeding [33] . The AcB/BcA panel used in the present study was derived from a reciprocal double backcross between C57BL/6J and A/J [34] , two mouse strains known to differ in their susceptibility to M . bovis BCG strain Montreal [35] . Each RC strain is genetically distinct with its own unique genome . The genomes of all RC strains have been mapped extensively and represent frozen replicas of recombinant progenitor genomes with known genomic boundaries of chromosomal segments derived from the two progenitor strains . A major advantage of RC strains over conventional crosses is that any phenotype can be measured repeatedly in genetically identical mice of a RC strain , greatly improving the accuracy of the phenotypic estimates . In the present study , 35 distinct AcB/BcA strains were infected with a low dose of either BCG Pasteur or BCG Russia . A genetic analysis of the bacillary counts in the spleen and lungs of these strains identified general , as well as tissue- and BCG strain-specific susceptibility loci for BCG infection . These results demonstrated that the host response to mycobacteria reflects a genetically controlled , joint effect of both host and pathogen . Our findings established strain specific effects of the host-mycobacteria interplay in the absence of selective pressure and , therefore , argue in favour of additional host-mycobacterial adaptation during the co-evolution of humans and mycobacteria .
A/J and C57BL/6J mice were purchased from the Jackson Laboratory ( Bar Harbor , Maine ) . Thirty-five independent RC strains originally derived from a reciprocal double backcross between the A/J and C57BL/6J progenitors [34] were purchased from Emerillon Therapeutics Inc . ( Montreal , Qc . ) . All mice were housed in the rodent facility of the Montreal General Hospital . Animal use protocols were approved by the Animal Care Committee of McGill University and are in direct accordance with the guidelines outlined by the Canadian Council on Animal Care . Recombinant BCG Russia ( ATCC 35740 ) and Pasteur ( ATCC 35734 ) , were transformed with pGH1 , an integrating vector that inserts into the attB site of the mycobacterial genome and that combines a firefly luciferase lux gene cassette , an integrase [int] gene , a MOP promoter , and a hygromycin resistance [Hyg] gene [31] . The pGH1 vector allows for growth on antibiotic-containing media to reduce risk of contamination [36] . BCG strains were grown on a rotating platform at 37°C in Middlebrook 7H9 medium ( Difco Laboratories , Detroit , Mich . ) containing 0 . 05% Tween 80 ( Sigma-Aldrich , St . Louis , Mo . ) and 10% albumin-dextrose-catalase ( ADC ) supplement ( Becton Dickinson and Co . , Sparks , Md . ) . At an optical density ( OD600 ) of 0 . 4 to 0 . 5 , bacteria were diluted in phosphate buffered saline ( PBS ) to 105 colony forming units ( CFU ) /ml . Mice were injected intravenously with 103 to 104 CFU of BCG in 100 µL of PBS . Inoculum doses were confirmed by plating on Middlebrook 7H10 agar ( Difco Laboratories , Detroit , Mich . ) supplemented with oleic acid-albumin-dextrose-catalase ( OADC ) enrichment ( Becton Dickinson and Co . , Sparks , Md . ) . Infected mice were sacrificed by CO2 inhalation after 1 and 6 weeks post-infection . Lungs and spleens were aseptically removed , placed in 0 . 025% Saponin-PBS , and homogenized mechanically using a Polytron PT 2100 homogenizer ( Brinkman Instruments , Westbury , NY ) . Homogenates were serially diluted tenfold and plated on Middlebrook 7H10 agar supplemented with OADC enrichment and containing hygromycin B ( Wisent Inc . , St . -Bruno , Qc . ) . Bacterial enumeration was performed following a six-week incubation at 37°C . For BCG Pasteur infection , a total of 221 and 175 mice were used at the week 1 and 6 time points , respectively . A total of 145 and 189 mice , respectively , were used at 1 and 6 weeks for BCG Russia infection . Strains of the AcB/BcA panel were genotyped for 625 microsatellite markers spanning the entire genome with an average distance of 2 . 6 cM [34] . Based on Build 36 . 1 of Mouse Genome Informatics ( MGI ) Mouse Genome Database , six markers with reassigned positions were removed from the current analysis [37] . The first QTL model was the linear model where y represents a vector with the individual total count of bacteria ( log10CFU ) ; is a vector with each entry being an indicator variable of the genotype BB at the marker position with being its associated effect ( major gene effect ) ; is a matrix of fixed covariates ( a constant and gender in our main model ) and its corresponding parameter vector ; is a vector of independent and identically distributed random variables representing the error term with and . At each marker position , M-estimates of the parameters and a t-statistic were computed . The genome-wide corrected p-values were obtained by bootstrap under the hypothesis that there is no major gene , i . e . , re-sampling under the reduced model Mean confidence bounds at each marker were defined as twice the standard error around the marker's group mean without considering gender effect in the model . In order to account for the genetic background , a second linear model of the form was employed , i . e . , our second model was the mixed model resulting from adding a random component , to our original model , where is a random vector associated to the genetic background of each RCS and is the design matrix associating the RCS effect to the phenotype y . The assumptions for this model component were and with being an unknown constant and a positive definite-matrix ( in fact , a background correlation matrix which is a function of length of the segments identical by descent shared amongst strains ) assumed to be known , although a genomic estimate of it was previously obtained . At each marker position iteratively , estimates of fixed effect parameters and the variance components were obtained under this model and a t-statistic of the same form as before was computed . The genome-wide corrected p-values were obtained by bootstrap under the hypothesis that there is no major gene , i . e . , re-sampling under the reduced model More details of estimation and testing are given in Methods S1 . Evidence was considered significant for linkage when single-point regression analysis at the markers was P<0 . 01 .
We determined the bacillary load of BCG strains Pasteur and Russia in the lungs and spleens of C57BL/6J and A/J mice following a low dose ( ∼3×103 bacilli ) intravenous injection of bacilli . Pulmonary counts of BCG Pasteur were below the limit of detectability ( 80 bacilli/lung ) at weeks 1 and 6 post infection but showed a modest peak of approximately 100 bacilli/lung at week 3 ( Figure 1 ) . This suggested limited dispersion and growth of BCG Pasteur in the lungs . In addition , there was no detectable difference in the pulmonary load of BCG Pasteur between C57BL6/J and A/J mice . By contrast , we observed an increase of 1–1 . 5 log CFU in the spleens between weeks 1 and 3 post infection that was followed by a 1 log decrease at week 6 . The splenic bacillary burden of BCG Pasteur was substantially higher in C57BL/6J mice at weeks 1 and 3 . BCG Russia showed a constant increase of pulmonary CFU from week 1 to week 6 . In the spleen , growth of BCG Russia lagged growth of Pasteur and did not show evidence for a peak at 3 weeks post infection , as was observed for Pasteur ( Figure 1 ) . Overall , the pattern of tissue CFU for BCG Pasteur strongly resembled the one described for BCG Montreal which has previously been shown to be under Nramp1 control [35] , [38] . The kinetics of lung and spleen bacillary counts of BCG Russia were distinct from the previously described BCG growth patterns . To investigate the genetic control of in-vivo growth of BCG Russia and BCG Pasteur , mice from a panel of 35 AcB/BcA RC strains were intravenously challenged with a low dose ( 3–5×103 bacilli ) of BCG Russia or BCG Pasteur . The number of colony forming units ( CFU ) in the spleen and lung was used as the phenotype for the genetic analysis . CFU were determined at 1 week and 6 weeks post infection since it is well established that at 3 weeks , the Nramp1 gene dominates the host response to BCG Montreal [38] , making it potentially more difficult to discern additional genetic control elements . To best indicate the effect of genotype on CFU , all RCS were stratified according to genotype at each marker , i . e . AA for markers on chromosomal segments derived from A/J or BB for chromosomal segments derived from C57BL/6J . Mice of all RCS with a given genotype were then used to obtain the mean and 95% confidence interval of their pulmonary and splenic CFU . This presentation allowed to graphically depict the effect of both marker genotype and of the general strain background on CFU . Results for the spleen and lung for both BCG strains are presented in Figures 2 and 3 . A clear impact of strain background on susceptibility to BCG in the spleen at 1 week post infection was evidenced by the larger bacillary counts in mice of the BB genotype across most chromosomes ( Figure 2 ) . The strong strain background effect on splenic CFU was resolved by 6 weeks post infection , particularly for BCG Pasteur where differences in splenic bacillary burden appeared negligible across all markers ( Figure 2 ) . By contrast , CFU differences in BCG Russia were observed for several small chromosomal segments possibly suggesting the presence of specific genetic loci ( Figure 2 ) . As in the parental strains , pulmonary burdens were at the limit of detectability at week 1 for both Russia and Pasteur , and week 6 for Pasteur . However , at the 6-week endpoint , preferential replication of BCG Russia was observed in mice bearing specific A/J-derived chromosomal segments , particularly at the distal portion of chromosome 11 ( Figure 3 ) . Markers where the mean CFU of the AA and BB genotype groups diverged were indicative of chromosomal regions that potentially harboured a BCG susceptibility locus . To confirm the potential linkage of these chromosomal segments to bacterial burden , a genetic analysis comparing mice of the AA to BB genotype was performed . The initial analysis compared genotype groups without taking into account the genetic background of the strain or the gender of the mouse ( incomplete model ) . As expected , markers significantly linked to bacterial burden corresponded well with chromosomal regions where the two genotypes differed ( Figures 2 and 3; Figures S1 to S3 ) . From this analysis , the genetic control of BCG Pasteur and Russia splenic infection appeared to be highly multigenic at the early time point . Employing a very stringent level of significance ( P<0 . 0003 ) , quantitative trait loci ( QTL ) were identified across 8 and 15 different chromosomes for BCG Pasteur and Russia , respectively ( Figure S1 ) . At the 6 week endpoint , a locus was identified on chromosome 1 for splenic BCG Russia load whereas genetic effects were not detected for BCG Pasteur load ( Figure S2 ) . Pulmonary CFU of BCG Russia was controlled by a locus on chromosome 11 while for BCG Pasteur a locus was identified on chromosome 8 ( Figure S3 ) . Visual inspection of CFU across genotypes suggested a strong impact of strain background on bacillary loads . To account for the potential impact of background genes on linkage peaks , we developed a main model that accounted for the genetic background and gender of the mice . The number of loci identified by the main model was reduced relative to the incomplete model , particularly at the 1 week time point ( Figures S4 and S5 , and Table 1 ) . For lung CFU , the locus on chromosome 11 remained that impacted on bacillary load of BCG Russia at 6 weeks post infection ( Figure 4 ) . No genetic effect was detected for pulmonary load of BCG Pasteur which is consistent with the very limited growth of BCG Pasteur in the lungs of all mice ( data not shown ) . In contrast to the lung , the genetic control of splenic bacillary load remained largely multigenic even after correction for strain background effects . For BCG Russia at 1 week post infection , a single locus on chromosome 1 ( 36 . 9 cM–48 . 8 cM ) was found to control splenic load ( Figure S4 ) . At 6 weeks post infection , the genetic control of BCG Russia was multigenic ( Figure S5 ) . In addition to the chromosome 1 locus ( 32 . 8–55 . 1 cM ) , loci were detected on chromosome 6 ( 45 . 5–46 . 3 cM ) , chromosome 11 ( 47 . 67 cM ) and chromosome 19 ( 51 cM ) . Splenic load of BCG Pasteur at 1 week post infection was controlled by loci on chromosome 2 ( 10–15 and 22 . 5–26 . 2 cM ) , chromosome 7 ( 63 . 5–65 . 6 cM ) and the X chromosome ( 37–40 . 2 cM ) . Additional weaker effects were identified on chromosome 3 ( 33 . 7 and 58 . 8 cM ) , chromosome 6 ( 63 . 9 cM ) , chromosome 10 ( 3 cM ) , and chromosome 17 ( 23 . 2 cM ) . A major gene effect detected on chromosome 1 ( 17–58 . 5 cM ) overlapped the chromosome 1 locus controlling BCG Russia infection ( Figure S4 , Table 1 ) . Genetic control elements were not detected in response to BCG Pasteur infection at the 6 week time point ( data not shown ) . The inverse complexity of BCG Pasteur ( multigenic at 1 week; no genes at week 6 ) and BCG Russia ( a single gene at week 1 , multigenic at week 6 ) reflects differences in the replication pattern of the bacteria: BCG Russia showed a delayed onset of growth that continued at week 6 while BCG Pasteur showed rapid initial growth with a strong decline of CFU at week 6 as compared to week 3 . The chromosome 1 locus significant for linkage early during BCG Pasteur infection and at the early and late phase of BCG Russia infection was indistinguishable from Nramp1 . Employing what we termed the “conditional model , ” we determined whether the additional linkage peaks were conditional on the Nramp1 gene . For this the main model was modified to adjust for the effect of Nramp1 by adding a column with the BB genotype indicator at the Nramp1 position to the matrix X . Chromosomal regions identified at the week 1 time point of both BCG Pasteur and BCG Russia infection were no longer significant for linkage following correction for the chromosome 1 locus ( data not shown ) . Similarly , the genetic effects detected on chromosome 6 and 19 were no longer significant at the 6 week time point of BCG Russia infection . However , the linkage hit detected on chromosome 11 ( 47 . 67 cM ) retained its significance . By contrast , a secondary peak detected only for splenic CFU immediately proximal to this locus did not reach significance ( Figure 5 ) . Finally , an additional locus was localized to chromosome 13 ( 73–75 cM ) ( Figure 5 ) .
RC strains are particularly useful to establish pathways of causality in complex read-outs such as immune reactivity and are well suited to track gene-gene interactions [33] . However , RC strains have also proven useful for positional identification of disease susceptibility loci by employing RC strains with extreme phenotypes in subsequent genetic crosses [39] , [40] , [41] . A third application of RC strains is the genome-wide identification of quantitative trait loci ( QTL ) in complex diseases . This feature of RC stains is particularly attractive since it allows the measurement of quantitative traits in many genetically identical mice belonging to the same strain which greatly increases the accuracy of trait determination . A genome-wide scan for the presence of QTL can then be conducted among the relatively limited number of RC strains in each panel . This is highly efficient compared to the breeding and genotyping of hundreds of mice in traditional backcross or F2 based genome-wide mapping studies . For example , a recent study used the AcB/BcA RC strain panel to localize a large number of asthma susceptibility loci across the genome [42] . A potential problem that is faced in these speedy genome-wide scans in RC strains is the confounding impact of strain background and of strong susceptibility loci on the overall pattern of QTLs mapped . We have developed a new analytical methodology that overcomes both of these potentially confounding limitations while conducting genome-wide QTL mapping in RC strains . Our results demonstrate the ease of genome-wide scanning in RC strains and the importance of adjusting especially on strain background to achieve reliable QTL identification . Our ability to detect the Nramp1 genomic region also served as an internal validation of the analytical approach . Another interesting observation was the loci that could only be detected in connection with Nramp1 . Once the analysis was adjusted on the Nramp1 gene , these loci were no longer significant for linkage . The most parsimonious explanation for this effect is that these loci are interacting with Nramp1 . Why we would detect a large number of genes that interact with Nramp1 in the genetic control of BCG Pasteur as compared to BCG Russia is not known but may reflect the differences in pathogenesis between the two BCG strains . For BCG Pasteur , putatively interacting genes were detected at 3 weeks post infection while for BCG Russia such interacting loci were observed at the 6 week time point . At 3 weeks , BCG Pasteur shows a sharp peak of splenic bacillary burden while the growth of BCG Russia continues well past 6 weeks before a slow and gradual reduction of splenic burden becomes evident after 12 weeks of infection ( data not shown ) . While the interpretation of our results as Nramp1 interacting loci appears reasonable , it is important to realize that this conclusion needs further direct experimental validation . However , if correct , the mapping tools presented in this paper would provide a very powerful approach for the identification of interacting loci which is still a major obstacle in complex trait analysis in both human and model animals . The study of the impact of strain variability of M tuberculosis on disease expression is of considerable interest for the implementation of tuberculosis control measures . An increasing body of evidence suggests that different strains/lineages of M . tuberculosis display substantial differences in their pathogenic potential [8] , [9] . In addition , evidence is emerging that genetic variability among BCG vaccine strains is a potent factor in modulating BCG induced anti-tuberculosis immunity [31] . This mycobacterial strain variability reflects an even greater divergence in host responsiveness to both BCG and M . tuberculosis that is largely under host genetic control ( reviewed in [43] ) . These observations raise the question if host and mycobacterial variability are independent of each other . If independent , we would expect hosts to display a spectrum of responsiveness from highly resistant to highly susceptible irrespective of the infecting mycobacterial strain . Similarly , M . tuberculosis strains would vary from highly virulent to mildly virulent across all hosts . Alternatively , it is possible that “susceptibility” and “virulence” are not absolute but rather reflect specific combinations of mycobacterial strain and human host . The latter possibility is supported by recent observations of preferential associations of tuberculosis lineages with ethnic groups that may reflect co-adaptation of M . tuberculosis and its human host [10] . Moreover , a number of host genetic association studies have reported a preferential association of tuberculosis susceptibility variants with specific M . tuberculosis lineages [12] , [13] , [14] . The results of our study obtained in a highly controlled experimental setting support the hypothesis of host – pathogen specific genetic “fits . ” Hence , human susceptibility to tuberculosis may only become tractable by jointly considering host and pathogen genetic backgrounds . By conducting a genome-wide mapping of loci that impact on the splenic and pulmonary burden following a low dose infection with two strains of BCG , we revealed a divergent pattern of susceptibility loci . An unexpected result was the pronounced dynamic of genetic loci impacting on bacillary counts . This observation demonstrated how different genetic control elements came into play as the BCG infection advanced and further emphasized the intimate interplay between host genetics and pathogenesis . Perhaps less surprising was the large difference in the number of loci involved in the control of splenic vs pulmonary bacillary counts . BCG Pasteur shows little dissemination and growth in the lungs of infected mice and the absence of susceptibility loci was therefore expected . However , BCG Russia reaches bacillary counts in the lungs that are similar to those in the spleen . Yet , only one susceptibility locus on chromosome 11 was detected to impact on pulmonary counts while splenic counts are under more complex control . It is interesting that a locus on chromosome 1 which is indistinguishable from the Nramp1 gene had by far the strongest impact on bacillary burden in both BCG Pasteur and Russia , but this effect was limited to splenic counts . By contrast , the chromosome 11 locus was detected only for BCG Russia but in both the spleens and lungs . The results therefore indicate that host genetic control is characterized by very strong common control elements that act in a tissue –specific manner , and by somewhat weaker BCG strain specific susceptibility genes that are not tissue specific . Together these data indicate that host genetic control of mycobacterial replication is sensitive to the particular strains but also to differences in disease manifestations ( here , lung vs spleen ) . Interestingly , the strongest genetic effect ever found in human studies was found in an outbreak of tuberculosis in Northern Canada [44] . During this outbreak , all cases had been infected from a single index case , i . e . a single bacterial strain [45] . A fine tuned host genetic response to mycobacteria might explain why it has been difficult to reproducibly detect strong host genetic effects in human tuberculosis . Consequently , future genetic studies of tuberculosis susceptibility might need to be adjusted on the detailed clinical picture and infecting M . tuberculosis strain .
|
Susceptibility to mycobacterial infection results from a complex interaction between host and bacterial genetic factors . To examine the effect of host and pathogen genetic variability on the control of mycobacterial infection , we infected a panel of genetically related recombinant congenic ( RC ) mouse strains with two closely related strains of Mycobacterium bovis BCG . Bacterial counts of BCG Russia and BCG Pasteur were determined in the lung and spleen at 1 and 6 weeks following infection and used for genetic analysis . A novel analytical approach was developed to perform genome-wide linkage analyses using the RC strains . Comparative linkage analysis using this model identified a strong genetic effect on chromosome 1 controlling counts of BCG Pasteur at 1 week and of BCG Russia at 1 week and 6 weeks in the spleen . A locus impacting on late BCG Russia counts in the lung and spleen was identified on chromosome 11 . Nine additional loci were shown to control bacterial counts in a tissue- , time- , and BCG strain-specific manner . Our findings suggest that the host genetic control of mycobacterial infection is highly dynamic and adapted to the stage of pathogenesis and to the infecting strain . Such a high degree of genetic plasticity in the host-pathogen interplay is expected to favour evolutionary co-adaptation in mycobacterial disease .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases/bacterial",
"infections",
"genetics",
"and",
"genomics/complex",
"traits",
"infectious",
"diseases",
"genetics",
"and",
"genomics"
] |
2010
|
Strain-Specific Differences in the Genetic Control of Two Closely Related Mycobacteria
|
Clostridium difficile infections have become a major healthcare concern in the last decade during which the emergence of new strains has underscored this bacterium's capacity to cause persistent epidemics . c-di-GMP is a bacterial second messenger regulating diverse bacterial phenotypes , notably motility and biofilm formation , in proteobacteria such as Vibrio cholerae , Pseudomonas aeruginosa , and Salmonella . c-di-GMP is synthesized by diguanylate cyclases ( DGCs ) that contain a conserved GGDEF domain . It is degraded by phosphodiesterases ( PDEs ) that contain either an EAL or an HD-GYP conserved domain . Very little is known about the role of c-di-GMP in the regulation of phenotypes of Gram-positive or fastidious bacteria . Herein , we exposed the main components of c-di-GMP signalling in 20 genomes of C . difficile , revealed their prevalence , and predicted their enzymatic activity . Ectopic expression of 31 of these conserved genes was carried out in V . cholerae to evaluate their effect on motility and biofilm formation , two well-characterized phenotype alterations associated with intracellular c-di-GMP variation in this bacterium . Most of the predicted DGCs and PDEs were found to be active in the V . cholerae model . Expression of truncated versions of CD0522 , a protein with two GGDEF domains and one EAL domain , suggests that it can act alternatively as a DGC or a PDE . The activity of one purified DGC ( CD1420 ) and one purified PDE ( CD0757 ) was confirmed by in vitro enzymatic assays . GTP was shown to be important for the PDE activity of CD0757 . Our results indicate that , in contrast to most Gram-positive bacteria including its closest relatives , C . difficile encodes a large assortment of functional DGCs and PDEs , revealing that c-di-GMP signalling is an important and well-conserved signal transduction system in this human pathogen .
Clostridium difficile is a Gram-positive , anaerobic , spore-forming bacterium causing mild diarrhea to fulminant colitis in humans . Due to spreading of hypervirulent and high toxin-producing strains , C . difficile has caused in the last decade several epidemics in Europe and North America where it is now the leading cause of nosocomial diarrhea [1]–[3] . Its ability to sporulate allows this bacterium to remain dormant for years and survive to harsh conditions such as gastric acid after ingestion or the presence of oxygen in the environment . C . difficile-associated diseases are commonly associated with antibiotic usage , which creates a favorable niche for C . difficile to grow and cause infection in part by disrupting the gut microflora . Bis- ( 3′-5′ ) -cyclic dimeric guanosine monophosphate ( c-di-GMP ) is a bacterial second messenger controlling diverse bacterial phenotypes mostly known to be involved in the transition from free-living , motile to biofilm lifestyle in Gram-negative bacteria [4] , [5] . c-di-GMP has also been shown to be involved in the development and cell cycle control in Caulobacter crescentus [6] , [7] , and the modulation of virulence in several pathogens such as Vibrio cholerae , Vibrio vulnificus , Bordetella pertussis or Pseudomonas aeruginosa [8]–[11] . c-di-GMP is synthesized from 2 GTP molecules by enzymes named diguanylate cyclases ( DGCs ) that contain a GGDEF domain [12] . It is degraded respectively into pGpG or 2 GMP by phosphodiesterases ( PDEs ) that contain an EAL ( PDEA ) or a HD-GYP domain [13] , [14] . GGDEF domains were named by Hecht and Newton according to their conserved amino acid motif GG[D/E]EF [12] . EAL and HD-GYP domains are also named based on conserved amino motifs within these domains [13] , [14] . Whole genome analysis of a large number of bacterial species has revealed that the number of genes coding for enzymes involved in c-di-GMP turn-over varies widely between different species [15] . Genomes of proteobacteria generally encode a much wider array of such enzymes compared to those of Gram-positive bacteria . For instance , 66 genes coding for predicted enzymes involved in c-di-GMP turn-over are found in Shewanella oneidensis , 62 in V . cholerae , 41 in Pseudomonas aeruginosa , 29 in Escherichia coli , 6 in Bacillus subtilis and 1 in Staphylococcus aureus . In fact , very little is known about c-di-GMP's input in the regulation of phenotypes within Gram-positive bacteria . GdpS , the sole predicted staphylococcal GGDEF domain-containing protein , positively regulates biofilm formation in both S . aureus and S . epidermidis , and expression of protein A , a major virulence factor in S . aureus . However , GdpS does not appear to be an active DGC in vitro and its C-terminal GGDEF domain is not involved in these two phenotypes [16] , [17] . Although the enzymes producing and degrading c-di-GMP share customary domains in all bacteria , when known , the downstream effectors and pathways regulating the different phenotypical responses are usually different . c-di-GMP-sensing proteins containing the characterized c-di-GMP-binding PilZ domain [18] and some non-PilZ proteins are known c-di-GMP binding receptors . In E . coli and related bacteria , the PilZ domain-containing protein YcgR was found to decrease motility by interacting with the flagellar motor to control its direction and rotation speed upon binding of c-di-GMP [19] , [20] . In V . cholerae , VpsT , a key transcription regulator that inversely regulates the expression of genes associated with motility and biofilm formation , was only recently found to be active following binding of c-di-GMP to its atypical receiver domain [21] . Recently , the first c-di-GMP-binding riboswitches ( c-di-GMP-I ) have been discovered in the genomes of V . cholerae , C . difficile and other bacteria [22] . Riboswitch Cd1 of C . difficile is located upstream of the large operon coding for the synthesis of the flagellum and was found to have an “off” switch action on transcription in an in vitro transcription assay and in β-galactosidase assays in B . subtilis [22] . Additionally , self-splicing of an unusual group I intron in C . difficile genome was found to be allosterically controlled by a c-di-GMP-II riboswitch aptamer , likely enabling the translation of a putative surface protein upon c-di-GMP binding [23] . Furthermore , 37 genes of C . difficile 630 that encode putative proteins containing GGDEF and/or EAL domains are available in the SignalCensus database [24] . Together , these observations suggest that c-di-GMP is a key signalling component in this emerging pathogen; yet studies on proteins regulating the intracellular c-di-GMP level , i . e . DGCs and PDEs , are still lacking . The identification and characterization of enzymes producing or degrading c-di-GMP is a critical step to determine and understand the relevance of this second messenger in C . difficile's lifecycle and virulence . In this study , we analyzed the prevalence and conservation of genes coding for putative DGCs and PDEAs in the genomes of 20 C . difficile isolates . Thirty-one conserved genes were assayed for their ability to encode functional DGC or PDEAs by evaluating the effect of their expression in V . cholerae . Most of these proteins conferred phenotypes that were consistent with their predicted function in our heterologous expression model . Our results indicate that , unlike the vast majority of Gram-positive bacteria including the Clostridiaceae , C . difficile regulates its intracellular c-di-GMP pools via a plethora of functional DGCs and PDEAs .
Initial examination of the Pfam database 24 . 0 [25] for proteins involved in c-di-GMP turn-over in C . difficile 630 , correlated to data provided by the NCBI's SignalCensus database [24] , reveals a total of 37 proteins containing a GGDEF ( Pfam PF00990 ) and/or an EAL ( Pfam PF00563 ) domain: 18 proteins have a GGDEF domain , one protein contains an EAL domain and 18 proteins have both GGDEF and EAL domains . Proteins containing both GGDEF and EAL domains have been shown to act either as DGCs or as PDEAs , or to exhibit both activities [14] , [26] , [27] . Furthermore , not all proteins containing a GGDEF or an EAL domain have been shown to exhibit DGC or PDEA activity . Several conserved amino acids in the GGDEF and EAL domains are predicted to be important to confer enzymatic activity [28] , [29] , among which the highly conserved motifs GG[D/E]EF and EXLR , respectively . These motifs and other conserved amino acid residues were sought for in the primary sequences of the 37 putative c-di-GMP-signalling proteins encoded by the genome of C . difficile 630 by multiple sequence alignment ( Figure 1 ) . Briefly , among those 37 proteins , 15 are most likely active DGCs , 18 could be active PDEAs , 1 protein ( CD0522 ) could either be an active DGC and/or PDEA , and 3 contain a predicted catalytically inactive GGDEF domain ( Table S1 ) . Except for CD0522 , all the proteins with both GGDEF and EAL domains were predicted to be PDEAs having an inactive GGDEF partner domain since these have a degenerated GG[D/E]EF motif . Most of the 37 proteins are predicted to have at least one sensor domain , transmembrane regions or a signal peptide region . To assess whether c-di-GMP signalling components are conserved within the species C . difficile , orthologs of the proteins identified in strain 630 were exhaustively sought for in 19 other partially or completely sequenced C . difficile genomes using tblastn . Most genes found in C . difficile 630 are conserved in all the strains examined with 31 of the 37 GGDEF and/or EAL proteins having an ortholog in at least 15 strains and only CD2753 and CD2754 being unique to strain 630 ( Figure 2A ) . The putative glycosyltransferase CD2545 , the sole protein predicted to contain a PilZ domain ( Pfam PF07238 ) , has orthologs in all the other analyzed strains ( Figure 2A ) . Furthermore , two RNA targets ( named herein Cd1-a and 84Cd , respectively ) that have been shown to bind c-di-GMP , the riboswitch Cd1 located upstream of the flagellum synthesis operon , and the self-splicing group I intron ( tandem riboswitch-ribozyme ) are conserved in a subset of strains [22] , [23] . The conservation pattern of the c-di-GMP regulatory proteins and downstream effectors clearly follows the phylogenetic distribution of the strains ( Figure 2A and 2B ) . Interestingly , the cluster of 10 hypervirulent NAP1/BI/027 strains ( cluster CD196/2007855 in Figure 2B ) regroups the strains encoding the lowest number of c-di-GMP signalling components . Like CD2753 and CD2754 that are specific to C . difficile 630 , we assumed that other strains could also encode c-di-GMP signalling proteins that were absent from all of the other strains . The Microbial Signal Transduction database ( MiST2 ) [30] , which currently provides data for 14 out of our 19 strains , contains 3 putative c-di-GMP turn-over proteins that are absent from C . difficile 630's proteome . Notably , CdifA_020200002673 , is unique to strain ATCC43255 ( Figure 2A ) , and is the only HD-GYP domain-containing protein detected in C . difficile . Based on analysis of amino acid conservation , it is predicted to have both DGC and PDE enzymatic activities ( Figure S1 ) . To identify additional strain-specific genes encoding c-di-GMP signalling proteins , we performed profile hidden-Markov model ( profile HMM ) searches with the Pfam HMMs for GGDEF , EAL , HD and PilZ conserved domains using HMMER3 software against the proteomes of strains 2007855 , BI1 , BI9 , CF5 , M120 and M68 as predicted by GeneMark . hmm ( Table S2 ) . No additional c-di-GMP-signalling proteins were detected . Besides CD2545 , no other PilZ domain-containing protein was found . Finally , no GGDEF , EAL , HD-GYP or PilZ domain-containing proteins were found to be encoded by C . difficile plasmids pCD630 , pCD6 , pCDBI1 or the 300 kb putative phage or plasmid from BI1 ( Table S2 ) . The remarkable prevalence of c-di-GMP-signalling components among C . difficile strains suggests that c-di-GMP is an important second messenger in this bacterium . The functionality of the 31 most conserved GGDEF and/or EAL domain proteins among C . difficile strains ( Figure 2A ) was therefore assessed to confirm their biological activity . To the best of our knowledge , no model of Gram-positive bacteria is currently available to efficiently and reliably evaluate in vivo the enzymatic activity of proteins regulating the intracellular levels of c-di-GMP . Moreover , to circumvent the tedious laboratory procedures associated with working with C . difficile and the lack of information regarding the phenotypes regulated by c-di-GMP in this bacterium , assessment of the enzymatic activity of the 31 putative DGCs and PDEAs ( cloned from C . difficile 630 ) was carried out by heterologous expression in V . cholerae . Characteristic phenotype alterations associated with variations of the intracellular c-di-GMP concentration of V . cholerae are easily observable and measurable . High levels of intracellular c-di-GMP concentrations increase biofilm formation and decrease motility whereas lower c-di-GMP concentrations cause the opposite effects [31] . As expected , ectopic expression of most of the predicted DGCs containing the canonical GG[D/E]EF motif decreased cell motility ( Figure 3A ) and increased biofilm formation ( Figure 3C ) , supporting the hypothesis that these C . difficile proteins are genuine and functional DGCs ( Table S1 ) . Replacement of the second glycine of the GGDEF motif by site-directed mutagenesis has been shown to greatly reduce the enzymatic activity of other DGCs [32] , [33] . Such a mutation in CD1420 ( mutant G204E ) abolished alterations of both biofilm and motility phenotypes ( Figure 4A , 4B and 4C ) confirming that the phenotypical alterations observed in V . cholerae are linked to the enzymatic activity of this functional C . difficile DGC . Moreoever , the expression of CD1420 in V . cholerae N16961 led to a dramatic increase of the c-di-GMP level compared to the same strain expressing LacZ ( Figure S2B and S2D , Text S1 ) . Furthermore , we observed that CD1015 , CD1185 and CD1420 , the DGCs causing the strongest phenotypical shifts ( Figure 3A and 3C ) , lacked the amino acid motif RXXD that is part of the retro-inhibition site ( I-site ) of the GGDEF domain ( Figure 1 ) [34] . The putative DGC CD2887 significantly enhanced biofilm formation without affecting the motility of V . cholerae . Interestingly , CD2887 contains a GGEEY motif instead of the canonical GG[D/E]EF motif suggesting that it might not be a functional DGC . However , Malone and colleagues [35] showed that upon substitution of the F amino acid residue for a Y residue in the A-site , the DGC WspR of Pseudomonas fluorescens retains its activity . Unlike the putative DGCs presented above , several predicted DGCs that appear to possess all the conserved amino acid residues necessary for c-di-GMP synthesis ( e . g . CD2385 and CD3365 ) did not modulate biofilm formation or motility of V . cholerae in our experimental conditions . Yet we cannot conclude that these putative DGCs are not functional since they might not have been produced , may have been unstable or may lack the appropriate activating signal in the heterologous host . Unexpectedly , CD0537 , which also has a canonical A-site , did not enhance biofilm formation but enhanced cell motility by ∼60% . While this increase is notable compared to other DGCs , it remained very modest compared to the increase promoted by PDEAs ( see below ) suggesting that in our experimental setting , this putative DGC was not functional . The unexpected result on motility could result from partial sequestration of intracellular c-di-GMP by CD0537's I-site upon overexpression of the protein . Additionally , the lack of apparent DGC activity of CD0537's might simply be due to a lack of phosphorylation of its phosphoreceptor REC domain , a modification that could be catalyzed by the putative kinase cheA ( CD0539 ) that was absent in our assay . Other DGCs have been shown to be activated by phosporylation of their phosphoreceptor REC domain [29] , [32] , [36] . As expected , CD1028 and CD2384 , which respectively possess the degenerated QKDMI and GGEEI motifs , did not alter V . cholerae's phenotypes . Consistent with our observation , the substitution of the F residue of the A-site for an I residue alone is known to eliminate the activity of WspR of P . fluorescens [35] . Expression of several putative PDEAs ( CD0757 , CD1515 , CD1616 , CD1840 and CD2663 ) significantly enhanced cell motility on soft agar by 2 to 4 fold ( Figure 3B and Table S1 ) . While five putative PDEAs exhibited significant activity ( CD0204 , CD1421 , CD1515 , CD2134 and CD2663 ) , in most cases , the impact of ectopic expression of these proteins on biofilm formation was modest and not significant ( Figure 3D ) . The weak response of V . cholerae N16961 to PDEA activity could be due to its low basal level of biofilm formation in our assays . However , we indirectly confirmed that the V . cholerae motility response to overexpression of these proteins was linked to PDEA activity by mutating the glutamic acid residue of the EVLxR motif of CD0757 , which is critical for enzymatic activity . Indeed , unlike the wild-type protein , overexpression of CD0757-E339A did not enhance the motility of V . cholerae N16961 on soft agar ( Figure 4A and 4C ) . Since CD0522 has a particular combination of GGDEF and EAL domains suggesting that it could have both DGC and PDEA activities , it was analyzed apart from those in Figure 3 . CD0522 contains two predicted N-terminal GGDEF domains and one predicted C-terminal EAL domain ( Figure 1 and Figure 4D ) . Our analysis indicates that the first GGDEF domain and the EAL domain should be catalytically active due to the conservation of the A-sites . However , the second GGDEF domain contains the strongly degenerated motif YADVF suggesting that it is catalytically inactive ( Figure 1 and Figure 4D ) . CD0522 or its individual domains were tested in our V . cholerae heterologous expression model to verify these predictions ( Figure 4C , 4E and 4F ) . Since the variations of cell motility and biofilm formation we observed upon ectopic expression of CD0522 were not statistically significant , we could not clearly establish a DGC activity for the complete CD0522 . On the other hand , expression of the N-terminal fragment N-448 , which encompasses the first GGDEF domain , reduced motility by more than half and increased biofilm by ∼7 fold , suggesting that this fragment of CD0522 acts as a functional DGC ( Figure 4E and 4F ) . The observed phenotypes correlate with a marked increase of intracellular c-di-GMP upon expression of N-448 in V . cholerae N16961 compared to the same strain expressing LacZ ( Figure S2B and S2C , Text S1 ) . DGC catalytic activity of CD0522 was also indirectly confirmed by mutating the glutamic acid residue of the EAL motif to abolish any possible PDEA activity ( Figure 4D ) . Expression of CD0522-E814A altered phenotypes as expected for a DGC , as observed for N-448 ( Figure 4C , 4E and 4F ) . Expression of the C-terminal fragment C-307 led to a modest but significant increase of motility . As expected , expression of the degenerated GGDEF-containing central fragment M-305 did not lead to any significant change of phenotype compared to the control . The degenerated GGDEF domain of M-305 could be involved in the regulation of the PDEA activity of the EAL domain of CD0522 . We observed that motility of cells expressing MC-591 , which contains the central and C-terminal fragments , increased by ∼50% ( Figure 4E ) , which is consistent with a diminution of intracellular c-di-GMP . Increased motility was also observed when we overexpressed CD0522-G366E , a protein containing a substitution of the second glycine residue of the first GGDEF to abolish any DGC activity ( Figure 4C and 4E ) . Complex proteins such as CD0522 that are composed of several GGDEF and EAL domains suggest a possible two-way c-di-GMP control and must be studied in detail to reveal what stimuli switches their enzymatic activity between the DGC or PDEA state . CD1420 and CD0757 enzymatic activities were further assessed in vitro to corroborate the results obtained in the V . cholerae expression model and confirm that the C . difficile proteins are genuine DGC and PDEA , respectively . These proteins were chosen for their strong activity in V . cholerae and the simplicity of the structure of the N-terminal sensor domain that suggested little requirements for in vitro assays ( Figure 1 ) . Purified CD1420 in its native form was able to produce c-di-GMP from GTP as substrate and the accumulation of the product increased with time ( Figure 5A ) . Conversely , purified CD0757 did not produce c-di-GMP from GTP even after 1 h incubation . Therefore , we confirmed that CD1420 is a functional DGC . The absence of DGC activity of CD0757 suggests that it contains an inactive GGDEF domain and acts as a PDEA only . CD0757 was then assessed for PDEA activity on c-di-GMP . c-di-GMP hydrolysis by PDEAs is known to yield the linear diguanylate pGpG [37] . We incubated purified CD0757 with radiolabeled c-di-GMP , yielding small amounts of pGpG . This characteristic PDEA activity was abolished by denaturing the protein prior to the assay ( Figure 5B ) . Inactive GGDEF domains have been shown to enhance PDEA activity of an adjacent EAL domain by binding GTP [26] . Addition of GTP to the enzymatic reaction increased noticeably the PDEA activity of CD0757 presumably through binding to the GGDEF domain like for PdeA ( CC3396 ) from C . crescentus . After a 30-min incubation period , virtually all the c-di-GMP was converted to pGpG . Marginal degradation of c-di-GMP to GMP by CD0757 was detected as previously shown to occur with another PDEA [37] .
Studies on c-di-GMP have addressed with some depth many aspects regarding the proteins involved in its synthesis ( DGCs and PDEAs ) and the molecular targets of c-di-GMP such as proteins and riboswitches in several bacteria . While c-di-GMP signalling has been extensively studied in many Gram-negative bacteria like C . crescentus , E . coli , V . cholerae , Salmonella and Pseudomonas , very few studies have been carried out on Gram-positive bacteria . To the best of our knowledge , the staphylococcal GGDEF domain protein GdpS has been the only c-di-GMP regulatory protein studied to date in low G+C Gram-positive bacteria . GdpS does not seem to have any measurable DGC activity [16] . The recent discovery of a functional c-di-GMP binding riboswitch in C . difficile and Bacillus cereus , as well as the prediction of several other similar riboswitches in other Gram-positive bacteria has revived the interest in studying c-di-GMP metabolism in these microorganisms . The recent characterization of a c-di-GMP-dependent self-splicing group I ribozyme in C . difficile further reinforces the role of c-di-GMP in Gram-positive bacteria . In this work , we have shown that many of the genes encoding putative DGCs and PDEAs of C . difficile behave like genuine DGCs and PDEAs in heterologous expression experiments ( Figure 3 and Figure 4 , Table S1 ) . This number of c-di-GMP regulatory proteins encoded by C . difficile is high compared to what is found in its closest relatives ( Table S3 ) , and also among the Firmicutes in general ( median = 1 ) [38] . Analysis of the genomes of 49 strains of Clostridiaceae representing 27 species revealed that most contain less than 20 of such genes ( Table S3 ) . Only 2 species of Clostridium were found to encode more putative DGCs/PDEAs than C . difficile , Clostridium asparagiforme DSM15981 and Clostridium bolteae ATCC BAA-613 , two newly characterized yet barely studied species isolated from human fecal samples [39] , [40] . The disparity in the occurrence of c-di-GMP signalling proteins is remarkable . The two species coding for the lowest number of c-di-GMP regulatory proteins , Clostridium hiranonis and Clostridium bartlettii , are the closest phylogenetically related species to C . difficile ( Figure S3 and Table S3 ) . On the opposite , the two species coding for the highest number of GGDEF , EAL or HD-GYP protein , C . asparagiforme and C . bolteae , are among the most distant species from C . difficile . Additionally , C . difficile , which encodes with one exception no HD-GYP domain proteins , seems to be an exception among the Clostridiaceae and contrasts with Clostridium beijerinckii which encodes 14 HD-GYP domain proteins ( Table S3 ) , while retaining the same number of c-di-GMP regulatory proteins as C . difficile . In addition , C . difficile does not seem to carry any gene encoding c-di-GMP-signalling proteins that could have been recently exchanged by horizontal transfer with the 3 other Clostridium species containing the highest number of such proteins ( Table S3 and data not shown ) . The Clostridiaceae seem to have a high number of c-di-GMP-signalling proteins among the Firmicutes in general , but it remains similar to other Firmicutes of comparable size ( 3000–4000 genes , median = 10 ) [38] . The high number of c-di-GMP turn-over proteins in C . difficile is likely indicative of the importance of this second messenger in the bacterium's lifecycle and suggests a major role in regulating different phenotypes . The diversity of N-terminal structures suggests that their function is not redundant . Instead , these proteins could individually act in a functionally or spatially sequestered way , in addition to being temporally regulated through differential expression . It has been shown that DGCs usually are not interchangeable and can contribute to very specific and distinct phenotypes for a unique microorganism . For example , while the DGC YddV of E . coli impacts poly-N-acetylglucosamine production , other DGCs like AdrA do not [41] . Instead AdrA controls the production of cellulose , another exopolysaccharide , in E . coli and Salmonella [42] , [43] . Furthermore , the prevalence of DGCs and PDEAs in C . difficile could also indicate the importance of these proteins in sensing and relaying a diversified array of environmental conditions through their sensor domains or their eventual differential expression . In V . cholerae , the PDEA CdpA is not expressed in vivo until the late stage of infection in a mouse colonization model [44] . C . difficile vegetative cells encounter various environmental conditions during their journey through the gastrointestinal tract , during which c-di-GMP signalling might play a role in regulating diverse phenotypes . Despite the current lack of experimental data , it is reasonable to assume that c-di-GMP regulates at least two phenotypes in C . difficile: flagella synthesis/motility and polysaccharide synthesis . A putative c-di-GMP-binding PilZ domain is located in the putative glycosyltransferase CD2545 , which is predicted to be a cellulose synthase . Interestingly , although motility is commonly controlled by c-di-GMP in bacteria , the c-di-GMP-responsive effectors that likely regulate this phenotype in C . difficile appear to differ from those found in V . cholerae , E . coli and related bacteria . The c-di-GMP-sensing riboswitch Cd1 appears to control the transcription of the large operon of genes essential for assembling the flagellum apparatus [22] . Bacterial flagella are obviously important for motility but can also be involved in adhesion . Adhesion to mouse cecal mucus of the flagellin FliC and of the flagellum cap protein FliD of C . difficile has been demonstrated in vitro [45] . Moreover , FliD has been shown to specifically adhere to cultured cells [45] . Therefore , c-di-GMP signalling might impact both cell motility and adhesion of C . difficile to mucosal surface through the regulation of flagellum assembly . Additionally , c-di-GMP signalling may play a significant role in the excessive inflammation caused by C . difficile infection since flagellin is a very potent immunogenic protein recognized as a proinflammatory ligand by toll-like receptor 5 ( TLR-5 ) located at the baso-lateral surface of intestinal cells ( reviewed in [46] ) . Except for one EAL protein ( CD3650 ) , all of C . difficile's PDEAs contain a GGDEF domain predicted to be non-catalytically active as shown in vitro for CD0757 ( Figure 1 and Figure 5 ) . Composite proteins containing both GGDEF and EAL domains are relatively frequent , representing approximately one third of proteins with such domains [47] . Some of these composite proteins act as DGCs , PDEAs , or have both activities ( reviewed in [47] ) . Inactive GGDEF or EAL domains can act as sensor domains rather than catalytic domains . GGDEF domain proteins with degenerated active sites have been reported to bind c-di-GMP at their conserved I-site , as for the C . crescentus protein PopA [48] , or to retain the ability to bind GTP at their degenerated active site , as for the C . crescentus protein PdeA [26] . Christen and colleagues [26] have demonstrated that binding of GTP to the inactive GGDEF domain of PdeA of C . crescentus , strongly enhanced the PDEA activity of the C-terminal EAL domain . The authors formulated two hypotheses to explain why the PDEA activity is linked to GTP intracellular concentrations: ( i ) to prevent GTP pools to drop by the uncontrolled successive activities of DGCs and PDEAs and ( ii ) to sense physiological changes . Intracellular GTP levels have been shown to impact the activity of CodY , a major transcriptional regulator in many low G+C Gram-positive bacteria such as C . difficile , S . aureus , Streptococcus pneumoniae , Streptococcus pyogenes , Streptococcus mutans , Listeria monocytogenes , B . cereus and Bacillus anthracis in which it affects virulence gene expression ( [49] and references therein ) . CodY is known to have greater affinity to target DNA promoter regions upon binding of two synergistic effectors , GTP and branched-chain amino acids [50] , [51] . C . difficile CodY has been shown to repress the expression of the toxin A and B genes ( tcdA and tcdB ) , through binding to the promoter region of the positive transcriptional regulator TcdR [52] . A recent study aimed at identifying all DNA promoter regions targeted by C . difficile CodY as well as genes differentially expressed in a codY null mutant [49] . Interestingly , among the 165 genes identified with altered expression , PDEA genes CD0757 and CD1476 were highly derepressed . Additionally , DNA regions containing CD1476 , CD2385 , CD2873 , CD2965 and CD3650 were identified as CodY binding-sites . These data suggest a probable interplay between the c-di-GMP and CodY signalling pathways , known to be important in the regulation of many metabolic genes and of the major virulence factors , toxins A and B [49] , [52] . To the best of our knowledge , no model of Gram-positive bacteria is currently available to efficiently and reliably evaluate the enzymatic activity of proteins regulating the intracellular levels of c-di-GMP . With the recent availability of molecular tools for C . difficile genetic manipulation [53]–[55] , it will finally be possible to study in detail the many genes involved in c-di-GMP signalling and turn-over in this bacterium and to identify the phenotypes associated with the variation of intracellular c-di-GMP pools . The need to decipher the regulatory mechanisms underlying C . difficile's behaviors is imperative to the development of new therapeutics and treatment strategies . Particularly , the bacterial signalling pathways and phenotypes involved at the colon mucosal interface ought to be addressed .
Proteins containing the c-di-GMP-associated conserved domains ( GGDEF , EAL , HD for HD-GYP and PilZ ) were searched for in the Clostridiaceae proteomes on the Pfam 24 . 0 server [25] . HD domains were further analyzed to identify HD-GYP domains by looking for the HD-GYP amino acid motif by multiple alignment with the HD-GYP domain of Rpfg from Xanthomonas campestris 8004 ( Accession number AAY49388 ) using ClustalW version 2 . 0 . 12 [56] . Other conserved domains , signal peptides , transmembrane regions , and coiled-coil motifs annotations are as determined on Pfam 24 . 0 [25] . Identification of proteins with c-di-GMP-associated conserved domains in C . difficile strains other than 630 was achieved with the hmmsearch program of the HMMER 3 . 0 software ( http://hmmer . org/ ) . C . difficile annotated protein sequences were retrieved for the 13 other strains and 2 plasmids available in the NCBI Refseq database ( Table S2 ) [57] . Protein sequences from C . difficile 2007855 , BI1 , BI9 , CF5 , M120 and M68 genomes and extrachromosomal sequences ( Table S2 ) were predicted using GeneMark . hmm for Prokaryotes version 2 . 4 [58] . Profile hidden Markov models ( profile-HMMs ) of c-di-GMP-associated conserved domains were downloaded from Pfam 24 . 0 . The bit score threshold values used in every search were the “trusted cutoff” values for the Pfam profile-HMMs . Proteins containing the c-di-GMP-related conserved domains identified using HMMER 3 . 0 software were further analyzed to identify other conserved domains ( Pfam 24 . 0 ) , signal peptides and transmembrane regions ( Phobius [59] ) , and coiled-coil motifs ( ncoils [60] ) . Nucleotide and amino acid conservation of selected C . difficile 630 genes and proteins were assessed with the appropriate BLAST algorithms [61] . Since most of the genomes are drafts , pseudogenes were ignored and assumed to be the results of sequencing errors . As a matter of fact , pseudogenes are found in many of these strains even for important , unique and well-conserved genes such as the gene encoding DNA polymerase I ( data not shown ) . Phylogenetic trees were generated using the neighbor-joining method as implemented by ClustalX version 2 . 012 [56] from gapless alignments of nucleotide sequences . Nucleotide sequences were aligned using ClustalW version 2 . 0 . 12 [56] and gap columns were removed using Jalview version 2 . 5 multiple alignment editor [62] . The reliability of each tree was subjected to a bootstrap test with 1000 replications . Trees were edited using FigTree version 1 . 3 . 1 ( http://tree . bio . ed . ac . uk/software/figtree/ ) . Bacterial strains were routinely grown in Luria–Bertani ( LB ) broth at 37°C in an orbital shaker and maintained at −80°C in LB broth containing 15% ( v/v ) glycerol . Ampicillin ( Ap ) was used at 100 µg ml−1 when needed . For induction of gene expression in the strains carrying arabinose-inducible vectors ( pBAD series ) , L-arabinose was added to the growth medium at a final concentration of 0 . 02% ( w/v ) . The bacterial strains and plasmids used in this study are described in Table S4 . The oligonucleotides used for plasmid constructions are described in Table S5 . For expression of putative DGCs and PDEAs in V . cholerae , genes cloned in pBAD-TOPO were amplified by PCR with their native Shine-Dalgarno sequence using C . difficile 630 genomic DNA as a template . Truncated versions of CD0522 were cloned to include the native Shine-Dalgarno sequence of CD0522 . DNA was amplified to express CD0522 protein fragments N-448 , M-305 , C-307 and MC-591 respectively containing the N-terminal 448 amino acids ( aa ) , 305 aa encompassing the middle domain , the 307 aa in C-terminal and 591 aa encompassing the middle and C-terminal domains ( Figure 4D ) . Plasmids pCD0522-G366E , pCD0522-E814A , pCD0757-E339A and pCD1420-G204E , which accordingly contain amino acid substitutions in their respective conserved GG[D/E]EF or EXL A-sites , were created by site-directed mutagenesis of pCD0522 , pCD0757 and pCD1420 using the QuickChange Lightning Site-directed Mutagenesis Kit ( Stratagene ) using primer pairs listed in Table S5 . The mutations introduced were designed to create new EarI or PvuII restriction sites for initial screening of the mutated plasmids . Mutated genes were verified by sequencing . For CD0757 and CD1420 proteins purification , the corresponding genes were amplified by PCR from C . difficile 630 genomic DNA and cloned into BamHI/SalI-digested pGEX6P-1 in frame with the glutathione S-transferase ( GST ) coding sequence . All the enzymes used in this study were obtained from New England BioLabs and were used according to the manufacturer's instructions . Plasmid DNA was prepared with a Qiaprep Spin miniprep kit ( Qiagen ) . Genomic DNA of C . difficile 630 was extracted using the illustra bacteria GenomicPrep mini spin kit ( GE Healthcare ) . PCR assays were performed with the primers described in Table S4 in 50 µl of PCR mixtures with 1 U of Pfu Ultra DNA polymerase ( Agilent ) . PCR conditions were as follows: ( i ) 3 min at 94°C , ( ii ) 30 cycles of 30 s at 94°C , 30 s at suitable annealing temperature , and 30–300 s at 72°C , and ( iii ) 5 min at 72°C . When needed PCR products were purified using a QIAquick PCR Purification Kit ( Qiagen ) according to the manufacturer's instructions . E . coli was transformed by electroporation according to Dower and colleagues [63] . V . cholerae was transformed by electroporation according to Occhino and colleagues [64] . In both cases , transformation was carried out in 0 . 1 cm electroporation cuvettes using a Bio-Rad GenePulser Xcell apparatus set at 25 µF , 200 Ω and 1 . 8 kV . Motility and biofilm assays were performed as described before [32] . Briefly , a semi-solid medium composed of 1% tryptone , 0 . 5% NaCl , 0 . 3% agar supplemented with ampicillin and L-arabinose was used to evaluate motility of V . cholerae mutant strains during over-expression assays at 30°C . Motility was assessed from the comparison of the surface area ( mm2 ) of the colonies from plate images captured and analyzed using a Gel Doc XR system and Quantity One software ( Bio-Rad ) . The capacity of V . cholerae mutant strains to form biofilm was determined after 6 h static growth in LB broth containing ampicillin and L-arabinose at 30°C . Bound crystal violet was solubilized with 200 µl of 95% ethanol and quantified by absorbance at 595 nm in a Model 680 microplate reader ( Bio-Rad ) . Motility and biofilm formation assays were carried in triplicate and data were normalized as fold expression compared with the control LacZ over-expressing bacteria . Data from at least three independent experiments were combined . Overnight-grown cultures of E . coli BL21 bearing pGCD0757 or pGCD1420 were diluted 1∶100 in fresh 2× YTA broth and incubated at 37°C with agitation . Protein expression was induced with 0 . 1 mM IPTG ( isopropyl 1-thio-β-D-galactopyranoside ) at mid-exponential phase ( OD600 of 0 . 6 ) for CD0757 or at late-exponential phase ( OD600 of 1 . 2 ) for CD1420 . The cultures were grown for an additional 4 h at 37°C for CD0757 or 2 h at 25°C for CD0757 . Cells were collected by centrifugation , re-suspended in PBS containing 1% Triton X-100 and protease inhibitors ( Protease Inhibitor Cocktail , Sigma ) , and lysed by sonication . CD0757 and CD1420 were recovered by affinity chromatography using the GST purification module ( GE Healthcare ) with the PreScission protease ( GE Healthcare ) according to the manufacturer's instructions . After elution , proteins samples were dialyzed against the conservation buffer ( 50 mM Tris-HCl pH 7 . 8 , 250 mM NaCl , 25 mM KCl , 10 mM MgCl2 , 30% glycerol ) for 18 h in D-Tube Dialyzer Maxi ( MWCO 12–14 , Novagen ) , concentrated by centrifugation on Amicon Ultra-15 columns ( MWCO 10 , Millipore ) , and stored at −20°C . Protein concentration was estimated using a BCA Protein Assay Kit ( ThermoScientific ) and purity was determined by SDS-PAGE analysis . Diguanylate cyclase and phosphodiesterase activities were measured according to previously described procedures [32] , [37] with the following modifications . Diguanylate cyclase assays were performed with approximately 1–2 µg of purified proteins in a final volume of 50 µl . Reaction mixtures were pre-incubated for 5 min at 30°C in the reaction buffer ( 50 mM Tris-HCl pH 7 . 8 , 250 mM NaCl , 25 mM KCl , 10 mM MgCl2 ) . DGC reactions were initiated by adding 33 . 3 nM [α-33P]-GTP ( 0 . 1 µCi µl−1 ) and incubated at 30°C . Samples were taken at various times , and the reactions were stopped by addition of one volume 0 . 5 M EDTA . Radiolabeled c-di-GMP for phosphodiesterase activity assays was synthesized using purified DgcK [32] . Purified DgcK ( 30 µg ) was incubated 8 h at 30°C in the reaction buffer to completely convert [α-33P]-GTP into c-di-GMP . Reactions were stopped by denaturing at 99°C for 15 min , centrifuged for 2 min at 16 , 000 g to elimate DgcK and recover the supernatant containing the radiolabeled c-di-GMP . Phosphodiesterase assays were performed with approximately 1–2 µg of purified proteins in a final volume of 50 µl of reaction buffer containing 20 nM prepared radiolabeled c-di-GMP ( 0 . 1 µCi µl−1 ) with or without 100 µM GTP . One unit of snake venom phosphodiesterase ( Phosphodiesterase I , Worthington ) suspended in SVPD conservation buffer ( 100 mM Tris-HCl pH 8 . 0 , 100 mM NaCl , 14 mM MgCl2 , 50% glycerol ) was used as a positive control in PDEA assays . Proteins denatured at 99°C for 15 min were used as negative controls in both DGC and PDEA assays . Reaction products were analyzed by TLC as described before [32] . Briefly aliquots ( 2–4 µl ) were spotted on polyethyleneimine-cellulose TLC plates ( Sigma ) previously washed in 0 . 5 M LiCl and air dried . Plates were then soaked for 5 min in methanol , dried , and developed in 2∶3 ( v/v ) saturated ( NH4 ) 2SO4/1 . 5 M KH2PO4 ( pH 3 . 5 ) . Plates were allowed to dry prior to exposition to a phosphor imaging screen ( Molecular Dynamics ) . Data were collected and analyzed using a FX molecular imager and the Quantity One software ( Bio-Rad ) .
|
c-di-GMP is a bacterial intracellular signalling molecule regulating motility , biofilm formation , cell cycle control , or virulence in Gram-negative bacteria . The function and importance of this molecule still remain unknown in Gram-positive bacteria , even in important emerging pathogens such as Clostridium difficile , which causes from mild to deadly intestinal infections and has lately been on the rise in the healthcare setting and in the community . Here , we expose in the genomes of C . difficile strains a large number of conserved genes encoding proteins involved in the synthesis , degradation , or sensing of c-di-GMP , in contrast with most other Gram-positive bacteria including C . difficile's closest relatives . We confirmed the activity of most of these well-conserved proteins in a microorganism for which typical behavior alterations associated with variation of the intracellular c-di-GMP pools are known . We further confirmed the c-di-GMP synthesis and degradation activities of two purified C . difficile proteins . Our results indicate that c-di-GMP signalling is important in the lifecycle of this pathogen . This finding is particularly exciting because the c-di-GMP signalling network could serve as a target for the development of new drugs against C . difficile-associated diseases that are commonly associated with antibiotic usage .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"bacteriology",
"medicine",
"microbial",
"metabolism",
"microbiology",
"host-pathogen",
"interaction",
"bacterial",
"biochemistry",
"microbial",
"growth",
"and",
"development",
"infectious",
"diseases",
"microbial",
"physiology",
"medical",
"microbiology",
"microbial",
"pathogens",
"biology",
"gastrointestinal",
"infections",
"molecular",
"biology",
"bacterial",
"physiology",
"signal",
"transduction",
"molecular",
"cell",
"biology"
] |
2011
|
c-di-GMP Turn-Over in Clostridium difficile Is Controlled by a Plethora of Diguanylate Cyclases and Phosphodiesterases
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cis-regulatory DNA sequences known as enhancers control gene expression in space and time . They are central to metazoan development and are often responsible for changes in gene regulation that contribute to phenotypic evolution . Here , we examine the sequence , function , and genomic location of enhancers controlling tissue- and cell-type specific expression of the yellow gene in six Drosophila species . yellow is required for the production of dark pigment , and its expression has evolved largely in concert with divergent pigment patterns . Using Drosophila melanogaster as a transgenic host , we examined the expression of reporter genes in which either 5′ intergenic or intronic sequences of yellow from each species controlled the expression of Green Fluorescent Protein . Surprisingly , we found that sequences controlling expression in the wing veins , as well as sequences controlling expression in epidermal cells of the abdomen , thorax , and wing , were located in different genomic regions in different species . By contrast , sequences controlling expression in bristle-associated cells were located in the intron of all species . Differences in the precise pattern of spatial expression within the developing epidermis of D . melanogaster transformants usually correlated with adult pigmentation in the species from which the cis-regulatory sequences were derived , which is consistent with cis-regulatory evolution affecting yellow expression playing a central role in Drosophila pigmentation divergence . Sequence comparisons among species favored a model in which sequential nucleotide substitutions were responsible for the observed changes in cis-regulatory architecture . Taken together , these data demonstrate frequent changes in yellow cis-regulatory architecture among Drosophila species . Similar analyses of other genes , combining in vivo functional tests of enhancer activity with in silico comparative genomics , are needed to determine whether the pattern of regulatory evolution we observed for yellow is characteristic of genes with rapidly evolving expression patterns .
The production of a complex , multi-cellular organism requires transcription of a subset of the genome in each cell . This process , known as gene expression , is controlled by cis-regulatory DNA sequences that interact with trans-regulatory proteins and RNAs . These cis-regulatory sequences include “enhancers” , which contain binding sites for transcription factors . The specific combination of transcription factor binding sites within an enhancer determines its activity and specifies the timing , location , and abundance of expression for the gene it regulates . Many genes , especially those involved in development , are controlled by multiple enhancers , each of which controls a subset of the gene's total expression pattern and can be located 5′ , 3′ or in an intron of the gene whose transcription it regulates . Like all DNA , cis-regulatory sequences are subject to the unavoidable process of mutation , which – over evolutionary time – can change enhancer sequence , enhancer function , and the genomic location of enhancers relative to the gene whose expression they control . Comparing the cis-regulatory architecture of orthologous genes among species reveals how they evolve as well as which features are essential for their activity . Conserved sequences between orthologous enhancers represent putatively functional elements ( e . g . , [1] , [2] ) , but conservation of DNA sequence is not strictly required for conservation of enhancer function: transcription factor binding sites are often degenerate and comparable enhancer functions can be produced by multiple arrangements of these sites [3]–[6] . Compared to enhancer sequence , enhancer location within the genome ( relative to exonic sequences of the associated gene ) appears to be more constrained . For example , the location of enhancers is conserved for the even-skipped gene between Drosophila and Sepsid species [5] , which diverged over 100 million years ago , and for six Dorsal target genes between Drosophila and Anopheles or Tribolium [7] , which diverged over 200 million years ago . In fact , conservation of enhancer location within the genome is something that many researchers rely upon in their search for orthologous enhancers . Here , we investigate the evolution of cis-regulatory architecture controlling expression of the Drosophila yellow gene . Yellow is required for the production of dark melanic pigment in insects [8]–[10] , and its expression during late pupal stages has evolved in a manner that often correlates with the distribution of melanins in adults [11]–[13] . In D . melanogaster , yellow expression is controlled by multiple tissue-specific enhancers , with enhancers driving expression in the pupal wing , abdomen , and thorax located 5′ of the yellow gene and an enhancer driving expression in bristle-associated cells located within its lone intron [12] , [14]–[16] . Comparisons of yellow expression and regulation among species suggest that changes in cis-regulatory activity are most often responsible for divergent yellow expression patterns [11]–[14] , [17] , although changes in trans-regulatory factors also contribute to expression divergence in some species [12] , [17] ) . Changes in the spatial pattern of yellow expression within the developing abdomen result from changes in orthologous enhancers located in the 5′ intergenic sequences of yellow [12] , [14] , and convergent yellow expression in “spots” on the developing wing results from enhancers that evolved in the 5′ intergenic region of one species and in the intron of another [11] , [13] , [17] . To examine the evolution of yellow cis-regulatory architecture more comprehensively and systematically , we determined the enhancer activity of sequences 5′ of yellow and in its intron for six species spanning the phylogenetic tree of the genus Drosophila . These species include members of both the Drosophila ( D . mojavensis , D . virilis , and D . grimshawi ) and Sophophora ( D . melanogaster , D . pseudoobscura , and D . willistoni ) subgenera and have pairwise divergence times ranging from approximately 20 to 40 million years ago [18] , [19] . Surprisingly , we found that the location of yellow enhancer activity controlling expression in a particular tissue- or cell-type differed frequently among species , with only the enhancer controlling bristle-associated expression located in the same genomic region of all species . These differences in cis-regulatory architecture were accompanied by differences in enhancer activity that often correlated with species-specific pigment patterns , as expected based on prior studies [11]–[14] , [17] . Sequence comparisons between pairs of species showed no clear evidence of duplications or transpositions near yellow , suggesting that differences in enhancer location among species evolved by sequential sequence substitutions , one or a few nucleotides at a time . To the best of our knowledge , such extensive and rapid turnover in the genomic location of enhancers has not been observed for any other eukaryotic gene .
All DNA fragments tested for enhancer activity were sufficient to activate GFP expression above background levels in at least one tissue during the pupal stage examined ( Figure 2 ) . A DNA fragment was considered to lack enhancer activity in a particular tissue if it failed to drive GFP expression above background in that tissue . Reporter genes containing 5′ intergenic and intronic sequences from D . melanogaster drove expression patterns consistent with prior studies [12] , [14]–[17]: the 5′ intergenic sequence drove expression in the epidermal cells of the abdomen , thorax and wing ( Figure 2B ) , whereas the intronic sequence drove expression in bristle-associated cells ( Figure 2C ) . We also observed faint expression in wing veins activated by the D . melanogaster intronic sequence ( Figure 2C , arrows ) – an enhancer activity that ( to the best of our knowledge ) has not previously been reported in D . melanogaster . Reporter gene expression was similarly used to infer the location of tissue- and cell-type specific enhancers in each of the other five species . Locations for enhancers that drive expression in the epidermal cells of the abdomen , thorax , wing , and head; in the wing veins; and in bristle-associated cells are summarized in the following paragraphs . For each species , enhancers driving expression in epidermal cells of the abdomen , thorax , wing , and ( when expression was present ) head were typically found in the same genomic region; however , the location of this region differed among species and half of the species showed evidence of epidermal cell enhancers in both the 5′ intergenic and intronic regions . Enhancers driving expression in epidermal cells of the abdomen , thorax , and wing were observed in the 5′ intergenic regions of all three Sophophora species ( i . e . , D . melanogaster , D . pseudoobscura , and D . willistoni ) and D . virilis from the Drosophila subgenus ( Figure 2B , 2E , 2H , 2N ) as well as in the introns of D . pseudoobscura and all three species from the Drosophila subgenus ( i . e . , D . mojavensis , D . virilis , and D . grimshawi ) ( Figure 2F , 2L , 2O , and 2R ) . In addition , the intron from D . willistoni drove expression in the epidermal cells of the thorax and wing ( Figure 2I ) , and the D . grimshawi 5′ intergenic region drove expression in a small region of epidermal cells flanking two of the wing veins ( Figure 2Q , arrows ) . Expression in head epidermal cells was observed only in D . pseudoobscura and D . virilis , with the enhancer controlling this expression located in the 5′ intergenic or intronic regions of these species , respectively ( Figure 2E and 2O ) . The genomic location of enhancers driving expression in wing veins was also variable among species . In the subgenus Sophophora , the two most closely related species , D . melanogaster and D . pseudoobscura , both showed this enhancer activity in the intron ( Figure 2C and 2F , arrows ) , whereas the more distantly related D . willistoni showed wing vein enhancer activity in the 5′ intergenic sequence ( Figure 2H , arrow ) . In the subgenus Drosophila , both 5′ intergenic and intronic sequences from D . mojavensis and D . virilis drove expression in the wing veins ( Figure 2K , 2L , 2N , and 2O , arrows ) , but no wing vein expression was observed from either reporter gene containing D . grimshawi sequence ( Figure 2Q and 2R ) . Expression in bristle-associated cells of both the body and wing was controlled by intronic sequences from all six species , making it the only yellow enhancer activity whose genomic location appears to be conserved within the genus Drosophila ( Figure 2C , 2F , 2I , 2L , 2O , and 2R ) . The spatial patterns of reporter gene expression in epidermal cells of the abdomen , thorax , and ( less frequently ) wing often differed between species ( Figure 2 ) . With few exceptions ( noted below ) , sequences from each species activated GFP expression in transgenic D . melanogaster hosts in patterns that correlated with adult pigmentation of the species from which the enhancer sequences were derived . In the abdomen , for example , D . melanogaster , D . willistoni , and D . grimshawi all have dark stripes at the posterior edge of each dorsal abdominal segment ( Figure 2A , 2G , and 2P ) and show similar stripes of reporter gene expression in each abdominal segment driven by either their 5′ intergenic or intronic sequences ( Figure 2B , 2H , and 2R ) . D . mojavensis , however , also has pigment stripes on its dorsal abdomen , but the weak abdominal reporter gene expression observed was not restricted to these stripes ( Figure 2L ) . In addition , D . mojavensis has a series of pigment spots on its head and thorax ( Figure 2J ) , and D . grimshawi has dark pigments along the dorsal midline in the abdomen and in the thorax ( Figure 2P ) , neither of which are reflected in the expression patterns of the corresponding species-specific reporter genes ( Figure 2K , 2L , 2Q , and 2R ) . Finally , D . pseudoobscura and D . virilis have an overall dark body color and faint stripes on the thorax ( Figure 2D and 2M ) , all of which are reflected in the reporter gene expression patterns for both species ( Figure 2E , 2F , 2N , and 2O ) . Partial correlations between reporter gene expression and adult pigmentation were also seen in the wing . D . virilis has a visible spot of dark pigment surrounding one of its cross-veins ( Figure 2M ) , and D . grimshawi has an elaborate pattern of pigment spots ( Figure 2P ) . The 5′ intergenic region from D . virilis drove higher levels of expression in cells that will give rise to the pigmented spot surrounding L4-L5 cross-vein than in the rest of the wing ( Figure 2N , arrowhead ) , whereas the D . grimshawi intron drove elevated expression in a subset of wing epidermal cells in a pattern that did not correlate well with adult D . grimshawi wing pigmentation ( Figure 2R ) . Interestingly , the D . pseudoobscura intron drove elevated expression in an anterior spot of the wing ( Figure 2F , arrowhead ) despite the fact that D . pseudoobscura lacks any obvious dark pigment patterns in this region . As described above , similar tissue-specific enhancer activities were found in different genomic regions among the species surveyed . Such changes in cis-regulatory architecture can be achieved through ( 1 ) the movement of existing enhancers via duplications and/or transpositions of DNA sequence or ( 2 ) the de novo construction or destruction of transcription factor binding sites individually via sequential nucleotide changes . Each of these mechanisms is expected to produce a different pattern of sequence similarity between species . For example , consider D . melanogaster , which has an enhancer driving expression in abdominal epidermal cells in its 5′ intergenic region ( Figure 2B ) , and D . pseudoobscura , which has two enhancers driving expression in abdominal epidermal cells located in its 5′ intergenic and intronic regions ( Figure 2E and 2F ) . If the intronic enhancer in D . pseudoobscura resulted from a duplication of the 5′ enhancer shared with D . melanogaster , sequence similarity is expected between the 5′ region of D . melanogaster and the intron of D . pseudoobscura as well as between the 5′ intergenic and intronic sequences of D . pseudoobscura itself . If , however , a more gradual sequence substitution process caused either the loss of abdominal epidermal cell enhancer activity in the D . melanogaster intron or the gain of this activity in the D . pseudoobscura intron , regions of sequence similarity are expected to be collinear between species . That is , the introns of both species should share greater sequence similarity with each other than either does with the other species' 5′ intergenic sequence and vice versa . To try to distinguish between these mechanisms , we performed pairwise comparisons of yellow genes and their 5′ intergenic sequences for all six species . As expected , significant sequence similarity was observed between homologous exons for all pairs of species ( Figure 3 ) . Outside of these regions , very little sequence similarity was observed for all but the most closely related pairs of species in each subgenus: D . melanogaster and D . pseudoobscura in the Sophophora subgenus , and D . mojavensis and D . virilis in the Drosophila subgenus . These two pairs of species provide the most power for investigating the molecular mechanisms responsible for interspecific differences in enhancer location . In both cases , one species in the pair has enhancer activity driving epidermal cell expression in the abdomen , thorax , and wing only in the 5′ intergenic region or only in the intron , whereas the other member of the pair has similar activities in both the 5′ intergenic region and the intron . Despite these differences in the genomic location of enhancers with similar tissue-specificity , we observed only collinear regions of sequence similarity ( Figure 3 , red and blue arrows ) . Such a pattern favors a model in which enhancers have been gained or lost through sequential sequence substitutions .
Comparative studies that examine cis-regulatory sequences in an evolutionary context can uncover features overlooked by dissecting cis-regulatory sequences from a single species . For example , studies of D . melanogaster yellow identified non-overlapping DNA sequences that are necessary and sufficient to activate expression in epidermal cells of the body ( i . e . , abdomen and thorax ) or wing , suggesting the presence of two distinct tissue-specific enhancers [12] , [15] , [16] . We found that these “wing” and “body” enhancer activities colocalize to the same genomic region in most species despite frequent evolutionary changes in the relative position of this region ( Figure 4 ) . This suggests that these enhancers are not fully independent , but rather interact in a way that constrains their evolution . For example , they might require close proximity to function properly at the native yellow locus because they share transcription factor binding sites and/or chromatin structure that promotes expression in pupal epidermal cells . Such colocalization was not observed for enhancers driving expression in bristle-associated cells or wing veins . Therefore , we propose that three evolutionarily independent enhancer modules regulate yellow expression: one controlling expression in bristle-associated cells , one controlling expression in the wing veins , and one controlling expression in the epidermal cells of the abdomen , thorax , head , and/or wing . Consistent with this proposal , a DNA fragment containing both the previously defined “body” and “wing” enhancers drives reporter gene expression in epidermal cells of the abdomen that is more representative of endogenous D . melanogaster yellow expression in those cells than that driven by a fragment containing the “body” enhancer alone [14] . Examining divergent phenotypes in concert with a phylogenetic tree allows inferences to be made about the evolutionary changes that led to the observed trait diversity . To this end , Figure 4 shows the phylogenetic relationships among the species surveyed alongside a summary of the genomic locations of yellow enhancers from each species . Enhancer activity was considered present if reporter gene expression above background levels was observed in the tissue- or cell-type indicated regardless of the precise spatial pattern within that tissue . To assess the evolutionary changes that gave rise to the observed diversity of cis-regulatory architecture , we must first infer the genomic locations of enhancers in the common ancestor of the six species studied . To do this , we considered each enhancer activity independently . The historical genomic location of bristle enhancer activity could be inferred with the most confidence: all six species showed bristle enhancer activity only in the intron , strongly suggesting that the common ancestor of these six species also had a bristle enhancer in this region . The ancestral locations of the wing vein and epidermal cell enhancers are less clear; these enhancer activities were found in the 5′ intergenic region , in the intron , and in both of these regions depending on the species surveyed . Furthermore , it is possible that there have been even more changes in cis-regulatory architecture than we were able to detect . For example , when functionally similar enhancers were observed in homologous genomic regions in different species , we made a conservative assumption that these enhancers were identical by descent . We also considered the possibility that trans-regulatory divergence might cause the activity of a heterologous enhancer to be different in D . melanogaster than it is in its native species ( e . g . , [12] ) ; however , this is unlikely to explain the extensive changes in enhancer location we observed because of the very specific combination of cis- and trans-regulatory changes required to cause a spurious enhancer relocation with our assay . Inferring the most likely genomic location ( s ) of wing vein and epidermal cell enhancers in the common ancestor requires an assumption about the relative likelihood of enhancer gain and enhancer loss in different lineages . Because mutations are expected to disrupt transcription factor binding sites more often than they are expected to create new ones , we assume that the loss of enhancer activity is more likely in all lineages than the gain of a novel tissue-specific enhancer . On the basis of this assumption , we propose that the most parsimonious explanation for the observed data is that the common ancestor had enhancers in both the 5′ intergenic and intronic regions of yellow that drove expression in the wing veins as well as in the abdomen , thorax , and wing epidermal cells . Such a scenario involves at least one loss of enhancer activity in the lineage leading to each of the species surveyed except D . virilis , as shown in Figure 4 . While we find a common ancestor with redundant enhancers in the 5′ intergenic and intronic regions for both the wing veins and epidermal cells surprising , enhancers with overlapping tissue- and cell-type specific activities have been identified for other genes ( e . g . , [21]–[27] ) . For example , some genes are regulated by both primary and “shadow” enhancers that drive expression in the same cells [24]; the relative strength of these two enhancers may change over time . Scenarios involving a common ancestor with wing vein and/or epidermal cell enhancer activity in only one genomic region include multiple gains and losses in most lineages , which is presumably even less likely . Regardless of the specific gains , losses , and/or relocations of yellow enhancers that occurred over the last 40 million years , it is clear that the genomic location of enhancer activities within and surrounding the yellow gene has changed multiple times . This finding is contrary to recent studies of seven genes expressed during embryogenesis that all have conserved genomic locations of enhancers between Drosophila and species that diverged over 100 million years ago [5] , [7] . One way in which yellow differs from these genes is that its expression is much more divergent among species . This is presumably because yellow expression is required for pigmentation and pigmentation is a rapidly evolving trait among Drosophila species [28] . ( See Text S1 for a discussion of how the observed changes in yellow enhancer activity relate to species-specific pigment patterns . ) Evolutionary processes resulting in divergent yellow expression might have allowed – or even facilitated – changes in the genomic location of its enhancers . For example , if changes in pigmentation are adaptive ( or at least not maladaptive ) mutations both inside and outside of existing yellow enhancers that affect its expression may not be eliminated by purifying selection , causing the gradual reorganization of enhancer architecture . cis-regulatory regions controlling conserved expression patterns , on the other hand , are more likely to have been subject to strong purifying selection , with new mutations that change enhancer activity and/or position selected against . Consistent with this proposal , we found that conserved expression of yellow in bristle-associated cells was controlled by an enhancer with a conserved genomic location , whereas divergent yellow expression in epidermal cells was controlled by enhancers with divergent locations ( Figure 4 ) . Divergent expression patterns are not a prerequisite for changing the location of cis-regulatory elements , however: the location of a twist enhancer with conserved activity has diverged between D . melanogaster and D . virilis [29] , and changes in the genomic location of Polycomb/Trithorax response elements have also been observed between Drosophila species [30] . The prevalence of changes in enhancer position among species remains unknown . Many studies of cis-regulatory evolution have relied heavily on physical homology and sequence conservation to identify functionally homologous enhancers among species [31] , creating an ascertainment bias that contributes to the prevailing view that enhancer position is usually conserved among species . Only once additional unbiased searches for enhancers using in vivo functional tests are performed will it be possible to determine whether nomadic enhancers are the exception or the norm .
For five of the six species used in this study ( D . pseudoobscura , D . willistoni , D . mojavensis , D . virilis , and D . grimshawi ) , BAC libraries ( CHORI-222 , DW_Ba , DM_CBa , DV_VBa and DG_Ba , respectively ) were screened for clones containing yellow as well as its flanking genes . Nylon filters containing arrayed clones from the BAC libraries were obtained from BACPAC Resources ( CHORI-222 ) and Arizona Genomics Institute ( AGI ) ( DW_Ba , DM_CBa , DV_VBa and DG_Ba ) , and screened with [alpha-32-P]-labeled , random hexamer-primed probes synthesized using PCR amplicons from exons of the yellow gene; the CG3777 gene , which is located 5′ of yellow; and either the CG4165 ( D . mojavensis ) or achete ( all other species ) gene , both of which are located 3′ of yellow . ( Primers and PCR conditions used to amplify the DNA template for each probe are available upon request . ) Probe synthesis was performed as described in Molecular Cloning [32] . Unincorporated radionucleotides were removed using CentriSpin columns ( Princeton Separations ) . Purified radioactive probes were denatured at 100°C for 5 minutes and placed on ice until they were added to the hybridization buffer containing the appropriate species specific BAC filter . BAC filter screening conditions and buffer recipes were as described in the AGI BAC Filter Manual available from the Arizona Genomics Institute ( http://www2 . genome . arizona . edu/research/protocols_bacmanual ) . After hybridizing each filter with a radioactive probe , the filter was washed and exposed to Kodak BioMax XAR films for 72 hours @ −80°C and developed . Radiographs were used to identify clones as directed by the filter manufacturers ( Arizona Genomics Institute and BACPAC Resources ) , and BACs that hybridized to all three probes were ordered . Upon receipt , each BAC clone was tested for the presence of CG3777 , yellow , and achete or CG4165 using PCR amplification . Table S1 lists all BAC clones found to contain yellow and at least one flanking gene . For D . willistoni , D . mojavensis , D . virilis , and D . grimshawi , BAC clones with code numbers 10L5 , 4J24 , 1A7 and 23K7 , respectively , were used for reporter gene construction . For D . melanogaster , the RP98-13J2 BAC clone from the Roswell Park Cancer Institute Drosophila BAC Library , which was identified computationally and confirmed by PCR to contain CG3777 , yellow and achete , was used for reporter gene construction . Note that none of the D . pseudoobscura BAC clones containing yellow had sufficient 5′ sequence to be used for reporter gene construction . For each species , 5′ intergenic and intronic regions of yellow were cloned into a plasmid containing piggyBac transposable element arms , a 3xP3-Enhanced Green Fluorescent Protein ( EGFP ) marker driving cytoplasmic GFP expression in the eyes [33] , and a 300 bp attB site [20] , [34] that we amplified from the pTA-attB plasmid provided by Michele Calos ( Stanford University ) and inserted into the unique XbaI site . As described in the main text , the 5′ end of the 5′ intergenic sequences was defined by the highly conserved region shown in Figure S1 . The 5′ intergenic and intronic sequences from D . melanogaster , D . subobscura , D . pseudoobcsura , and the intron of D . virilis yellow were PCR amplified from BAC RP98-13J2 , plasmid ysub-pBac [12] , genomic DNA extracted from D . pseudoobscura ( UCSD stock number 14011-0121 . 94 ) , and plasmid yvir-pBac [10] , respectively . Primer sequences used for these amplifications are available upon request . PCR products were ligated to the PCR 2 . 1 TOPO vector ( Invitrogen ) , fully sequenced to identify clones with no PCR introduced mutations , and subcloned into the piggyBac-EGFP vector described above using the unique AscI restriction site . For D willistoni , D . mojavensis , and D . grimshawi , both the 5′ intergenic and intronic regions , and for D . virilis , only the 5′ integenic region , were cloned into the piggyBac-EGFP vector using recombineering ( http://recombineering . ncifcrf . gov/ ) . Briefly , PCR was used to amplify 450–500 bp homology arms corresponding to the 5′ ( left arm ) and the 3′ ( right arm ) end of each target DNA sequence . PCR sewing was used to combine the left and right arms into a single fragment with a unique NheI restriction site between them . These DNA fragments were subcloned into PCR 2 . 1 TOPO , fully sequenced to identify clones without PCR introduced mutations , and subcloned into the piggyBac-EGFP vector using the unique AscI restriction site . Each piggyBac vector containing a species-specific pair of homology arms was linearized using the introduced NheI restriction site and electroporated into SW102 cells containing the yellow BAC from the appropriate species . Electroporation was conducted using Eppendorf Electroporator 2510 at 1250 Volts , with time constants ranging between 4 . 5–5 . Following electroporation , SW102 cells were incubated in 1 ml LB at 30°C rotator for 1–1 . 5 hours , spread on LB agar plates supplemented with ampicillin ( 50 ug/ml ) , and grown overnight at 30°C to select for cells containing a circularized piggyBac-EGFP plasmid harboring the DNA of interest . Primers located in the piggyBac vector and in the target DNA sequences were paired to screen colonies for the existence and the direction of the DNA region of interest using PCR . Positive clones were confirmed by diagnostic digests using restriction enzymes specifically chosen for each construct , and the inserted DNA was completely sequenced to confirm once again that no experimentally introduced mutations were present . Next , a DNA fragment derived from pSLfa1180fa-nEGFP ( Ernst Wimmer , Georg August University , Göttingen ) containing an hsp70 promoter and the coding sequence for a nuclear EGFP protein was cloned into each piggyBac plasmid using the unique FseI restriction site . The resulting DNA transgene constructs were confirmed using appropriate diagnostic digests with restriction enzymes and sent to Genetics Services , Inc . ( Cambridge , MA ) where they were injected into the w−; attP-40 line of D . melanogaster [35] . This line contains a transgene expressing the φC31 site-specific integrase enzyme [34] , which causes the targeted integration of each attB-containing piggyBac construct into the attP site on the D . melanogaster 2nd chromosome . An “empty” piggyBac plasmid lacking any yellow sequence was also transformed into D . melanogaster and analyzed as a control to determine background levels of GFP expression . Homozygous transgenic D . melanogaster lines were obtained by crossing each transgenic D . melanogaster genotype to a 2nd chromosome balancer line ( w[*]; Kr[If-1]/CyO; D[1]/TM6B , Tb[+]; Bloomington stock number 7197 ) , intercrossing the F1 offspring , and then intercrossing selected homozygous F2 individuals . Homozygous transgenic animals were imaged at 70–80 hours APF , a stage which is recognized by pigmented wings as well as the presence of visible malpigian tubes on the anterior sides of the abdomen . The pupal case was removed prior to imaging using a probe and a pair of fine forceps . To prepare the pupal bodies for confocal microscopy , the transparent pupal cuticle was kept in place without any tears and the pupa was mounted on a microscope slide with a drop of water and a coverslip . To prepare the pupal wings for confocal microscopy , the transparent pupal cuticle was removed and the whole fly was submerged in Milli-Q water . After the wings had unfolded , they were carefully detached from the rest of the pupa at the base of the wing where it connects to the thorax . Using a wide mouth pipette tip , each wing was transferred onto a microscope slide with a drop of water . A coverslip was applied and pressed gently to achieve full expansion of the wings . All specimens were imaged immediately after mounting using a Leica SP5 confocal microscope . Identical settings ( e . g . , laser power , pinhole size , etc ) were used on the confocal microscope for all samples , and all raw confocal images of the same tissue ( e . g . , wings or bodies ) were processed identically in Adobe Photoshop CS3 . Results from the analysis of reporter genes containing 5′ intergenic and intronic sequences from D . subobscura are presented and discussed only in Figure S2 and its associated legend because the 5′ intergenic region surveyed in D . subobscura did not extend to the highly-conserved region used for all other species . yellow sequences and 5′ intergenic DNA from all species except D . willistoni were downloaded using the UCSC Genome Browser [36] . Specific assemblies and coordinates for each species were as follows: D . melanogaster , Apr . 2006 ( BDGP R5/dm3 ) Assembly , chrX:246 , 727-255 , 037; D . pseudoobscura , FlyBase release r2 . 11 , chrXL_group1e:4227884-4238281; D . willistoni , FlyBase release r1 . 3 scf2_1100000004909:5315142-5325379; D . mojavensis , Aug . 2005 ( Agencourt prelim/droMoj2 ) Assembly , scaffold_6359:2 , 460 , 150-2 , 478 , 221; D . virilis , Aug 2005 ( Agencourt prelim/droVir2 ) Assembly , scaffold_13042:3 , 903 , 783-3 , 920 , 981; D . grimshawi , Aug 2005 ( Agencourt prelim/droGri1 ) Assembly , scaffold_24821:2 , 532 , 826-2 , 547 , 390 . Homologous D . willistoni sequences were identified and downloaded using the BLAST implementation on FlyBase . These sequences were subject to repeat masking prior to analysis . Alignments were performed using LASTZ ( Release 1 . 02 . 00 , built January 12 , 2010 ) , which was downloaded from Webb Miller's laboratory website ( http://www . bx . psu . edu/ ) . This unpublished software replaces the BLASTZ program developed by the same group [37] . Default settings were used except for the ” --mismatch = 2 , 23” option that sets an alternative threshold for the gap-free extension step . The basic structure of this analysis is as follows: all sequences 19 nucleotides long with matches in 12 specific positions were identified as “seeds”; seeds were extended in both directions without gaps until two mismatches were found in each end; extended seeds at least 23 nucleotides long were treated as “high scoring segment pairs” ( HSPs ) ; HSPs were converted into anchor points; anchor points were extended in both directions using gapped local alignments; and the coordinates of local alignments output by LASTZ were plotted using R statistical software [37] . The decision to allow a maximum of two mismatches during the gap-free extension stage was arbitrary , whereas the minimum length of extended seeds treated as HSPs ( i . e . , 23 nucleotides ) was determined empirically by randomizing concatenated multi-species yellow sequences with the “Shuffle DNA” tool in the web-based “Sequence Manipulation Suite” [38] and iteratively testing length thresholds to find the smallest value that failed to identify any stretches of significant sequence similarity in the randomized sequence . Figure S3 shows the result of the same analysis with a decreased length threshold ( ”--mismatch-2 , 19” ) ; 40 regions of significant sequence similarity were identified between the real and randomized sequences using these parameters .
|
In order for a gene to be active , it must be turned on , or “expressed . ” Instructions determining when , where , and how much a gene will be expressed are encoded by DNA sequences known as enhancers . The precise DNA sequence of a particular enhancer changes over evolutionary time , which may or may not change its effects on gene expression . Many genes are controlled by multiple enhancers and prior work has shown that the location of these enhancers within the genome tends to remain stable for long periods of evolutionary time . Here , we examine the enhancers controlling expression of a gene ( yellow ) involved in generating pigmentation diversity among fruit fly ( Drosophila ) species . Surprisingly , we find that not only have the sequence and function of individual enhancers changed among Drosophila species , but so has the location of these enhancers within the genome of each species . This finding is important because it demonstrates a type of evolutionary change affecting DNA sequence elements critical for gene expression that is currently under appreciated and should be considered when searching for enhancers in related species .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"evolutionary",
"biology/developmental",
"molecular",
"mechanisms",
"genetics",
"and",
"genomics/gene",
"expression",
"evolutionary",
"biology/evolutionary",
"and",
"comparative",
"genetics"
] |
2010
|
Nomadic Enhancers: Tissue-Specific cis-Regulatory Elements of yellow Have Divergent Genomic Positions among Drosophila Species
|
PhoQ/PhoP is a central two-component system involved in magnesium homeostasis , pathogenicity , cell envelope composition , and acid resistance in several bacterial species . The small RNA GcvB is identified here as a novel direct regulator of the synthesis of PhoQ/PhoP in Escherichia coli , and this control relies on a novel pairing region of GcvB . After MicA , this is the second Hfq-dependent small RNA that represses expression of the phoPQ operon . Both MicA and GcvB bind phoPQ mRNA in vivo and in vitro around the translation initiation region of phoP . Binding of either small RNA is sufficient to inhibit ribosome binding and induce mRNA degradation . Surprisingly , however , MicA and GcvB have different effects on the levels of the PhoP protein and therefore on the expression of the PhoP regulon . These results highlight the complex connections between small RNAs and transcriptional regulation networks in bacteria .
Gene regulation in response to environmental conditions is a key feature of bacterial cells , that allows their development in multiple and diverse niches . While this was originally thought to rely only on transcriptional control by proteins , it is now well established that mechanisms underlying the control of gene expression are much more diverse . For instance , numerous examples of post-transcriptional control have been reported that can be mediated by proteins , riboswitches or trans-acting small RNAs ( sRNAs ) [1] . Even though the first example of a chromosomally-encoded bacterial sRNA regulating the expression of a target-gene encoded at a different locus was described almost 30 years ago [2] , it is only in the last decade that our understanding of the number and the role of sRNAs in bacterial physiology has greatly improved [3] . Among those , the Hfq-dependent sRNAs have been extensively studied . This class of sRNAs imperfectly pairs to target-mRNA ( s ) , which in most cases occludes the ribosome binding site ( RBS ) of the target-gene and therefore down-regulates its expression through translational inhibition . This is often accompanied by degradation of the target-mRNA , either as a consequence of the translation inhibition and/or independently of this process [4] . It is also known that sRNAs can activate gene expression , again by increasing translation and/or stability of their target ( s ) through base-pairing interactions [5]–[8] . Hfq both prevents the sRNAs from being degraded , and facilitates and stabilizes sRNA-mRNA duplexes . As a result , a productive interaction between an Hfq-dependent sRNA and its target relies only on short and imperfect duplexes . Most , if not all , Hfq-binding sRNAs have multiple targets and , in parallel , a single target can be regulated by multiple sRNAs . This , in addition to the great number of sRNAs in bacterial species ( >80 in E . coli for instance ) , contributes to the importance of these molecules in bacterial physiology . One of the best examples is probably the Hfq-dependent GcvB sRNA that has been shown to target more than 20 different mRNAs , most of them probably directly . Its transcription is activated by the product of the adjacent gene , GcvA , a regulator that also controls the gcvTHP operon as well as its own transcription [9] . Whereas GcvA negatively autoregulates its own synthesis , it can either activate or repress expression of gcvTHP , the glycine cleavage operon whose products catalyze the oxidation of glycine into carbon dioxide , ammonia and a one carbon-unit that will be transferred to tetrahydrofolate . Whether GcvA activates or represses gcvTHP operon expression depends on the presence of the GcvR protein and/or glycine . The GcvA/GcvR complex acts as a repressor; but in the presence of glycine , it is disrupted , allowing GcvA to activate the synthesis of the glycine cleavage system . In contrast , purines seem to promote repression . Similarly , gcvB transcription requires GcvA and is repressed by GcvR unless glycine is present . In addition , the Lrp global regulator has a positive effect on gcvTHP expression [10] , but represses gcvB expression [9] , [11] . As a result of this control by GcvA , GcvB is mostly present in fast-growing cells in rich medium [12] . It negatively controls expression of multiple targets involved in aminoacid transport and metabolism [9] , [12]–[14] . As is often the case for sRNAs with several targets , a unique region of the sRNA , referred to as R1 for GcvB , pairs with almost all targets . This region is very well conserved , single-stranded and GU-rich [12] . So far , only 3 targets have been found to be regulated by GcvB independently of its R1 region: lrp , gdhA and cycA [13] . Also highlighting the importance of sRNAs is the fact that several of them target transcriptional regulators . This is true for instance for the master regulator of stationary phase RpoS , whose expression is post-transcriptionally controlled by at least 4 distinct sRNAs [8] , [15]–[17] . Several two-component systems ( TCS ) , such as EnvZ/OmpR or DpiA/DpiB , have also been shown to be repressed by sRNAs [18] , [19] . Similarly , we have shown in a previous work that MicA , an RpoE-dependent sRNA known to repress the synthesis of multiple proteins [20] , many of which are located in the outer membrane [21]–[23] , was a direct regulator of PhoQ/PhoP synthesis [24] . This TCS is a central regulatory system in which the PhoQ sensor protein controls the phosphorylation status of the cognate response regulator PhoP , so that it is activated ( i . e . phosphorylated ) upon low magnesium conditions or in presence of antimicrobial peptides . Under such conditions , PhoP directly regulates dozens of genes involved in major cellular functions such as magnesium homeostasis , bacterial virulence , cell envelope composition and acid resistance [25] . Our previous findings linked therefore the expression of phoPQ operon to cell envelope stress through the regulatory sRNA MicA . In addition , they strongly suggested the existence of at least another Hfq-dependent sRNA controlling expression of phoPQ at the post-transcriptional level . In this study , we identify GcvB as such an sRNA , and address the mechanism as well as the physiological consequences of this control on the expression of the PhoP regulon .
Even though MicA is an Hfq-dependent sRNA , expression of phoP was found to be strongly activated at the post-transcriptional level by the deletion of hfq in both wt and micA deleted cells [24] . We thus hypothesized that one or several Hfq-dependent regulator ( s ) could affect phoP expression independently of MicA . Therefore , we transformed a strain carrying a PBAD-phoP-lacZ reporter fusion with a plasmid library overexpressing most of the known E . coli Hfq-dependent sRNAs from an IPTG-inducible modified Plac promoter [8] . Transcription of the phoP-lacZ fusion is driven by the PBAD promoter so that expression of this fusion should not be sensitive to control of phoP at the transcription initiation level . The transcription start site is expected to be identical to that of the proximal phoP promoter , P1 , which is normally positively regulated by PhoP in E . coli [26] . The fusion encompasses only 66 nts of phoP mRNA , that correspond to a 36 nts 5′ leader followed by the first 30 nts of the ORF . The ß-galactosidase activity of the different transformants was assayed and the results are shown in Figure 1A . Of the 25 sRNAs tested , 4 modulated the expression of the fusion by more than 2-fold , SgrS and RydC positively and MicA and GcvB negatively . Since SgrS is involved in sugar metabolism [27] , we suspected that its overproduction could affect expression from the arabinose-induced PBAD promoter . To test this possibility , we measured the SgrS-mediated repression of the same phoP-lacZ fusion when constitutively expressed from the Ptet promoter instead of PBAD . Since the pSgrS plasmid had no effect on this Ptet-phoP-lacZ fusion ( Figure 1B ) , it is likely that it activated the PBAD- driven fusion at the promoter level and this was not investigated further . The RydC sRNA has been shown to activate ( repress ) the expression of fusions that are negatively ( positively ) regulated by Hfq , most likely by titrating Hfq [28] . One possibility is therefore that it acts on PBAD-phoP-lacZ in the same way , but further experiments are required for a definitive proof . The same may be true for sRNAs such as ChiX , that also activates the expression of the fusion almost 2-fold . In the experiment shown in Figure 1A , pMicA repressed the expression of phoP-lacZ by 3 . 1-fold , which is in agreement with our previous results . Furthermore , this experiment also identified GcvB as a multicopy repressor of phoP-lacZ , since pGcvB was responsible for a 4 . 5-fold decrease in the ß-galactosidase activity of the fusion . This last result was confirmed using the Ptet-phoP-lacZ fusion ( repression of 3 . 5-fold , Figure 1B ) , indicating that , as shown previously for MicA [24] , GcvB most likely acts at the post-transcriptional level . Importantly , this repressor effect of GcvB was also visible when GcvB was expressed from the chromosome; a deletion of the gcvB gene was sufficient to increase expression of phoP-lacZ by 1 . 9-fold ( Figure 1C ) . One possible explanation for these results is that GcvB regulates the phoP-lacZ fusion by controlling the synthesis and/or activity of a post-transcriptional regulator of phoP . Since MicA is so far the only post-transcriptional regulator of phoP known to affect our phoP-lacZ fusion , we analyzed the effect of GcvB on phoP expression in the absence of MicA . In this context , overproduction of MicA and GcvB from a plasmid caused a 3 . 4- and 4 . 3-fold decrease respectively in the activity of the phoP-lacZ fusion ( Figure 2A ) , which is similar to what was observed in micA+ cells . Consistent with this observation , deletion of gcvB resulted in a 1 . 7- or 2 . 3-fold activation of phoP-lacZ in wt or micA− cells respectively ( Figure 2B ) . Therefore , GcvB acts independently of MicA to regulate phoP expression . In this experiment , deletion of micA has no significant effect on the expression of phoP , because transcription of MicA is dependent on the RpoE sigma factor , which is not activated under the experimental conditions of Figure 2B . We had previously constructed a mutant form of phoP-lacZ ( phoPmut-lacZ , where the 4 nts directly downstream of the AUG start codon are changed from CGCG to GCGC , Figure 3A ) such that this fusion is no longer controlled by MicA [24] . Interestingly , this mutant fusion is still controlled by GcvB , since its expression is up-regulated by 2 . 2-fold in a ΔgcvB strain ( Figure 2B , two last bars ) . This result suggests that the precise regions of the phoP mRNA that are required for MicA or GcvB action are different . Thus , GcvB acts on phoP , independently and apparently at a different site from that of MicA . GcvB is a pleiotropic regulator , whose expression is highest in exponentially growing cells in rich medium as a result of its control by the GcvA transcriptional regulator . GcvB directly regulates more than 20 genes , the large majority of which are targeted via a very well conserved single stranded G/U rich region of GcvB , referred to as R1 ( Figure 3A ) . Even though this might be partially due to an experimental bias given that the R1 region was used as a “bait” in a bioinformatic search for targets , this region is clearly required for the control of almost all targets identified so far [13] . Since sRNAs often regulate multiple targets via a single conserved region [18] , [29] , [30] , we reasoned that the R1 region was also likely to be involved in the control of phoP , regardless of whether phoP was a direct or indirect target . We therefore measured the expression of the PBAD-phoP-lacZ fusion in the presence of a plasmid overexpressing either GcvB wt or a GcvB mutant in the middle of the R1 region ( GcvBmutR1 , see Figure 3B ) . In this experiment , the chromosomal copy of gcvB is deleted and the steady-state level of GcvBmutR1 was slightly lower than that of GcvB wt . Somewhat surprisingly , these two forms of GcvB repressed the expression of phoP-lacZ to a similar extent ( Figure 3C , left panel ) ; in contrast , a previously identified target of GcvB R1 region , livJ , was , as expected , less regulated by GcvBmutR1 than by GcvB wt ( Figure 3D ) . While this does not completely rule out a possible role for the R1 region in the control of phoP ( for instance , if pairing involves nts of R1 that are not affected by the mutR1 change or if alternative pairing ( s ) can take place with this mutant ) , this suggests that the role of R1 in phoP control is not as crucial as for the other targets of GcvB . Because GcvB action on phoP was independent of MicA ( see above ) , we next envisioned the possibility that it could directly pair with phoP to control its expression . If this interaction exists , we expected it to involve a region of GcvB outside of the R1 . The TargetRNA program [31] was used to predict a potential pairing between GcvB and the phoP mRNA fragment encompassing nts −36 to +30 relative to the AUG ( i . e . the region of phoP that is present on the phoP-lacZ fusion ) . According to this prediction ( Figure 3B ) , the region between nts 148 and 174 of GcvB can imperfectly pair with phoPQ mRNA in the translation initiation region ( TIR ) , which is the most frequent binding site for negatively acting sRNAs . Interestingly , this corresponds to a region of GcvB that was shown to be mostly single-stranded in solution [12] and is now referred to as region R3 . The relevance of this putative direct interaction was tested in vivo by compensatory changes . While the PBAD-phoP-lacZ fusion was repressed by more than 4-fold upon overproduction of GcvB wt , this was not the case with the GcvBmutR3 variant , where nts 154 to 158 were changed from CUGUC to GACAG . Rather , expression of the fusion was increased by more than 2-fold ( Figure 3C ) . The inability of GcvBmutR3 to repress phoP-lacZ expression is not due to an intrinsic instability , since it accumulates to a level similar to that of wt GcvB ( Figure 3C ) . A possible explanation for the fact that GcvBmutR3 activates phoP-lacZ is that it could titrate Hfq when overexpressed , leading to changes in expression of Hfq-regulated genes , such as phoP ( see [28] , [32] for examples of competition for Hfq ) . In contrast , GcvBmutR3 , but not wt GcvB , caused an 8-fold decrease in the activity of the compensatory mutant fusion ( Figure 3C , right panel ) , clearly showing that GcvB and phoP mRNA directly interact in vivo . It is also worth noting that MicA efficiently repressed both the wt and the mutant fusion , which confirms that GcvB and MicA pair at different loci of phoPQ mRNA . In addition , when mutations in the R1 and R3 regions of GcvB were combined , the resulting GcvBmutR1R3 repressed the expression of phoPmutR3-lacZ , but not that of phoP-lacZ ( Figure S1A ) . This again indicates that , at least when GcvB is overexpressed , its R1 region is not involved in the control of phoP . To provide experimental support to the proposed phoP-MicA and phoP-GcvB base-pairing interactions ( Figure 3B ) , a structural probing analysis of phoP mRNA alone or in the presence of either sRNA was performed in vitro using chemical probes ( Figure 4 , A and B ) . DMS ( dimethyl sulfate ) , CMCT ( 1-cyclohexyl-3- ( 2-morpholinoethyl ) carbodiimide metho-p-toluene sulfonate ) and kethoxal ( 1-1-dihydroxy-3-ethoxy-2-butanone ) respectively modify unpaired adenosine ( and to a much lesser extent cytidine ) , uridine and guanosine residues . According to our probing data , the secondary structure of the 5′ region of phoP mRNA appears as a long irregular stem-loop which is hold by seven double-stranded elements named H1 to H7 , separated by bulges or loops ( Figure 4C ) . Upon addition of MicA , most nts from positions −13 to +11 of phoP mRNA , which include the nts forming the 5′ strands of H4 , H5 and H6 , display either a decreased reactivity towards the probes or correspond to RT-stops or -pauses which occur in a region rich in GC pairs ( Figure 4 , A and D ) . This model is also consistent with the fact that many nts located between positions +27 to +45 of phoP mRNA , which include all the nts forming the 3′ strands of H4 , H5 and H6 in the absence of MicA , become more reactive in the presence of MicA ( Figure 4 , A and D ) . In conclusion , the interaction between phoP mRNA and MicA relies on ( i ) the disruption of at least three of these elements , namely H4 , H5 and H6 , and ( ii ) the formation of an extended base-pairing interaction between nts −15 to +11 of phoP mRNA and nts 4 to 31 of MicA , whereby both the Shine-Dalgarno ( SD ) sequence and the phoP translation start codon are base-paired ( Figure 4D ) . Upon addition of MicA , further reactivity enhancements in phoP mRNA nts are observed outside of the proposed phoP mRNA-MicA duplex ( see nts +53 and +54 in H1 , −21 and +46 to +49 in H3 , −16 which joins H3 to the duplex , +14 to +21 in H7 and its apical loop , Figure 4D ) . It is likely that these changes are due to either local breathing or even disruption of H1 , H3 and H7 , which are destabilized by the binding of MicA . Also , decreased reactivities are observed ( see nts +12 which joins the duplex to H7 and +23 in H7 , Figure 4D ) for which we have no explanation . In contrast , the duplex formed by phoP mRNA and GcvB seems shorter as it requires only the disruption of H3 and H4 to form; it is centered around and blocks the SD sequence , which is in complete agreement with the in vivo data . Indeed , nts displaying decreased reactivities towards the probes or corresponding to a region where GcvB-induced RT-stops or -pauses occur are clustered between nts −17 and −11 of phoP mRNA ( Figure 4B and 4E ) . However , the duplex is likely to be subject to breathing as a certain number of nts located on both side of the cluster become more reactive in the presence of GcvB ( see nts −21 to −19 , −10 and −9 , Figure 4E ) . Additional reactivity enhancements have been mapped outside of the proposed duplex ( see nts −5 , +2 , +3 , +5 and +31 to +36 in H5 and H6 and in the bubble located in between , Figure 4E ) , which are probably due to breathing of H5 which results from its destabilization by the binding of GcvB . Other regions of phoP mRNA located outside of the predicted duplex are subject to increase or decrease in reactivity in the presence of GcvB ( see nts located in H6 and H7 and in the bubble in between , Figure 4E ) . They can be due to some rearrangement of the overall structure of phoP mRNA upon GcvB-binding and/or to a supplementary interaction between phoP mRNA and GcvB . For instance , nts +9 to +17 of phoP mRNA , several of which appear protected upon GcvB-binding , could theoretically pair with nts 89 to 97 of GcvB . While our in vivo data show that this putative supplementary interaction is not sufficient for control , it could nevertheless play a role in stabilizing the phoP mRNA-GcvB duplex or in increasing the kinetics of association . At this stage , its existence and importance remains to be experimentally addressed . Finally , the reactivities of nts +37 to +50 , which form the 3′ strands of H3 and H4 in the absence of GcvB , could not be assessed with confidence because of the presence of several RT-stops or –pauses which are also present when phoP mRNA alone is reverse-transcribed in the absence of the probe ( data not shown ) . Target-mRNAs of negatively acting Hfq-dependent sRNAs are frequently degraded upon sRNA production . Therefore , to confirm the results obtained above by gene fusion , the levels of phoPQ mRNA were analyzed by Northern-Blot upon overexpression of MicA , GcvB or their mutant derivatives , using a chromosomal PBAD-phoPQ construct ( Figure 5A ) . In this experiment , transcription of the phoPQ operon is again expected to start 36 nts upstream of the phoP start codon and has been put under the control of the PBAD promoter for two reasons: ( i ) to focus only on promoter-independent regulation and ( ii ) because of the low abundance of the phoPQ mRNA when expressed from its own promoters under the experimental conditions used here . With this construct , several specific bands are visible . The upper band migrates below a 3 kb RNA marker and most likely corresponds to the whole phoPQ mRNA , while the bands of lower molecular weight could result from either alternative transcription or processing events ( Figure 5A ) . MicA , GcvB and GcvBmutR1 induce a decrease in phoPQ mRNA levels , but not MicAmut and GcvBmutR3 , that have lost the ability to control phoP expression . This is in complete agreement with the results obtained with the phoP-lacZ fusions . Pairing of MicA and GcvB to the phoPQ mRNA is therefore likely to induce a degradation of this mRNA . The effect of MicA or GcvB on the steady-state levels of the PhoP protein was then investigated by Western-Blot analysis , in a strain where phoPQ is expressed from its own promoter . As expected , MicA overexpression resulted in a strong decrease in the amount of PhoP ( Figure 5B ) , while overexpression of MicAmut had no noticeable effect . When GcvB was overexpressed , PhoP levels were decreased , albeit to a much lesser extent than upon MicA overproduction . This is rather surprising since pMicA and pGcvB had a similar effect on the expression of phoP-lacZ ( Figure 1 , Figure 2 , and Figure 3 ) and on the levels of phoPQ mRNA ( Figure 5A ) . Interestingly , pGcvBmutR1 , whose effect was also similar to that of pMicA and pGcvB in the previous experiments , is more efficient than pGcvB in down-regulating the levels of PhoP protein . Finally , GcvBmutR3 overproduction had no effect on the levels of PhoP , which is consistent with its inability to repress phoP expression ( Figure 5B ) . Therefore , control of phoP by GcvBmutR1 or MicA results , as expected , in a clear decrease of the PhoP protein levels . Surprisingly however , this decrease is only modest with wt GcvB , most likely because its R1 region has pleiotropic effects in the cell under the conditions used here , as discussed below . We then tested whether MicA and GcvB can control the expression of the PhoP regulon by repressing PhoQ-PhoP synthesis . For this purpose , the expression of 4 genes whose transcription is directly activated by PhoP [25] was analyzed under conditions where either MicA or GcvB was overproduced ( Figure 6 ) . These 4 genes are ompT , mgtA , yneM and mgrR , that encode an outer membrane protease , a magnesium transporter , a protein of unknown function located in the outer membrane and a sRNA regulator of LPS modification respectively . As expected , MicA induced a >2 . 5-fold decrease in expression of these 4 targets as analyzed by either translational fusions ( for ompT , mgtA ) or by transcriptional fusions ( for mgrR and yneM ) to ( Figure 6A ) . This decrease is most likely due to phoP regulation , since MicAmut , that does not regulate phoP , does not affect expression of these target genes . Similarly , the overproduction of GcvBmutR1 , that also represses phoP , led to a ∼2-fold decrease in the expression of the 4 fusions , while overexpression of GcvBmutR3 does not , in agreement with its inability to control phoP . Finally , when the same experiment was carried out in the GcvB overproducing strain , no decrease was observed in the expression of the 4 members of the PhoP regulon that were tested . Instead , activity of mgrR- and ompT-lacZ was unchanged , while activity of mgtA- and yneM-lacZ was increased by 1 . 6 and 2 . 1-fold respectively ( Figure 6A ) . These results obtained by gene fusion were confirmed when the levels of ompT or MgrR RNAs were analyzed by Northern-Blot ( Figure 6B ) . Indeed , MicA and GcvBmutR1 induced a decrease in the level of both RNAs , whereas MicAmut , GcvB wt and GcvBmutR3 did not . Therefore , MicA and GcvBmutR1 repress the PhoP regulon , by controlling expression of phoP . However , wt GcvB does not , which is consistent with the only modest decrease observed in PhoP levels upon its overproduction . In most cases , negatively acting sRNAs base-pair with their target-mRNAs in the TIR and occlude the RBS , thereby preventing ribosome binding and translation initiation . This is frequently accompanied by a degradation of the target-mRNA , possibly as a consequence of translational block , or in a process that is directly induced by the sRNA pairing to the target-mRNA . Since MicA and GcvB both pair to phoPQ mRNA in the TIR of the first cistron and decrease phoPQ mRNA levels , toeprinting experiments were performed in order to determine whether they also inhibited ribosome binding . In these experiments , addition of 30S ribosomal subunit and initiator tRNA to a ∼200-nt phoP mRNA fragment transcribed in vitro induced an arrest of reverse-transcription , that was visible on a sequencing gel as a band at position +16 , a classical toeprint position ( Figure 7 , A and B , lanes 3 and 2 respectively ) . When increasing concentrations of MicA were incubated with phoP mRNA prior to the addition of 30S and fMet-tRNA , the intensity of this band progressively decreased ( Figure 7A , lanes 4–6 ) . This was not observed when equal amounts of MicAmut were added instead ( lanes 8–10 ) , suggesting that it is the pairing of MicA to phoP TIR that inhibits ribosome binding . Similar results were observed with GcvB , whose addition inhibited the appearance of the +16 toeprint , even more efficiently than MicA ( Figure 7B , lanes 7–8 ) . Again , this is most likely due to the pairing between GcvB and phoP since GcvBmutR1 , that should still pair with phoP , also inhibited the toeprint , while GcvBmutR3 and GcvBmutR1R3 , that should not bind to phoP , inhibited the toeprint much less efficiently ( Figure 7B , lanes 11–12 , and Figure S1B ) . It is interesting that GcvB is much more efficient than MicA in inhibiting toeprint , and that GcvBmutR3 still inhibits toeprint to some extent . This might be related to the existence of a bipartite interaction between phoP and GcvB ( see above ) . Furthermore , as already observed in probing experiments ( Figure 4 ) , addition of MicA , but not MicAmut , to phoP mRNA in the absence of 30S subunits also induced stops or pauses of reverse-transcription , as indicated by the bands at positions +6 to +8 ( Figure 7A , lanes 2 , 4 , 5 and 6 ) . This corresponds to the 3′ end of the duplex between MicA and phoP ( Figures 3A , 4D , [24] ) , indicating that this duplex is stable enough to induce pauses in reverse-transcription . In contrast , while GcvB pairs with phoP in vitro , given the results of the probing experiments and the toeprint inhibition , its binding does not induce pauses or stops of the reverse transcription that are sufficiently strong to be observed in this experiment . This is in contrast to what was observed in Figure 4 and this discrepancy is most likely due to the use of different experimental conditions in the probing and toeprint experiments . In fact , under conditions where the signal is highly amplified , reverse transcriptase stops are visible in the toeprint experiments ( data not shown ) . Hfq protein was not included in these in vitro assays , because of the risk of non-specific interactions with RNA . The fact that , even in the absence of this chaperone , MicA and GcvB could both pair to phoP mRNA in vitro ( i . e . in the absence of RNases ) suggests that the requirement for Hfq in vivo is , at least in part , explained by its ability to protect MicA and GcvB from degradation . In summary , both MicA and GcvB inhibit ribosome binding by pairing to phoPQ TIR . This translational block could be the step leading to the degradation of the target-mRNA in presence of the regulatory sRNAs observed in vivo .
In this study , we identify phoPQ mRNA as a new target of the E . coli GcvB sRNA . After MicA , this is thus the second sRNA regulator of this operon . Similar to MicA , GcvB directly controls phoPQ expression by pairing to the TIR of phoP , although at sequences slightly different from those of MicA . These pairings cause a steric inhibition of ribosome binding as seen by toeprint experiments and , possibly as a consequence of this translational control , induce degradation of the phoPQ mRNA . Furthermore , this work identifies a novel pairing region of GcvB , namely R3 , essential for phoP control . Interestingly , this region was predicted in a computational approach as a potential target-binding region of GcvB ( together with the R1 ) on the basis of its conservation and accessibility [33] , and was proposed to participate in the control of cycA expression [34] . Whether GcvB controls yet additional genes through its R3 region remains to be investigated . This region R3 is with R1 and R2 one of the most conserved in GcvB among enterobacteria ( Figure S2A ) . However , phoP was not identified as a GcvB target in Salmonella in a recent study combining microarray analysis following GcvB pulse-expression and bioinformatic prediction based on complementarity to the R1 region [13] . While this could be due to the low abundance of phoPQ mRNA and to the fact that this control does not rely on R1 , this could also indicate that the control of phoP by GcvB that exists in E . coli is not conserved in S . typhimurium . Consistent with this , predictions of pairing between GcvB R3 region and the TIR of phoP mRNA in Salmonella identified only 4 consecutive complementary nts at the most , which is probably too short to ensure specific binding . Furthermore , a preliminary analysis of potential interactions between GcvB R3 and the phoP TIR in different families of enterobacteriaceae suggests that GcvB could control phoP in species such as Klebsiella pneumoniae , Photorhabdus luminescens , Proteus mirabilis , Serratia proteamaculans , Shigella flexneri , Xenorhabdus bovienii and Rahnella ( Figure S2B ) . At first glance , it is quite surprising that , even though MicA , GcvB and GcvBmutR1 similarly repress phoP expression when followed by gene fusion to lacZ or mRNA levels , their effects on the PhoP protein are quite different . Indeed , while MicA and GcvBmutR1 strongly decreased the cellular level of PhoP , as expected , the effect of wt GcvB was much more moderate ( Figure 5B ) . One noticeable difference in those experiments is that when the PhoP protein levels were assessed , phoPQ was expressed from its own promoter . In contrast , in both the experiments with gene fusion or mRNA levels , its transcription is driven by an heterologous promoter ( PBAD ) , with phoPQ and phoP-lacZ mRNAs expected to originate at the same transcription start site than from P1 . Therefore , one could hypothesize that wt GcvB would activate PhoP synthesis from transcripts originating from promoters P2 or P3 ( upstream of P1 ) , or by acting on phoPQ transcription , in addition to repress its expression post-transcriptionally . These possibilities were experimentally ruled out because ( i ) wt GcvB similarly repressed expression of a phoP-lacZ fusion whose 5′end is identical to the 5′end initiating from P1 or from P2 promoter ( Figure S3A ) and ( ii ) wt GcvB only poorly affects ompT expression ( and even increases yneM expression ) in a strain where all the phoPQ promoters have been replaced by PBAD ( Figure S3B ) . One could also envision that , when expressed from its own promoter under non-inducing conditions ( as in Figure 5B ) , phoP expression is poorly affected by wt GcvB , whose R1 region can pair with other competing targets . This competition could not take place with GcvBmutR1 , where the R1 region is mutated , in agreement with its ability to control phoP in all experiments . In contrast , when phoP expression increases , for instance because its transcription is driven by an induced PBAD promoter , it would now become available for repression by wt GcvB , because it would outcompete other GcvB targets , hence the stronger effect of wt GcvB on phoP in Figures 3C and 5A for instance . It will now be interesting to study how the induction of phoP from its own promoter and the expression of GcvB R1 targets will impact the control of phoP by GcvB; in other words , whether this model is physiologically relevant . Yet another possibility to explain the difference between phoP-lacZ expression , phoPQ mRNA and the PhoP protein levels in presence of pGcvB is that , in addition to its negative effect on phoP expression at the translation initiation step , wt GcvB could stabilize the PhoP protein . Such a regulatory event would affect only PhoP levels , but not the activity of phoP-lacZ or phoP mRNA levels . This putative stabilization would be dependent on the R1 region of GcvB and could be mediated by one or several of its targets . Furthermore , one could wonder whether this stabilization is related to the phosphorylation status of PhoP protein . Because these R1 targets that could directly or indirectly control PhoP are likely to be multiple , it might be difficult to identify them by a genetic approach . Again , it is tempting to speculate that the expression of these targets , as well as the availability of the R1 region to regulate them ( dependent on the expression of all GcvB targets and their relative affinity for the sRNA ) , will play an important role in the control of the PhoP regulon by GcvB . While under our experimental conditions , the amount of PhoP protein is not strongly affected by wt GcvB , there might be conditions where it could be . This would provide a mechanism to establish a hierarchy among the different GcvB-targets and to monitor the regulatory outcomes of GcvB-mediated controls . In a previous work , we showed that MicA , which is induced by envelope stress , repressed the expression of phoPQ . This finding related the activity of this operon to the cell envelope status [24] . Our present findings show that GcvB relates amino acid ( or peptide ) uptake and metabolism to the expression of phoPQ . The induction of GcvB occurs in the presence of two different amino acids with two different mechanisms . First through the GcvA/GcvR repressing complex that is inactivated in the presence of glycine , and second through the global regulator Lrp , whose repression is alleviated in the presence of leucine [9] , [11] ( Figure 8 ) . Although the raison d'être of such a connection between amino acid uptake/metabolism and PhoQ/PhoP regulon activity is not obvious , such a relationship has already been observed with several targets of this regulon . For instance , expression of mgtA and mgtCBR genes is derepressed under conditions of proline limitation due to the presence of a proline-rich open reading frame in their leader mRNAs [35] , [36] . Another example is the proline transporter encoded by the proP gene that is also regulated by PhoQ/PhoP [37] . The complexity of the relationship between GcvB and the phoPQ regulon is highlighted by two experimental data . The first is the surprising way wt GcvB fails to strongly decrease PhoP levels as discussed above . The second is related to the recent results of a deep-sequencing study indicating that PhoP positively regulates GcvB levels in the cell [38] ( Figure 8 ) . However , GcvB levels were unmodified when the magnesium concentration in the growth medium was varied . Therefore , GcvB could also modulate the degree of PhoQ/PhoP activation depending on the inducing signals . Interestingly , there are already many examples of connections between sRNAs and TCS , as several sRNAs were previously shown to control TCS and conversely [39] . In addition , the negative feedback loop that exists between phoP and GcvB is reminiscent of other feedback loops involving sRNAs [40] . As in most cases however , the properties and possible advantages of this feedback loop in bacterial physiology remain to be experimentally addressed . Our search for Hfq-dependent sRNAs regulators of the PhoQ/PhoP TCS was initially motivated by the fact that phoP expression was up-regulated in an hfq mutant strain independently of MicA . Interestingly however , even though GcvB is partially responsible for this Hfq-effect , expression of phoP is still higher in an hfq mutant than in hfq+ cells in the absence of both MicA and GcvB ( data not shown ) . This suggests that there might be even more sRNAs controlling PhoQ/PhoP , which would allow the integration of yet additional signals to fine-tune expression of this central TCS .
Strains and plasmids used in this study are listed in Table 1 , and sequences of the oligonucleotides in Table S1 . Strains were grown aerobically in LB medium at 37°C . When needed , antibiotics were used at the following concentrations: ampicillin 150 µg/ml , tetracyclin 10 µg/ml , kanamycin 25 µg/ml or chloramphenicol 10 µg/ml . PCR amplification was performed using the Phusion DNA polymerase ( New England Biolabs ) . IPTG ( isopropyl-ß-D-thiogalactopyranoside ) was used at a final concentration of 100 µM . Replacement of gcvB gene by a kanamycin or tetracyclin resistance cassette was engineered by recombineering of a cassette amplified by PCR ( with ΔgcvB::kanfor and rev , or ΔgcvB::tetfor and rev oligonucleotides ) and flanked by homology regions upstream and downstream of gcvB into a strain carrying a mini-lambda allowing recombineering upon induction , such as NM300 or NM1200 for instance . These mutant alleles , as well as ΔmicA::tet [24] or phoP::kan [41] were then moved by P1 transduction when necessary . Strains carrying gene fusions to lacZ were either obtained from different sources or constructed in this study by recombineering into strain PM1205 [19] or strain MG1508 . In strain MG1508 , a cat-sacB cassette following the PLtetO-1 promoter [42] is placed upstream of the lacZ gene in an MG1655 derivative that carries a mini-lambda . Strain MG1173 carries a translational ompT-lacZ fusion , whose construction was described previously , at the lambda attachment site [18] . Strains KM112 and KM194 were also described elsewhere [43] . They contain respectively the promoter region up to nt+10 of MgrR , or the promoter region of yneM up to nt+91 ( nt+1 is the transcriptional start site ) upstream of lacZ chromosomal gene , starting 17 nts upstream of its ATG start codon . The translational Ptet-phoP-lacZ fusion was constructed by replacing the cat-sacB cassette of the Ptet-cat-sacB-lacZ construct in strain MG1508 with a PCR fragment encompassing nts −36 to +30 ( relative to the ATG start codon ) of phoP between homology regions to Ptet and lacZ respectively . This PCR fragment was generated with primers 5′Ptet-phoP and 3′phoP-lacZ . Recombinants were selected on LB-agar plates without NaCl supplemented with 6% sucrose , and further verified as in [24] . Similarly , construction of the P1-mgtA-lacZ fusion was done by recombineering of a PCR fragment carrying nts −330 to +30 of mgtA in MG1508 , except that the homology regions were upstream of Ptet and within lacZ ( see primers 5′P1mgtA and 3′mgtA-lacZ ) . For strains MG1510 ( PBAD-phoPmutR3-lacZ ) and AC0067 ( PBAD-livJ-lacZ ) , PCR fragments were generated with primers phoPmutR3for and 3′phoP-lacZ , or 5′LivJ-lac and 3′LivJ-lac respectively , then recombined into strain PM1205 . Construction of strains where the phoPQ operon , wt or interrupted by a kanamycin resistance cassette , is expressed from a PBAD promoter was as follows . First , the chloramphenicol resistance cassette followed by the PBAD promoter was amplified by PCR from plasmid pTM26 [44] with primers 5′Cm-PBAD-phoPQ and 3′Cm-PBAD-phoPQ . This product was then recombined in strain NM300 ( or a derivative carrying the phoP::kan allele moved by P1 transduction using AB043 [41] as the donor strain ) . After selection on LB-chloramphenicol plates , the PBAD promoter and beginning of phoP gene were checked by sequencing , and resistance to kanamycin was verified for the PBAD-phoP::kan construct . These alleles were then moved by P1 transduction using DJ624 as the recipient strain and selecting for chloramphenicol resistant clones to create strains MG1516 and MG1517 . MG1517 was checked for resistance to kanamycin . Overnight cultures were diluted 500 fold in fresh medium ( see below for exact medium composition ) and grown to mid-exponential phase ( OD at 600 nm∼0 . 4 ) . The ß-galactosidase activity was then measured as in [45] and expressed in Miller units . Alternatively , the activity was measured in a 96-wells plate for some experiments . In this case , 100 µl of cells were mixed with 50 µl of permeabilization buffer containing 200 µg/ml of polymixin B [46] . After addition of 50 µl of ONPG , the absorbance at 420 nm was followed over-time and the ß-galactosidase activity was calculated as the slope of the resulting curve . It is expressed in arbitrary units . Cells were grown in the following media: for Figure 1A , LB-Ampicillin-IPTG-Arabinose 0 . 002% or 0 . 02%; for Figure 1B , LB-Ampicillin-IPTG; for Figure 1C , LB; for Figure 2A and 3C , LB-Ampicillin-IPTG-Arabinose 0 . 02%; for Figure 2B , LB-Arabinose 0 . 002%; for Figure 3B , LB-Ampicillin-IPTG-Arabinose 0 . 02% , but IPTG was not included in the overnight cultures; for Figure 6A , LB-Tetracyclin-IPTG; for Figure S1 , LB-Tetracyclin-IPTG-Arabinose 0 . 02% . Values of ß-galactosidase activity given in the paper are the average of at least two independent experiments and are listed in Table S2 . RNA was extracted following the hot phenol method as previously described [47] and using 650 µl of cells . For experiment of Figure 3 , RNA was extracted at the same time than samples were taken to measure ß-galactosidase activity . For experiments of Figures 5 and 6 , cells were grown overnight in LB-Tetracyclin-IPTG-Arabinose ( 0 . 2% for Figure 5 and 0 . 02% for Figure 6 ) , then diluted in fresh medium and grown to mid-exponential phase ( A600∼0 . 4 for Figure 6B ) or to stationary phase ( A600∼2 for Figure 5 ) before RNA was extracted . For Northern analysis , a constant amount of RNA was separated on an 8% acrylamide TBE-urea gel ( for MicA , GcvB , MgrR and SsrA RNAs ) or on a 1% denaturing agarose gel ( for ompT and phoP mRNAs ) and transferred to an Hybond-N+ membrane . Detection was then performed using biotinylated probes and the Ambion brightstar detection kit following manufacturer's instructions . Overnight cultures in LB-Tetracyclin-IPTG of strain MG1173 ( wt ) or MG1446 ( phoP− ) , transformed with pBRplac and derivatives , were diluted 500-fold in the same medium and grown to mid-exponential phase . Cells were then pelleted and resuspended in SDS-sample buffer with DTT ( New England Biolabs ) at a final concentration of 15 OD600/ml . These samples were then boiled for 5 minutes and 15 µl were loaded on a 15% SDS-PAGE gel . Proteins were then transferred to an Hybond-C super membrane ( Amersham ) and the PhoP protein was detected using a 1∶1000 dilution of an anti-PhoP antiserum ( from Mark Goulian ) and the Immun-Star WesternC Chemiluminescent Kit ( Biorad ) . EF-Tu was immunodetected from the same membrane . A representative blot from three independent experiments is shown . PCR templates for the in vitro transcription of MicA or GcvB ( and their derivatives ) were prepared from the pBRplacMicA ( mut ) or pBRplacGcvB ( mutR1 , mutR3 or mutR1R3 ) plasmids , using the oligonucleotides 5′T7MicA ( mut ) and 3′T7MicA , or 5′T7GcvB and 3′T7GcvB respectively . Note that one or two G residues are added at the 5′end of MicA and GcvB respectively . For phoP , a PCR fragment corresponding to nts −36 to +169 of phoP mRNA preceded by two G residues was amplified from genomic DNA using oligonucleotides 5′T7phoP and 3′T7phoP . After purification , these PCR products were used in in vitro transcriptions reactions with the T7 RNA polymerase of Stratagene ( for phoP ) or the T7 Megascript kit from Ambion ( for MicA and GcvB ) following manufacturer's instructions . After phenol extraction and precipitation with ammonium acetate , RNA were purified using G-50 Microspin columns ( GE Healthcare ) . Toeprinting assays were adapted from Hartz et al . [48] as follows . 0 . 5 pmol . of phoP transcript were incubated with 2 pmol . of phoP-Cy5-probe#1 , an oligonucleotide complementary to nts 52 to 71 of phoP ORF and labeled with a Cy5 group at its 5′ end , in a buffer containing 10 mM Tris-acetate pH 7 . 4 , 60 mM ammonium chloride and 6 mM ß-mercaptoethanol . When required , sRNAs were added to this mix at the desired concentrations . These mixtures were denatured by heating at 80°C for 3 minutes , followed by a rapid cooling in an ethanol/solid CO2 mix . They were then thawed on ice and magnesium was added at a final concentration of 10 mM . For experiment of Figure 7A , an additional incubation step at 37°C for 10 minutes was performed at this stage . 190 µM dNTPs and 2 . 5 µM of initiator tRNAfMet were added together with 0 . 5 µM 30S subunits , and the mixtures were incubated at 37°C for 10 minutes . 1 unit of AMV RT ( Finnzymes ) was then added and cDNA synthesis was performed at 37°C for 20 minutes . Reactions were stopped by addition of formamide and EDTA , analyzed on a 6% sequencing gel together with sequencing reactions and RT stops were visualized using a typhoon fluorescent scanner set up for Cy5 detection . 1 . 25 pmol . of phoP transcript , mixed in water with 6 . 25 pmol . of sRNA when required , were denatured as above . After a slow thaw-out on ice , samples were incubated at 37°C for 10 minutes in order to allow sRNA and phoP message to pair . Samples were then diluted in a buffer containing 50 mM sodium cacodylate pH 7 . 5 , 10 mM magnesium acetate and 50 mM ammonium chloride prior to DMS treatment , or in the same buffer except that sodium cacodylate is replaced by sodium borate pH 8 . 0 prior to CMCT or Kethoxal treatment . After a 10 minutes incubation at 25°C , 1 µg of L . lactis 23S rRNA was added , followed by 0 . 1 volume of DMS or kethoxal ( stock solutions are 1/30 in ethanol or at 4 mg/ml in 20% ethanol respectively ) and samples were incubated at 25°C for 5 minutes . For CMCT treatment , 0 . 1 volume of 100 mg/ml CMCT in the previous buffer containing sodium cacodylate was added and samples were incubated at 25°C for 10 minutes . Modified RNA were then precipitated with ammonium acetate ( and 100 mM sodium borate pH 8 . 0 for samples treated with Kethoxal ) and resuspended in water ( or in 12 . 5 mM sodium borate pH 8 . 0 for samples treated with Kethoxal ) . For reverse transcription , phoP-Cy5-probe#2 , complementary to nts 87 to 106 of phoP ORF , was added to those samples at the final concentration of 1 µM . This was followed by the addition of 2 units of AMV RT ( Finnzymes ) , together with 1 mM dNTPs and 4 mM DTT . cDNA synthesis and analysis was then performed as described above .
|
Regulation of bacterial gene expression participates in the ability of these microorganisms to quickly adapt to their environment . This regulation can occur at every level of gene expression . For instance , two-component systems are involved in transcriptional control , while small RNAs usually act at the post-transcriptional level . In this study , the pleiotropic small RNA GcvB is identified as the second small RNA regulator of the central PhoQ/PhoP two-component system , which highlights the connections between the different types of regulation .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"bacteriology",
"gene",
"regulation",
"genetics",
"gene",
"expression",
"molecular",
"genetics",
"biology",
"microbiology",
"genetics",
"and",
"genomics"
] |
2013
|
Post-Transcriptional Control of the Escherichia coli PhoQ-PhoP Two-Component System by Multiple sRNAs Involves a Novel Pairing Region of GcvB
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The importance of honey bees to the world economy far surpasses their contribution in terms of honey production; they are responsible for up to 30% of the world's food production through pollination of crops . Since fall 2006 , honey bees in the U . S . have faced a serious population decline , due in part to a phenomenon called Colony Collapse Disorder ( CCD ) , which is a disease syndrome that is likely caused by several factors . Data from an initial study in which investigators compared pathogens in honey bees affected by CCD suggested a putative role for Israeli Acute Paralysis Virus , IAPV . This is a single stranded RNA virus with no DNA stage placed taxonomically within the family Dicistroviridae . Although subsequent studies have failed to find IAPV in all CCD diagnosed colonies , IAPV has been shown to cause honey bee mortality . RNA interference technology ( RNAi ) has been used successfully to silence endogenous insect ( including honey bee ) genes both by injection and feeding . Moreover , RNAi was shown to prevent bees from succumbing to infection from IAPV under laboratory conditions . In the current study IAPV specific homologous dsRNA was used in the field , under natural beekeeping conditions in order to prevent mortality and improve the overall health of bees infected with IAPV . This controlled study included a total of 160 honey bee hives in two discrete climates , seasons and geographical locations ( Florida and Pennsylvania ) . To our knowledge , this is the first successful large-scale real world use of RNAi for disease control .
The importance of honey bees as pollinators of crops to the global economy far surpasses their contributions in terms of honey production [1] . In all , 52 of the world's 115 leading agricultural crops rely on honey bee pollination to some extent . These crops represent approximately 35% of the human diet [2] . Insect pollination , which is provided predominately by honey bees , is estimated to have a value of US$ 212 billion [3] . Honey bee populations have been decreasing globally in recent years [4] . Since fall 2006 , honey bees overwintering in the U . S . A . have faced unusually high rates of mortality , in part because of a phenomenon now known as Colony Collapse Disorder ( CCD ) [5] . Several hypotheses have been offered to explain CCD and existing and emerging pathogens have been implicated either directly or indirectly [6] . Colonies affected by CCD are infected with larger numbers of pathogenic organisms than control colonies , yet no single pathogen was found associated with all affected colonies [7] . In another effort , researchers did find that single-stranded RNA viruses , specifically picorna-like viruses , occurred at elevated levels in CCD colonies . These elevated levels of viruses may interfere with gene transcription , thus reducing immune response competence and pesticide detoxification capabilities , subsequently leading to premature death of infected bees [8] . Honey bees are susceptible to a host of picorna-like viruses , including the closely related Acute Bee Paralysis Virus ( ABPV ) , Kashmir Bee Virus ( KBV ) , and Israeli Acute Paralysis Virus ( IAPV ) [9] , [10] . The latter of these three viruses was identified as a good marker for CCD in initial studies , especially when found in association with the microsporidia Nosema sp . [6] . While IAPV is probably not the sole cause of CCD [7] , its ability to cause increased mortality in honey bees has been established [11] . The process of post-transcriptional gene silencing is thought to be an evolutionarily-conserved cellular defense mechanism used to prevent the expression of foreign genes and is commonly shared by diverse flora and phyla [12] . The presence of long double-stranded RNAs in cells stimulates the activity of a ribonuclease III , Dicer , which is involved in the processing of the double stranded RNA ( dsRNA ) into short interfering RNAs ( siRNAs ) . The RNAi response also features an endonuclease complex , commonly referred to as an RNA-induced silencing complex ( RISC ) , which mediates cleavage of target ssRNA having sequence complementary to the antisense strand of the siRNA duplex . [13] , [14] , [15] , [16] . In a variety of organisms , exogenously applied dsRNA or their siRNA derivatives , can be used to arrest , retard or even prevent a variety of pathogens . In some of these organisms , such as plants and the nematode C . elegans , an amplification stage follows the initiation stage of gene silencing , involving an RNA dependent RNA Polymerase ( RdRp ) , which may lead subsequently to degradation of RNAs outside the initial dsRNA region of homology [17] . RNAi can spread from the initial site of dsRNA delivery , producing interference phenotypes throughout the treated animal . To serve as a preventive or curative strategy , amplification and systemic spread of the silencing signal are both paramount . In some invertebrates , including honey bees , a systemic interference defective ( SID ) gene encodes a transmembrane protein that is an important participator in the systemic RNAi pathway . Apparently , these SID1-like proteins channel dsRNAs between cells , enabling systemic spread of the silencing signal [18] , [19] . Although a canonical invertebrate RNA dependent RNA Polymerase ( RdRP ) homologue has not yet been described , there is evidence that such RdRp activity may occur via other enzymes , leading to amplification of the silencing signal in insects [20] . IAPV specific dsRNA ( Remebee-IAPV or herein Remebee-I ) was used successfully to prevent bees from succumbing to infection from IAPV in small scale lab experiments whereas bees fed Green Fluorescent Protein ( GFP ) dsRNA and virus died in a manner similar to the IAPV fed control bees [12] . Although these results were exciting per-se , transferring RNAi from a well characterized and efficient tool in the lab and making it successful in preventing the adverse effects of virus infection in the field , remains notoriously difficult . We present the first large-scale real world successful use of RNAi for disease control . We attempted to determine if IAPV specific homologous dsRNA can be used to reduce impacts from IAPV infection in 160 honey bee hives in two discrete climates , seasons and geographical locations ( Florida and Pennsylvania ) . To our knowledge , this is the first successful demonstration of the use of RNAi as a preventative treatment for an insect disease on such a large scale .
The field demonstration in FL was designed in a manner that permitted us to follow IAPV-infested bee colonies ( some given Remebee-I and others not ) for six weeks . One hundred standard colonies of honey bees were split into 5 groups with 20 colonies per group . Four groups were located within 100 m of one another ( non-isolated ) while a 5th group was isolated from the remaining four by at least 3 . 2 km to measure any environmental effects due to location . Treatment allocations ( 20 colonies per treatment ) were as follows: Treatment 1 – no treatment – non isolated Treatment 2 – Remebee-I only – non isolated Treatment 3 – Remebee-I+IAPV – non isolated Treatment 4 – IAPV only ( fed in sugar water solution ) – non isolated Treatment 5 – no treatment– isolated Honey ( or net weight gain ) is the ultimate proxy to the total active population of the hive . The non treated control produced the most honey in PA , but not in FL . In FL , colonies treated with Remebee-I+IAPV produced significantly more honey than colonies receiving IAPV alone ( Figure 2 , N = 40 , p<0 . 03 ) . In PA , the difference between the weight at the start and the end of the experiment ( 4 months ) shows that the non infected controls gained the most weight ( mean gain = 23 . 5kg ) , whereas Remebee-I+IAPV had gained slightly less ( mean = 21kg ) . Both made significantly greater weight gains compared with the group receiving IAPV alone ( mean = 16 . 3kg ) ( Figure 3 F = 2 . 7; df = 4 . 92; P = 0 . 034 ) . Subsequent trials done under a similar protocol were repeated in the winter of 2009–10 in FL and in California ( CA ) . Samples of bees were collected just before IAPV challenge and 2-weeks post treatment . Northern analysis was done with IAPV specific sequence probes corresponding with the Remebee-I sequence . The results of these are presented in detail in Supporting Information S2 . High levels of discrete Dicer Remebee metabolites are evident in Remebee-I treated hives prior to IAPV challenge up to four weeks after a Remebee-I application . Non- Remebee-I treated bees are mostly negative , but a low signal was detected in some colonies . Subsequent to IAPV challenge , levels of siRNAs and IAPV metabolites are highly elevated in both Remebee-I and in non-Remebee-I treated hives , showing that production of dsRNA is a natural defense mechanism in bees against IAPV infection . Varroa levels are unaffected by treatment . The change in varroa prevalence on adult bees did not differ significantly between treatment groups in either the FL ( F4 , 95 = 2 . 39; P = 0 . 056 ) or PA ( F2 , 57 = 1 . 03; P = 0 . 3642 ) trials . Nosema levels: While the change in nosema levels in the FL trial was not significantly different between treatment groups ( F4 , 95 = 0 . 47; P = 0 . 7586 ) , a highly significant difference in nosema spore levels was detected at the end of the PA trial ( F2 , 57 = 8 . 62; P = 0 . 0005 ) ( Figure 4 ) . Indeed , within the duration of the experiment , nosema spores levels increased in the IAPV treated group , yet went down in both Remebee-I+ IAPV and uninfected control colonies . A brief summary of findings is presented in Table 1 .
The negative effects of IAPV on honey bee health and colony vigor is evidenced by lower honey weight gains ( Figures 2 and 3 ) . Environmental factors and terrain also influence the availability of forage and thus can seriously reduce or increase the effects of virus infection on a hive by altering foraging patterns . Large scale examination of the RNAi treatment under such varied conditions at two separate and diverse locations ( i . e . FL and PA ) was challenging . However , one would expect that 7–8 weeks after a young queen begins to oviposition prolifically ( see Supporting Information S1 for brief overview ) , all hives would be overflowing with bees . This was evidently not the case in this situation , and the IAPV only group reversed in total population and bee/brood ratio . Thus , we observed relative de-population of the hive with probable greater loss of foragers in IAPV only infected hives . We conducted the trials described herein in spring and summer , whereas CCD is a mostly winter phenomenon [1] , [7] . This may be the reason that we did not see many hives devastated by CCD . However , subsequent trials performed in the 2009–10 winter in Florida and California resulted in 40% and 60% collapse of hives , respectively ( Hunter Wayne . and Oliver Randy . , personnel communication ) . Beyond cold weather providing additional stress on the bees , some of the difference may be attributed to the viruses' suppressors of gene silencing . In plants , these viral suppressors of gene silencing have more optimal enzymatic kinetic coefficients under cold temperatures in relation to the silencing enzymes , often leading to more acute virulence [21] . This could help explain the initiation and overall devastation of hives in the U . S . following winter cold snaps ( Dennis VanEngelsdorp unpublished observations ) . Our hypothesis that Remebee-I would protect bees from IAPV infection was supported by multiple observations: First , in FL , the Remebee-I+ IAPV treated hives were the only colonies with significantly increasing numbers of bees during the study . In PA bees increased within all treatments with no significant differences . Some of the difference between the two trials could account for this difference in observations . In PA the virus was introduced twice into the colonies within a three day period ( instead of once ) , and the total amount of virus introduced was thus much higher than the FL trial . The PA trial starter hives were weaker in strength , and had more collapses of hives across all treatments prior to infection than those starting the FL trial . Second , although bee to brood ratios started lower in Remebee-I treated hives it became stronger and remained so until the end of the trial . The change in the ratio is attributed to changes in the adult bee counts , since the capped brood coverage was the same between treated and non-treated bees , thus these hives also contained more adult foragers , which resulted in significantly more honey production over the IAPV only treatment ( Figure 2 , 3 ) . Furthermore , in subsequent trials , molecular evidence now proves that Remebee-I is active in the Remebee-I + IAPV treated groups , as determined by the presence of siRNA ( see Supporting Information S2 ) . The strong presence of siRNAs probably restricts the severity of the disease in the bees leading to a longer life-span and subsequently to an overall greater number of bees , with more foragers and consequently a greater yield of honey . It is interesting to note the natural occurrence of these siRNAs in bees receiving IAPV challenge . Presence of these siRNA in non- Remebee-I treated hives prior to infection may be a result of natural virus infection prior to the challenge , or by transcription of integrated viral sequences in the bee genome [22] . IAPV specific dsRNA ( Remebee-I ) was used successfully to prevent bees from succumbing to infection from IAPV . The results further demonstrate the possibility to produce targeted treatments for bee pathogenic diseases . These field results demonstrate the successful application of dsRNA as a viable treatment to solve a real world problem , which may further lead to concerted efforts to utilize this ubiquitous natural mechanism , RNAi , for the benefit of the bees , beekeepers , and hopefully to other applications in agriculture and veterinary health .
Essentially as described in [11] . Approximately 40 adult forager honey bees were collected from 10 colonies in a Florida bee yard ( apiary ) where CCD had been reported . Each bee was processed individually and tested using rtPCR for the presence of Israeli Acute Paralysis Virus ( IAPV ) , genome – NC_009025; Acute Bee Paralysis Virus ( ABPV ) genome – NC_002548; Kashmir Bee Virus ( KBV ) genome – NC_004807; Black Queen Cell Virus ( BQCV ) , genome-NC_003784; Deformed Wing Virus ( DWV ) genome NC_004830 . All bees had more than one virus detected so inoculum was prepared from bees which tested positive only for IAPV+KBV by homogenizing the bees with glass beads in small amounts of 10 mM buffer phosphate , pH 7 . 2 containing 0 . 02% DETCA ( Sigma-Aldrich Cat #22 , 868-0 ) . Inoculum was prepared by passing the virus solution through a syringe filter , 0 . 45 µm , to remove bacteria , after which ∼10 µl were administered by microinjection along the lateral side of the abdomen of ∼700 pupae using a Hamilton syringe with a 30Gx½ gauge sterile needle . Inoculated pupae were kept in petri dishes covered with slightly damp filter paper and maintained at 21–23°C for three days to permit virus replication . On the third day , batches of about 50 pupae were homogenized with glass beads . Small amounts of 10 mM buffer phosphate ( pH 7 . 2 contained 0 . 02% DETCA , Sigma-Aldrich Cat #22 , 868-0 ) were added to the homogenates . The homogenates were collected in a beaker volume adjusted to ∼350 ml with buffer ( see above ) and mixed . Each sample was split into two 250 ml centrifuge tubes and centrifuged at 300×g ( ∼1 , 400 rpm ) on a GSA rotor for 20 min . The supernatant ( S1 ) was collected and kept at 4°C for 3 d . The pellet ( P1 ) was recovered and saved at 4°C . Since some precipitation was noticed after 3 d , the supernatant was centrifuged again as before for 10 min to remove debris . Supernatant ( S1 ) next was transferred to 12 ultracentrifuge tubes ( about 26 ml/tube ) ( Beckman Cat #355618 ) and centrifuged for 4 h , 4°C , at 37 , 000 rpm ( ∼124 , 500×g ) ( Beckman Type 50 . 2 Ti rotor , Beckman Optima L-70K Ultracentrifuge ) . After 4 h the supernatant ( S2 ) was removed and saved . The pellet ( P2 ) was resuspended in 10 mM phosphate buffer containing 4% Brij 58 ( Aldrich Cat #388831 ) and 0 . 4% Sodium deoxycholate ( Sigma-Aldrich D6750 ) : about 1 ml of buffer was used per tube . It was necessary to insert a spatula to help pellet into solution and this was followed by vortexing the suspension . The content from each tube was transferred to clean 50 ml centrifuge tubes . The process was repeated twice but only buffer phosphate was added the final time . Because the final solution was very thick , buffer was added to increase the final volume to ∼30 ml and this was mixed by inversion . The tube was centrifuged for 15 min at ∼10°C , 800×g ( Beckman Coulter Allegra 25R ) , to remove debris . The pellet ( P3 ) was saved at 4°C . ( the pellet saved as a backup ) . The supernatant ( S2 ) was transferred into two clean 50 ml tubes and 13 . 2 g CsCl ( Amresco Cat #0415 ) were added to each tube . To ensure the right CsCl concentration , 13 . 2 g CsCl were added to ∼10 g sample; however the final volume was adjusted to 24 ml with buffer and gently mixed ( up/down ) . The second tube was set by adding CsCl to the remaining sample . This final preparation was transferred to two ultracentrifuge tubes ∼25 ml ( Beckman Cat #355618 ) and centrifuged at 37 , 000 rpm ( ∼124 , 500×g ) , 18°C for 24 h . After 24 h centrifugation , the tubes were removed carefully from the rotor and the whitish virus band collected by insertion of a needle attached to a syringe . Two more fractions were recovered for analyses: ( 1 ) the “liquid” part left after removing the virus band and ( 2 ) the “pellet” ( P4 ) attached to the bottom of tube: Each fraction was transferred to dialysis tubes ( Thomas Scientific Cat #3787-F42 ) and dialyzed overnight against nanopure filtered water followed by 3–4 additional changes in water the following day . After dialysis , content from the tubes was collected in 15 ml clean tubes and the volumes were measured . A subsample of 20 µl from each fraction was tested for virus presence . Adult bees were transferred to 1 . 5 ml centrifuge tubes . Tri Reagent ( Sigma Cat #T9424 ) , was added and individual bees were homogenized in 0 . 5 ml Tri reagent using disposable pestles and glass beads . Homogenates were frozen at −20°C if needed . Samples then were centrifuged 10 min at 12 , 000×g , at 4°C . The clear supernatant was transferred to a new tube and left at least 5 min at room temperature ( RT ) . Next , 0 . 2 ml chloroform was added and samples were shaken vigorously . This was followed by a10–15 min incubation at RT . Tubes were centrifuged 15 min at 12 , 000×g at 4°C . The colorless upper aqueous phase was transferred to a new tube and 0 . 5 ml isopropanol was added . After mixing , samples were allowed to stand for 10 min then spun 10 min at 12 , 000×g at 4°C . The supernatant was removed and the pellet containing the RNA was washed with 1 ml 75% ethanol . After 5 min centrifugation at 7 , 500×g at 4°C , the RNA was allowed to dry ( 5–10 min ) and reconstitute in ∼30 µl Nuclease free water ( Qiagen ) . RNA concentrations were measured in a Nanodrop , ND-1000 Spectrophotometer . Samples were diluted in Nuclease free water . The field demonstration in FL was designed in a manner that permitted us to follow IAPV-infested bee colonies ( some given Remebee-I and others not ) for six weeks . One hundred standard colonies of honey bees were split into 5 groups with 20 colonies per group . Four groups were located within 100 m of one another ( non-isolated ) while a 5th group was isolated from the remaining four by at least 3 . 2 km to measure any environmental effects due to location . Treatment allocations ( 20 colonies per treatment ) were as follows: Treatment 1 – no treatment – non isolated Treatment 2 – Remebee-I only – non isolated Treatment 3 – Remebee-I+IAPV – non isolated Treatment 4 – IAPV only ( fed in sugar water solution ) – non isolated Treatment 5 – no treatment– isolated Colonies were equalized according to standard protocols prior to the beginning of the study ( frames of bees/brood moved between colonies until populations leveled ) and were managed optimally for honey production . Data collected at the beginning , middle , and end of the study included: frames of adult bees , cm2 brood , the presence of other bee maladies ( nosema , varroa , and tracheal mites ) , bee activity , honey production and IAPV presence/absence and titer . The study lasted 6 weeks from the date of colony inoculation with IAPV and was replicated in PA with the following modifications: Only Treatment groups 1 , 3 and 4 were established and the trial lasted 12 weeks after inoculation to enable the bees to take advantage of a honeyflow ( Tables 2 and 3 ) . In PA the virus was introduced twice into the colonies within a three day period ( instead of once as in FL ) , and the total amount of virus introduced was thus much higher than the FL trial . We calculated all test/sampling dates below from the date of the last treatment with Remebee-I . Controls 1 , 2 , and 5 , accounted for ‘within treatments’ , a Remebee-I alone treatment to evaluate any potential detrimental effects to bees , and a distant control to measure environmental effects in the absence of IAPV . Colonies were equalized at the beginning of the studies , starting and ending colony strength parameters were compared using ANOVA recognizing treatment as the main effect ( PROC GLM ) . Honey gains in treated colonies in FL and PA were compared identically . However , to compare colony size measures in the PA trial and for levels of nosema and varroa mites , a Before-After Control-Impact ( BACI ) design [33] , [34] was used . A BACI design us a way of comparing data that are measured before treatment with data obtained after treatment . In general , it can be described as a repeated measures analysis of variance ( ANOVA ) which is performed using colonies as replicates and the covariance structure that best suits the data ( PROC MIXED , SAS Institute ) . Each variable is measured at the start of the experiment to show existing conditions before treatment and then after a treatment . The analysis then looks at whether the change in variable measures was different between treatment groups . A repeated measures analysis of variance [34] was performed using colonies as replicates and an unstructured covariance structure was performed using SAS statistical software ( PROC MIXED ) [35] . Israeli Acute Paralysis Virus ( IAPV ) genome – NC_009025 ( RefSec ) ; Acute Bee Paralysis Virus ( ABPV ) genome – NC_002548 ( RefSec ) ; Kashmir Bee Virus ( KBV ) genome – NC_004807 ( RefSec ) ; Black Queen Cell Virus ( BQCV ) , genome-NC_003784 ( RefSec ) ; Deformed Wing Virus ( DWV ) genome NC_004830 ( RefSec ) ; RNA dependent RNA Polymerase protein ( C . elegans ) – NP_492131 ( RefSec ) ; SID-1 protein ( A . mellifera ) – XP_395167 ( RefSec ) ; GFP nucleotide sequence – U87625 ( GenBank ) .
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High rates of honey bee mortality continue to threaten food security and apicultural industries worldwide . At least some of these losses are likely the result of viral infections . Application of RNAi technologies in the treatment and management of disease promises new solutions to disease problems through the naturally occurring biological processes of living organisms . This study applied a novel dsRNA product developed specifically with the aim of improving honey bee health . The results demonstrate the successful application of RNAi strategies to improve disease tolerance . Honey bees were fed a dsRNA product , Remebee-I , in the presence of the Israeli Acute Paralysis Virus . Treatment resulted in larger colony populations and thus increased honey production . We show that IAPV specific homologous dsRNA successfully curbed the negative effects of IAPV infection in 160 honey bee hives in two discrete climates , seasons and geographical locations ( Florida and Pennsylvania ) . We provide the first successful demonstration of the use of RNAi as a preventative treatment for an insect disease on such a large scale .
|
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"Abstract",
"Introduction",
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"Methods"
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"biotechnology",
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"biology/post-translational",
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2010
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Large-Scale Field Application of RNAi Technology Reducing Israeli Acute Paralysis Virus Disease in Honey Bees (Apis mellifera, Hymenoptera: Apidae)
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The identification of Mycobacterium tuberculosis genes necessary for persistence in vivo provides insight into bacterial biology as well as host defense strategies . We show that disruption of M . tuberculosis membrane protein PerM ( Rv0955 ) resulted in an IFN-γ-dependent persistence defect in chronic mouse infection despite the mutant’s near normal growth during acute infection . The perM mutant required increased magnesium for replication and survival; incubation in low magnesium media resulted in cell elongation and lysis . Transcriptome analysis of the perM mutant grown in reduced magnesium revealed upregulation of cell division and cell wall biosynthesis genes , and live cell imaging showed PerM accumulation at the division septa in M . smegmatis . The mutant was acutely sensitive to β-lactam antibiotics , including specific inhibitors of cell division-associated peptidoglycan transpeptidase FtsI . Together , these data implicate PerM as a novel player in mycobacterial cell division and pathogenesis , and are consistent with the hypothesis that immune activation deprives M . tuberculosis of magnesium .
With an estimated one-third of the world’s population latently infected with Mycobacterium tuberculosis ( Mtb ) , the question remains: how is this pathogen able to persist in vivo ? In the mouse model , Mtb infection is characterized by an acute phase of logarithmic bacterial growth lasting approximately three weeks , followed by a plateau in bacterial burden , persisting as a chronic infection . The transition from acute to chronic infection—from logarithmic bacterial growth to stable bacterial counts—results from the onset of the adaptive immune response and activation of host macrophages by CD4+ T cell-derived IFN-γ [1 , 2] . IFN-γ enhances the antimicrobial capacity of macrophages by numerous mechanisms including promotion of phagosome maturation and acidification via induction of the GTPase Irgm1 and production of reactive nitrogen and oxygen species mediated by nitric oxide synthase and phagocyte oxidase [3–6] . However , IFN-γ induces hundreds of genes in macrophages [7] and the array of environmental modifications occurring within these macrophages and leading to control of Mtb growth is not entirely understood . Mtb persistence mutants ( per mutants ) are a unique class of strains that are competent for replication during acute infection , but attenuated during chronic infection [8] . Several previously identified per mutants provide information about the processes required for survival in the activated macrophage following the onset of adaptive immunity . For example , a per phenotype was observed for an Mtb mutant lacking isocitrate lyase-1 , an enzyme involved in the glyoxylate shunt and methylcitrate cycle , as well as a mutant lacking the cholesterol transporter Mce4 , indicating that cholesterol and fatty acids are carbon sources required by Mtb to survive during chronic infection [9 , 10] . Macrophage activation promotes phagosomal maturation and intraphagosomal acidification [6 , 11 , 12] . In a screen for Mtb transposon mutants hypersusceptible to acid stress , we previously identified 21 genes whose interruption lead to reduced viability in low pH [13] . The majority of these genes are annotated to have functions related to cell wall processes . These included two independent transposon mutants of the previously uncharacterized Mtb gene rv0955 , a 1 , 368 base pair open reading frame , which is annotated to encode an integral membrane protein with a predicted topology of ten transmembrane helices ( S1 Fig . ) [14–16] . Rv0955 is highly conserved among mycobacteria and actinobacteria , but has no known homologues in other species , and no conserved sequence motifs to predict its function . It is included among the 219 mycobacterial “core” genes noteworthy for their conservation among mycobacterial species , including Mtb and M . leprae [17] . These core genes lack homologues in other bacteria , suggesting that their function may be unique to mycobacteria , and making them potential targets for mycobacteria-specific drugs . Here , we investigated the function of the previously uncharacterized Mtb Rv0955 protein . Disruption of rv0955 resulted in a striking persistence defect in chronic mouse infection with a 300-fold decline in bacterial burden in the lungs . We therefore named this gene perM , encoding a persistence-associated integral membrane protein . As Vandal et al . noted , the acid susceptibility of the perM mutant—similar to many of the mutants identified in the screen—was detergent-dependent , observed only when the bacteria were exposed to a combination of low pH and Tween-80 detergent [13] . We thus sought to investigate mechanisms beyond protection from acid , which might account for the strong attenuation of the mutant in vivo . We found that the perM mutant required increased magnesium ( Mg2+ ) compared to wild type ( wt ) Mtb for replication and survival in culture . Mg2+ is among the most abundant divalent cations in both prokaryotic and eukaryotic cells , and is essential for bacterial growth . In bacteria , Mg2+ serves a wide range of roles: it functions as a cofactor with ATP in numerous enzymatic reactions , enables the formation of tRNA and ribosomal tertiary structure , and regulates stability of the cell wall and membrane [18–20] . Mg2+ also impacts virulence in Salmonella enterica by regulating the PhoP/PhoQ two-component system [21] . In Mtb , two Mg2+-dependent mutants have been identified: Mtb∆phoP and Mtb∆mgtC [22 , 23] . PhoP shows high similarity to the PhoP response regulator of Salmonella enterica and is required in Mtb for the synthesis of several complex cell wall lipids as well as replication in macrophages and mice [22 , 24 , 25] . MgtC is required for virulence of both Mtb and Salmonella enterica and inhibits the bacterial F1F0 ATP synthase to maintain physiological ATP levels and intrabacterial pH [23 , 26] . Mg2+ restriction remains a plausible but unconfirmed antimycobacterial mechanism employed by the host . In media with low Mg2+ concentrations , the perM mutant elongated and upregulated expression of cell division and cell wall biosynthesis genes . Furthermore , Mtb PerM accumulated at the putative division septa in the closely related M . smegmatis . Disruption of perM resulted in pronounced hypersusceptibility to beta-lactam antibiotics , including cephalexin and piperacillin , which are specific inhibitors of the cell division-associated peptidoglycan synthesis protein FtsI . This work characterizes a novel mycobacterial protein necessary for persistence in vivo and implicated in cell division , and is consistent with the hypothesis that Mtb has reduced access to Mg2+ during chronic infection .
PerM was previously identified in a screen for Mtb genes required for acid resistance [13] . To examine the role of PerM in vivo , we monitored replication and survival of a perM transposon mutant , perM::tn , in wild type mice . PerM::tn established infection and replicated during the acute phase , with only a 5-fold reduction in peak bacterial burden , measured by colony forming units ( CFU ) , compared to wt ( P = 0 . 032 ) at 21 days ( Fig . 1A ) . However , perM::tn exhibited a severe persistence defect in chronic infection , with a 300-fold reduction in CFU in the lungs at fourteen weeks post-infection . In agreement with these growth patterns , histological analysis revealed markedly fewer and smaller lesions in perM::tn-infected lung tissue compared to wt Mtb-infected mice , a difference observed at the 148 day post-infection time point , but not at the end of acute infection ( Fig . 1B ) . Genetic complementation of the perM mutant with a wild-type copy of the gene expressed chromosomally under control of the hsp60 promoter restored persistence and increased granulomatous inflammation , indicating that attenuation in vivo was due to disruption of perM . The adaptive immune response to Mtb is characterized by IFN-γ mediated activation of host macrophages . To examine whether death of perM::tn in vivo was dependent on host IFN-γ , we infected IFN-γ knockout mice , which are unable to control replication of wt Mtb [1 , 2] . PerM::tn replicated in IFN-γ knockout mice , but at a slower rate than wt Mtb ( Fig . 1C ) . IFN-γ knockout mice infected with wt Mtb had to be sacrificed at day 50 , because they were moribund , in contrast to IFN-γ knockout mice infected with perM::tn , which remained healthy through the end of the experiment ( day 106 ) . These results indicate that killing of perM::tn in wt mice requires host IFN-γ , while the mutant also exhibits an IFN-γ-independent replication defect . Since later-occurring persistence defects like that of perM::tn often depend on the adaptive immune response of the host , we hypothesized that PerM might cause a more robust immune response than wt Mtb . To examine this possibility , we infected bone marrow derived mouse macrophages with equal numbers of wt , perM::tn and complemented mutant and measured cytokine concentrations in macrophage culture supernatants 24 hours later . Supernatants of macrophages infected with perM::tn contained elevated levels of proinflammatory cytokines , including TNF-α , IL-6 , IL-12 p70 , the anti-inflammatory cytokine IL-10 , and the chemokine KC ( Fig . 2A ) . To assess the immune response to perM::tn during mouse infection we measured cytokine transcripts in mouse lungs by quantitative real-time polymerase chain reaction ( qRTPCR ) , focusing on pro-inflammatory cytokines required to attenuate Mtb growth in vivo [1 , 27] . We did not observe significant differences in IFN-γ or TNF-α mRNA levels at 2 weeks post-infection , when bacterial titers of perM::tn were 3-fold lower than those of wt ( S2A–S2B Fig . ) . In an independent experiment , IL-12p40 protein levels in lung homogenates from mice infected with wt or perM::tn were similar at 1 or 2 weeks post-infection and increased in wt compared to mutant infected lungs or 3 weeks post-infection ( S2C–S2D Fig . ) . Differences in CFU confounded interpretation of these data , as bacterial counts of perM::tn were 3- , 5- and 6-fold reduced compared to wt at weeks 1 , 2 and 3 , respectively; however , these results suggested that attenuation of the mutant in vivo was not exclusively due to a more robust immune response that preceded in perM::tn-infected mice . We sought to better understand the properties of perM::tn leading to the increased innate immune response by macrophages infected ex vivo . In a mixed-strain Mtb infection of macrophages , TNF-α production was similar to that induced by infection with perM::tn alone at the same total multiplicity of infection ( MOI ) ( Fig . 2B ) . The dominance of the mutant suggested that the difference in response to these strains was due to an immunostimulatory effect of the mutant , as opposed to a suppressive effect of intact PerM protein produced by wt Mtb . The stimulatory effect of perM::tn was reproduced by exposure of macrophages to formalin-killed Mtb ( Fig . 2C ) and to cell-free Mtb-conditioned culture media ( Fig . 2D ) , indicating that the stimulatory component ( s ) were shed or secreted by live perM::tn , but did not require viable bacteria for production or release during macrophage infection . In the absence of NOD and TLR2 signaling , perM::tn still elicited higher levels of TNF-α than wt ( Fig . 2E ) . NOD and TLR2 are required for the macrophage response to bacterial peptidoglycan and triacylated lipoproteins , respectively , suggesting that the hyperinflammatory phenotype of perM::tn is not tied specifically to one of these cell wall components . TNF-α production was , however , significantly lower in cultures from knockout macrophages compared to wt macrophages , indicating that these receptors are important for TNF-α production following infection with both strains . Together , these data suggest that a combination of cellular components , both released into the medium during growth and expressed on the surface of killed perM::tn , function to stimulate increased inflammatory signaling in macrophages . In liquid media , perM::tn replicated at a near-normal rate ( Fig . 3A ) , but formed a loose aggregate during growth ( Fig . 3B ) . Unlike previously described mycobacterial biofilms [28] , these aggregates formed on the bottom of standing cultures , rather than at the liquid-air interface , and could be readily dispersed by shaking or pipetting . These aggregates suggested a perturbation of the perM::tn cell envelope . Since extracellular magnesium ( Mg2+ ) has been shown to overcome phenotypes of mutants with cell envelope defects [22] , we asked whether reduction of Mg2+ would affect growth or survival of perM::tn . Strains were cultured in nominally Mg2+-free Sauton’s minimal media , and supplemented with Mg2+ at a range of concentrations up to 2000 μM , the normal concentration in Sauton’s media ( Fig . 3C ) . Wt Mtb died in nominally Mg2+-free media , but survived and replicated at Mg2+ concentrations of 25 μM and higher . In contrast , perM::tn exhibited death , observed by decreasing CFU counts , and lysis , observed by decreasing absorbance , at Mg2+ concentrations 100 μM and below . At 250 and 500 μM Mg2+ , perM::tn replicated , but at a slower rate than wt and the complemented mutant . The requirement for additional Mg2+ was specific , as other cations , including Mn2+ , Ca2+ , Zn2+ , and Fe3+ , could not restore growth of perM::tn in reduced ( 100 or 250 μM ) Mg2+ media ( S3 Fig . ) . For further experiments , Mtb was grown in modified Sauton’s media containing 250 or 500 μM Mg2+ ( “reduced” Mg2+ ) , concentrations at which perM::tn displayed a growth defect without apparent death or lysis , or 2000 μM ( “high” ) Mg2+ . Within the IFN-γ-activated macrophage , Mtb is subject to numerous stresses , including low pH , reactive nitrogen intermediates , reactive oxygen species , and nutrient limitation [29 , 30] , and it has been postulated that Mg2+ restriction may be an additional stress encountered by intraphagosomal pathogens including Mtb [23 , 31 , 32] . The inability of perM::tn to replicate and survive in low Mg2+ raised the possibility that the persistence defect in vivo might follow depletion of intraphagosomal Mg2+ in activated macrophages . We infected resting and IFN-γ-activated macrophages with wt , perM::tn and the complemented mutant following growth in high ( 2 mM ) and reduced ( 250 μM ) Mg2+ . The mutant displayed a growth defect in resting macrophages , which was larger when it was pre-cultured in reduced magnesium . Survival of perM::tn in IFN-γ-activated macrophages was impaired in comparison to wt and complemented mutant , but only following pre-culture in reduced magnesium ( S4 Fig . ) . We examined whether perM::tn was more susceptible than wt Mtb to a range of stresses in vitro , including exposure to hydrogen peroxide , lysozyme , detergent , acidified sodium nitrite , free fatty acid , zinc , and copper , as well as carbon starvation , iron depletion , and a multi-stress assay combining a fatty acid carbon source , reduced pH , hypoxia , and sodium nitrite ( S5 Fig . ) . The mutant survived at wt levels under all conditions , indicating that perM::tn does not have a general viability defect , but rather , appears to be specifically vulnerable to reduced Mg2+ . The increased Mg2+ requirement of perM::tn suggested a possible role for PerM in Mg2+ transport . However , analysis of total Mg2+ content by inductively coupled mass spectrometry ( ICP-QQQ ) showed no significant differences in strains grown in either high ( 2000 μM ) or reduced ( 250 and 25 μM ) Mg2+ ( S6 Fig . ) , suggesting that PerM is not required for Mg2+ acquisition . Given our data , along with work in Salmonella suggesting a redundancy of Mg2+ transporters that ensures significant Mg2+ uptake [33] , the persistent defect of perM::tn is unlikely the result of impaired Mg2+ transport . In the context of the host response , infection of mouse macrophages with Mtb pre-grown in reduced ( 500 μM ) Mg2+ media resulted in a 2-fold increase in TNF-α production by macrophages infected with perM::tn , but not wt or complemented strains ( Fig . 3D ) . This suggests that either the immunostimulatory component ( s ) of perM::tn are more highly produced , secreted , or shed in reduced Mg2+; or that Mtb growth in reduced Mg2+ leads to increased exposure of these components to macrophage pattern recognition receptors and induction of a proinflammatory response . To gain insight into the function of PerM , we compared the transcriptomes of wt and perM::tn Mtb grown in high ( 2000 μM ) and reduced ( 250 μM ) Mg2+ in three independent experiments , using a p-value of 0 . 05 and 2-fold cutoff to identify differentially regulated genes . In reduced Mg2+ , 41 genes were differentially expressed between strains , all of which except one were upregulated in the mutant ( Table 1 ) . Sixteen of these genes are annotated with predicted or possible roles in cell division and/or cell wall biosynthesis . Upregulation of a subset of these genes was confirmed by qRTPCR analysis ( Fig . 4A ) . Genes listed were differentially expressed at least 2-fold in perM::tn compared to wt grown for 5 days in media supplemented with 250 μM Mg2+ . Fold change values are averages of three independent experiments , P<0 . 05 . Annotations adapted from TB Database ( tbdb . org ) , TubercuList ( tuberculist . epfl . ch ) and PATRIC ( patricbrc . org ) . FC , fold change in perM::tn compared to wt . Genes also regulated greater than 2-fold between strains in 2000 μM Mg2+ are marked with * . Cell division genes more highly expressed in the mutant compared to wt under reduced Mg2+ included ftsK and xerC , involved in chromosome segregation; ftsI , necessary for peptidoglycan crosslinking during division; and ftsW , whose product likely translocates peptidoglycan precursors across the cell membrane [34] and interacts with both FtsI as well as cell division initiator FtsZ in mycobacteria [35] . Also upregulated in the mutant were genes encoding four putative penicillin binding proteins ( FtsI , DacB1 , Rv2864c , and Rv1433 ) , enzymes which carry out the transpeptidation necessary for crosslinking of cell wall peptidoglycan strands; Rv3717 , a possible peptidoglycan amidase with a role in cell wall remodeling; and Rv0519c , a possible mycolyltransferase involved in mycolic acid processing [36] . Secreted fibronectin-binding protein C ( FbpC ) , a possible trehalose mycolyltransferase thought to have both antigenic and cell wall biosynthesis roles , also showed increased expression in the mutant . Notably , expression of ftsZ , encoding the cytosolic , tubulin-like initiator of cell division , was not increased in the mutant at either Mg2+ concentration , nor was expression of genes in the cell wall biosynthetic gene cluster ( rv3779-rv3809c ) contributing to mycolic acid , arabinogalactan , and LAM synthesis [37] , pointing towards a specific response rather than a global induction of all cell division and cell wall biosynthesis genes in the mutant . Seven genes were upregulated in the mutant compared to wt in both high and reduced Mg2+ , with more pronounced differences in expression between strains in reduced Mg2+ ( Tables 1 , S1 ) , suggesting that Mg2+ reduction exacerbates differential transcriptional responses that are also present in high Mg2+ . Comparison of gene expression in wt Mtb in reduced versus high Mg2+ revealed only two genes meeting the 2-fold cutoff: pe20 was upregulated in reduced Mg2+ and fadD5 was downregulated ( S2 Table ) . This transcriptional response was far less pronounced than that previously identified consisting of 24 genes differentially regulated in wt Mtb grown in media with or without Mg2+ [22] , suggesting that the transcriptional response to Mg2+ starvation in wt Mtb was not triggered at 250 μM Mg2+ , used in our experiment . The gene expression pattern of perM::tn in 250 μM Mg2+ ( S2 Table ) did not resemble Mg2+-starved wt Mtb [22] , contrary to what might be expected if Mg2+ uptake were impaired in the mutant . The increased expression of cell division and cell wall biosynthesis genes in the mutant suggested a possible defect in these processes . To examine the impact of perM disruption on cell morphology , Mtb was grown in a range of Mg2+ concentrations , fixed , and imaged by scanning electron microscopy ( SEM ) . PerM::tn exhibited Mg2+-dependent defects in morphology and division . Median cell length increased as the concentration of Mg2+ decreased , and some mutant bacilli exhibited bulging at the poles in reduced Mg2+ ( Fig . 4B , C ) . To examine localization of PerM , GFP-tagged Mtb PerM protein was expressed in wt Mycobacterium smegmatis , a non-pathogenic species closely related to Mtb and itself containing an PerM homolog with 73% identity . Mtb requires containment within a biosafety level 3 facility , which prevented us from performing live cell imaging experiments in Mtb . Live cell imaging of recombinant M . smegmatis revealed that PerMMtb localized to the membrane and it accumulated at the mid-cell division site ( Fig . 5 ) , similar to mycobacterial cell division proteins , such as FtsI and FtsZ , as well as peptidoglycan synthesis enzymes , such as penicillin binding protein 1 [38–40] . We next compared sensitivity of perM::tn and wt Mtb to a range of compounds targeting cell wall biosynthesis , as well as drugs with other established targets . The majority of compounds assayed exhibited a similar minimum inhibitory concentration ( MIC ) in wt and perM::tn ( Table 2 and S7 Fig . ) , with a shift of 2-fold or less considered insignificant . However , perM::tn was acutely sensitive to growth inhibition by β-lactam antibiotics , which target penicillin binding proteins that carry out the transpeptidation reaction resulting in crosslinking of cell wall peptidoglycan , a final step in peptidoglycan synthesis . The shift in MIC was most pronounced for cephalexin and piperacillin , β-lactams that specifically inhibit FtsI , the transpeptidase required for peptidoglycan crosslinking during bacterial cell division [41–43] . β-lactamase activities in wt and perM::tn were not significantly different ( S3 Table ) excluding the possibility that impaired β-lactamase activity caused the mutant’s increased susceptibility to β-lactams . Notably , the MICs of vancomycin and D-cycloserine , which inhibit earlier steps in peptidoglycan synthesis than do β-lactams , were similar for wt and perM::tn . Furthermore , there was little to no shift in MIC of isoniazid and ethambutol , which inhibit production of other cell wall components ( mycolic acids and arabinogalactan , respectively ) , indicating that the perM::tn is not broadly hypersusceptible to interference with cell wall biosynthesis . Minimum inhibitory concentration ( MIC ) of various drugs against wt and perM::tn Mtb . MIC90 values in μg/mL , determined by minimum concentration at which OD580 was less than 10% that of untreated control . FC , fold change reduction of perM::tn MIC compared to wt MIC .
This work implicates a novel mycobacterial membrane protein in cell division and demonstrates its requirement for Mtb persistence in vivo . The persistence defect of the PerM mutant is one of the most dramatic per phenotypes observed to date , and to our knowledge the first noted in a mutant of an Mtb membrane protein . Global gene expression profiling revealed increased expression of cell division and cell wall biosynthesis genes in the mutant , and these increases exacerbated during growth in reduced Mg2+ . Several additional observations support the hypothesis that PerM plays a role in cell division . First , the mutant elongated in reduced Mg2+ , with additional morphological changes at very low Mg2+ . Second , the mutant exhibited hypersusceptibility to β-lactam antibiotics , which inhibit the enzymes necessary for crosslinking of cell wall peptidoglycan . In particular , the mutant was hypersusceptible to piperacillin and cephalexin , β-lactams that specifically target the cell division-associated peptidoglycan transpeptidase , FtsI [42–44] . Third , PerM localized to the mid-cell region in M . smegmatis , similarly to previously studied mycobacterial proteins involved in cell division and peptidoglycan biosynthesis [38–40] . The mutant hyperstimulated mouse macrophages ex vivo , a phenotype exacerbated after culture in reduced Mg2+ , which may be related to shedding of cell wall components during a compromised cell division process . The inability of perM::tn to replicate and survive at low Mg2+ suggested that PerM may play a role in Mg2+ acquisition , could be necessary for the adaptive response of Mtb to low Mg2+ , or that Mg2+ might serve a compensatory function to mask physiological defects caused by the absence of PerM . We examined the first possibility by ICP-QQQ analysis , which revealed perM::tn and wt Mtb to contain the same total Mg2+ , even when grown in reduced Mg2+ media . Furthermore , gene expression data from the mutant showed a regulation pattern distinct from that of Mg2+-starved wt Mtb [22] . The second possibility , that PerM is a component of the bacterial response to low Mg2+ , is similarly not supported by the gene expression profile of wt Mtb grown in low Mg2+ [22] . However , it is possible that PerM , constitutively expressed , is required for a successful adaptive response to Mg2+ starvation , perhaps through interaction with Mg2+ response proteins . Future protein interaction studies may shed light on this question . Our work supports the third possibility , that Mg2+ serves a compensatory function in the mutant through stabilization of a weakened cell envelope; in particular , our data suggest that the mutant cell envelope may be especially vulnerable during cell division . While the role of Mg2+ in cell wall stability is widely acknowledged , the mechanism by which this occurs is not entirely clear . In Salmonella , outer membrane permeability decreased in high Mg2+ , and a phoP Salmonella mutant with lipopolysaccharide alterations displayed increased permeability and susceptibility to numerous antibiotics in low Mg2+ , but behaved like wt Salmonella when Mg2+ was high [45] . This suggests a role for Mg2+ in stabilizing the outer membrane , perhaps through interaction with negatively-charged lipopolysaccharide . In the Gram-positive B . subtilis , which lacks an outer membrane , high Mg2+ partially suppressed the growth defect of a mutant lacking teichoic acid suggesting that Mg2+ might be able to compensate for loss of teichoic acid in the cell wall [46] . On the other hand , high concentrations of Mg2+ may serve to stabilize an otherwise vulnerable peptidoglycan sacculus . B . subtilis mutants lacking MreB , RodB and PonA—proteins thought to be involved in peptidoglycan synthesis—display morphological and growth defects that were rescued by high Mg2+ [47–49] . Of note , peptidoglycan synthesis decreased and peptidoglycan precursors accumulated in Mg2+-deprived B . subtilis [50] , and in Salmonella , lipid A acylation increased in response to Mg2+ deprivation [20] , suggesting that the influence of Mg2+ on peptidoglycan integrity may occur by several mechanisms , both structural and regulatory . It has also been proposed that Mg2+ might affect the degree of peptidoglycan crosslinking that occurs; stabilize or regulate important cell-wall synthases or hydrolases; or serve to stiffen the cell envelope [45 , 51] . The upregulation of cell division genes in perM::tn , combined with the hypersensitivity of the mutant to specific inhibitors of FtsI , suggests a role for PerM in peptidoglycan synthesis or remodeling during cell division . The perM mutant was not hypersusceptible to all peptidoglycan synthesis inhibitors: the MICs of vancomycin and cycloserine were similar for mutant and wt Mtb . Cycloserine , an analog of D-alanine , blocks synthesis of cytoplasmic peptidoglycan precursors [52] , while vancomycin prevents both the early transglycosylation step necessary for incorporation of peptidoglycan monomer into the sacculus , as well as the final crosslinking of peptidoglycan by transpeptidases [53] . The specific vulnerability of perM::tn to β-lactams , which target the transpeptidation step , suggests that PerM may play a role in late peptidoglycan biosynthesis during cell division . Interestingly , a conditional mutant of ripA , which encodes an essential mycobacterial peptidoglycan hydrolase , was similarly hypersusceptible to a β-lactam , carbenicillin , but not to cycloserine following ripA depletion [54] . While its specific mechanism of action remains to be determined , it is plausible that PerM , as an integral membrane protein with 10 transmembrane helices , could serve a structural role , recruiting or anchoring key cell division proteins , such as peptidoglycan transpeptidases or hydrolases , to the division site . It may serve to bridge cytoplasmic proteins , such as FtsZ or early peptidoglycan synthesis machinery , with cell division proteins in the periplasm , or it could be involved in transport of cell envelope components . The perM mutant withstood numerous stresses in vitro , including reactive oxygen and nitrogen species , cell wall-perturbing detergent , and carbon starvation , showing a specific vulnerability to a low-Mg2+ environment . Surprisingly , the perM mutant was not more susceptible than wt to exposure to arachidonic acid at pH 5 . 5 , despite its sensitivity to Tween-80 at pH 4 . 5 , which suggested that free oleic acid might be toxic to the mutant at low pH . It is plausible that oleic acid released from Tween-80 and arachidonic acid cause toxicity by different mechanisms . In addition , the lower pH of the Tween-80 containing medium may have contributed to the enhanced killing of the mutant . Survival of perM:tn in IFN-γ activated macrophages was impaired , when the bacteria were pre-grown in reduced Mg2+ . IFN-γ activated , Mtb infected macrophages have a limited lifespan ex vivo , which prevented extending the time course of the ex vivo infection to better mimic the mouse infection . It is possible that pre-growth in reduced Mg2+ has the same impact as replication in the acute phase of mouse infection , but interpretation of the ex vivo macrophage infection data is difficult and does not allow direct conclusions about the intraphagosomal availability of Mg2+ . Previous work revealed that macrophage activation by IFN-γ results in changes in the intraphagosomal concentrations of several metals , but Mg2+ was not measured [55] . The Salmonella-containing phagosome was estimated to contain 10 to 50 μM Mg2+ , based on strong induction of the Mg2+-regulated mgtB gene in Salmonella in both low Mg2+ media and upon uptake by mammalian cells [56 , 57]; however , measurement of intraphagosomal Mg2+ using nanosensor particles showed the concentration to be approximately 1 mM in the first two hours of infection [58] . The intraphagosomal concentration of Mg2+ in vivo , after days or weeks of Mtb infection , remains a topic of speculation . Our work lends support to the hypothesis of an Mg2+-depleted environment in the Mtb-containing activated macrophage in vivo . Unfortunately , measuring intraphagosomal Mg2+ concentrations is extremely challenging . Purification of Mtb infected phagosomes is difficult and it is unknown if the purification process alters phagosomal ion concentrations . Fluorescent Mg2+ reporters exhibit much higher affinity for Ca2+ and also bind Zn2+ , while PEPPLE ( probe encapsulated by biologically localized embedding ) technology suffers from low magnesium affinity [59] . Future development of novel and better sensors for magnesium is required to overcome these obstacles . The PerM mutant stimulated a hyperinflammatory cytokine response in infected macrophages ex vivo . While we did not detect elevated cytokine levels in lungs of mice infected with the perM mutant compared to wt-infected mice , we cannot rule out the possible contribution of a hyperinflammatory response to the persistence defect . The cytokine measurements might have been confounded by differences in bacterial loads , and even a small , perhaps difficult to quantify , difference in the host immune response could synergize with Mg2+ restriction to result in killing of perM::tn; or the response may be localized , with lesion-centric inflammation contributing to killing perM::tn , but little impact on total cytokine levels in the lungs . Growth of Mtb in reduced Mg2+ prior to macrophage infection resulted in an augmented response to the mutant , but no difference in response to wt Mtb , suggesting that cell wall instability of the mutant may contribute to the hyperinflammatory phenotype . In light of other evidence linking PerM to cell division , it is plausible that the mutant sheds multiple components of the cell wall during a stalled or otherwise compromised division process , resulting in increased stimulation of macrophage response pathways . The bacterial cell wall is the target of many drugs in current use . The remarkable sensitivity of perM::tn to cephalexin and piperacillin , antibiotics routinely and safely used in clinical practice , suggests an exciting possibility of PerM as a co-target . An inhibitor of PerM could potentially be used to sensitize Mtb to β-lactam antibiotics , extending their use to mycobacterial infections .
Mouse studies were performed following National Institutes of Health guidelines for housing and care of laboratory animals and performed in accordance with institutional regulations after protocol review and approval by the Institutional Animal Care and Use Committee of Weill Cornell Medical College ( protocol # 2008–0006 , pH homeostasis in Mycobacterium tuberculosis ) . PerM::tn , the Mtb H37Rv transposon mutant of gene perM ( rv0955 ) containing a ΦMycoMarT7 transposon insertion at nucleotide 701 , was isolated in a screen for acid-sensitive mutants described previously [13] . Mtb strains were grown in a humidified incubator at 37°C with 5% CO2 in Sauton’s media with 0 . 05% Tween 80 or 0 . 05% tyloxapol; Middlebrook 7H9 medium ( Difco ) containing 0 . 2% glycerol , 0 . 5% bovine serum albumin , 0 . 2% dextrose , 0 . 085% NaCl , and 0 . 05% Tween 80; or Middlebrook 7H11 agar ( Difco ) containing 10% OADC supplement ( Becton Dickinson ) and 0 . 5% glycerol . Nominally magnesium-free Sauton’s media was prepared with 0 . 8 mM citric acid , 9 mM sodium citrate , 3 mM potassium phosphate , 30 mM L-asparagine , and 6% glycerol , chelated overnight with 20 g/L Chelex 100 resin ( Bio-Rad ) , filtered to remove Chelex , supplemented with 0 . 2 mM ferric ammonium citrate and 5 μM zinc sulfate , and adjusted to pH 7 . 4 . Before use , 0 . 05% Tween 80 and 2 mM MgCl2 were added unless otherwise indicated . We call this medium “nominally magnesium-free” as trace residual magnesium is likely present . Hygromycin B ( 50 μg/ml ) , kanamycin ( 15 μg/ml ) and streptomycin ( 20 μg/ml ) were included when required for selection . Rv0955 was PCR amplified from H37Rv genomic DNA and cloned behind the hsp60 promoter into a plasmid that integrates into the chromosomal phage integration attL5 site . For localization studies , GFP was fused to the C-terminus of Rv0955 and expressed from the hsp60 promoter on an integrative plasmid . Female C57BL/6 , or IFN-γ-/- mice ( Jackson Laboratory ) were infected using an inhalation exposure system ( Glas-Col ) with early-log-phase Mtb to deliver approximately 100 bacilli per mouse . Bacterial numbers were enumerated by plating serial dilutions of lung or spleen homogenates on 7H11 agar plates for CFU . Upper left lung lobes were fixed in 10% buffered formalin , embedded in paraffin and stained with hematoxylin and eosin . Bone marrow derived macrophages were harvested and differentiated as previously described [13] and seeded at 4x106 cells/mL , with or without 50 ng/mL murine IFN-γ ( R&D Systems ) . Approximately sixteen hours later , macrophages were infected at a multiplicity of infection ( MOI ) of 0 . 1 with a single cell suspensions of log-phase Mtb grown for 6 days in 250 or 2000 μM MgCl2 . Monolayers were washed with PBS 4 hours post-infection to remove extracellular bacteria . After 4 hours , 3 days , or 6 days , macrophages were lysed with 0 . 5% Triton X-100 and bacteria were enumerated by plating serial dilutions on 7H11 agar plates . Half of the media in each well was replaced with fresh media after 3 days . Bone marrow derived macrophages from C56BL/6 or TLR2-/- mice ( Jackson Laboratories ) were harvested and differentiated as previously described [13] . Immortalized macrophage cell lines from wild type , and Nod1/2-/- mice [60] were a gift from M . A . Kelliher at the University of Massachusetts . Macrophages were seeded at 4x105 cells/ml ( wt macrophages ) or 6x105 cells/ml ( knockout macrophages ) . After 16 hours , they were infected at the indicated MOI with a single cell suspension of log-phase Mtb . For experiments using dead bacteria , Mtb was fixed in 10% formalin for 16 hours , washed twice in PBS , and added to Mtb at an MOI of 20 . For exposure of macrophages to Mtb-conditioned culture media , Mtb was grown for 8 days in detergent-free Sauton’s media containing 2 mM MgCl2 , then culture supernatant was passed through a 0 . 2 μm filter , concentrated approximately 10-fold in Amicon Ultra-15 Centrifugal Filter Units ( Millipore ) , and added to macrophages at a volume equivalent to 10 μg protein . Supernatants were collected after 24 hours , passed through a 0 . 2 μm filter , and stored at -80°C . Cytokine levels were quantified using BD OptEIA ELISA kits for mouse TNF or IL-12p40 ( BD Biosciences ) , or a multiplex ELISA Mouse ProInflammatory 7-Plex Tissue Culture Kit ( Meso Scale Discovery ) . Tissue processing , RNA isolation , and real-time PCR were performed as previously described [61] . Mtb was grown to log phase in Sauton’s media containing 2000 μM MgCl2 prior to each experiment . Single cell suspensions were prepared in assay medium by centrifugation at 800 rpm for 12 minutes , then diluted to OD 0 . 02–0 . 05 and incubated in the following conditions: 3 days in pH 4 . 5 in media containing 0 . 05% Tween 80 or tyloxapol; 5 weeks in PBS with 0 . 05% tyloxapol; 24 hours in 7H9 media with 0 . 05% Tween 80 and 2 . 5 mg/ml lysozyme; 5 hours in 7H9 media with 0 . 05% Tween 80 and 0 . 1% SDS; 3 days in 7H9 media at pH 5 . 5 with 0 . 05% tyloxapol and 5 mM NaNO2; 3 days in 7H9 media at pH 5 . 5 with 0 . 05% tyloxapol and 50 μM arachidonic acid; 3 hours in 7H9 media containing 10 mM H2O2 . For the multi-stress survival assay , Mtb was incubated in 1% oxygen for 14 days in modified Sauton’s media at pH 5 . 5 containing 0 . 05% tyloxapol , 0 . 05% butyrate , 0 . 5 mM sodium nitrite , 2000 μM MgCl2 , and without glycerol . The exposure times to different stress conditions were selected so that viability of wt Mtb was reduced by approximately 5- to 10-fold . For conditions in which wt Mtb survived without significant or very slow death ( carbon starvation , multi-stress model ) extended incubation times were chosen . To determine viability , serial dilutions of cultures were plated on 7H11 plates . Mtb was grown to log phase , washed twice in assay medium , and diluted to OD580 0 . 02 in plates containing two-fold serial dilutions of MgCl2 , MgSO4 , ZnCl2 , MnCl2 , CaCl2 , CuCl2 , or ferric ammonium citrate . For experiments testing various cations as substitutes for Mg2+ , a basal level of either 100 or 250 μM MgCl2 was added as indicated . For experiments involving ZnCl2 or ferric ammonium citrate , modified Sauton’s medium was prepared without the respective cation . Mtb was washed twice in nominally Mg2+-free Sauton’s media , diluted to OD 0 . 1 in Sauton’s media containing 250 or 2000 μM added MgCl2 . After 5 days , cultures were washed twice in PBS with 0 . 05% Tween 80 . To determine the impact of very low Mg2+ , cultures were grown in Sauton’s media containing 2000 μM added MgCl2 until mid-log phase , then washed twice in nominally Mg2+-free Sauton’s media and incubated in Sauton’s media containing 25 μM added MgCl2 . Pellets were collected at 0 hour , 3 hours and 10 hours post inoculation . After normalizing for biomass , pellets were heated at 80°C for 1 hour to kill Mtb , then resuspended in 200 μL 70% nitric acid , trace element grade ( Fisher ) and heated at 80°C for 2h before ICP-QQQ processing and analysis . Samples were analyzed on an Agilent 8800 ICP-QQQ running in MS/MS mode . Instrument daily performance qualification and method specific tuning was achieved by the expert AutoTune function of the MassHunter software ( B . 01 . 02 ) . Typical sample introduction parameters for direct injection were used; RF Power 1550W , sample depth 8 mm , carrier gas 0 . 95 L/min , and dilution gas was set at 0 . 15 L/min . These parameters resulted in an oxide ratio of 0 . 8% ( CeO/Ce ) . Prior to analysis , samples were diluted to a final volume of 2 ml and analyzed against multi-element external calibration standards ( Agilent , Wilmington , DE ) . NIST 1643e was used as a standard reference material for calibration verification and monitor any possible drift during the analytical run . Mtb was grown standing for 5 days in Sauton’s media containing 250 or 2000 μM MgCl2 and 0 . 05% Tween 80 . Flasks were shaken for 5 hours prior to harvest . Cultures were mixed with an equal volume of GTC buffer containing guanidinium thiocyanate ( 4 M ) , sodium lauryl sulfate ( 0 . 5% ) , trisodium citrate ( 25 mM ) , and 2-mercaptoethanol ( 0 . 1 M ) and pelleted by centrifugation . Bacterial RNA was isolated as previously described [62] . For microarray experiments , RNA was labeled using a Low Input Quick Amp Labeling Kit ( Agilent ) . Microarrays were custom-designed ( Genotypic Technology , Bangalore , India ) . Analysis was performed using Agilent GeneSpring software . The complete Microarray data sets have been submitted to the Gene Expression Omnibus ( GEO ) database . For gene expression analysis by quantitative real-time PCR , cDNA was generated using MuLV Reverse Transcriptase ( Invitrogen ) and quantified using Roche Light Cycler 480 Real-Time PCR System with primers and TaqMan probes designed using Primer3 ( http://bioinfo . ut . ee/primer3-0 . 4 . 0 ) . Primer and probe sequences are available upon request . Mtb was grown for 5 days in Sauton’s media containing 25 , 250 , 500 , or 2000 μM MgCl2 and 0 . 05% Tween 80 before fixation , processing , and imagining by scanning electron microscopy as previously described [13] . Cell lengths were measured using Adobe Photoshop software . Mtb was grown to early log phase and diluted to an optical density of 0 . 02 in Sauton’s medium containing 2 mM MgCl2 and 0 . 05% Tween 80 . Bacteria were then exposed to twofold dilutions of piperacillin , cephalexin , ampicillin , meropenem , rifampicin , polymyxin B , DCCD , vancomycin , D-cycloserine , streptomycin , chloramphenicol , isoniazid , and ethambutol ( Sigma-Aldrich ) . For assays of meropenem , DCCD , isoniazid , chloramphenicol , and rifampicin , all wells contained 0 . 5% DMSO . For the assay of cephalexin , all wells contained 4 mM NH4OH . The MIC was recorded as the minimum concentration at which growth , measured by optical density ( OD580 ) , was inhibited by at least 90% , as compared to a control containing no antibiotic , after approximately 2 weeks . M . smegmatis expressing PerM-GFP was sealed in B04A microfluidic flow chamber plates ( Cell Asic , part of EMD Millipore ) and perfused with Middlebrook 7H9 broth at 37°C . Cells were visualized by fluorescence microscopy using an inverted Olympus IX-70 microscope equipped with a GFP filter set , a Photometrics CoolSnap QE cooled CCD camera , and an Insight SSI 7 color solid state illumination system . Snapshots were captured every 15 minutes . The chromogenic cephalosporin nitrocefin ( Fisher ) was used to assay ß-lactamase activity as previously described [63] in whole-cell lysates of Mtb saturated cultures grown in Sauton’s media with 2 mM MgCl2 .
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The success of Mycobacterium tuberculosis ( Mtb ) as a human pathogen is due to ability to persist in chronic infection , despite a robust adaptive immune response by the host . The mechanisms by which Mtb achieves this are , however , poorly understood . Here we show that a novel integral membrane protein , Rv0955/PerM , is essential for Mtb persistence during chronic mouse infection . The perM mutant required increased magnesium compared to wild type Mtb for replication and survival in culture and elongated in media with reduced magnesium concentration . Transcriptomic , electron microscopy and live cell imaging approaches provided evidence that PerM is involved in cell division . The survival defects of the perM mutant in reduced magnesium and during chronic mouse infection are consistent with the hypothesis that magnesium deprivation constitutes an IFN-γ dependent host defense strategy . This work also has potential clinical implications , as disruption of PerM renders Mtb susceptible to β-lactam antibiotics , which are commonly used to treat non-mycobacterial infections .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
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Disruption of an M. tuberculosis Membrane Protein Causes a Magnesium-dependent Cell Division Defect and Failure to Persist in Mice
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Most empirical and theoretical studies have shown that sex increases the rate of evolution , although evidence of sex constraining genomic and epigenetic variation and slowing down evolution also exists . Faster rates with sex have been attributed to new gene combinations , removal of deleterious mutations , and adaptation to heterogeneous environments . Slower rates with sex have been attributed to removal of major genetic rearrangements , the cost of finding a mate , vulnerability to predation , and exposure to sexually transmitted diseases . Whether sex speeds or slows evolution , the connection between reproductive mode , the evolutionary rate , and species diversity remains largely unexplored . Here we present a spatially explicit model of ecological and evolutionary dynamics based on DNA sequence change to study the connection between mutation , speciation , and the resulting biodiversity in sexual and asexual populations . We show that faster speciation can decrease the abundance of newly formed species and thus decrease long-term biodiversity . In this way , sex can reduce diversity relative to asexual populations , because it leads to a higher rate of production of new species , but with lower abundances . Our results show that reproductive mode and the mechanisms underlying it can alter the link between mutation , evolutionary rate , speciation and biodiversity and we suggest that a high rate of evolution may not be required to yield high biodiversity .
The impact of sexual reproduction on the rate of evolution could stand as one of biology's grand achievements [1]–[4] . Does sex speed genetic divergence , speciation , and thus increase the world's diversity relative to asexual reproduction ? An immediate difficulty with any theory is how to define speciation in asexual organisms , where Mayr's Biological Species Concept [5] does not easily apply [6] , [7] . Nevertheless , asexual organisms do diversify and are assigned species names [8]–[12] , and many observations and experiments describe speciation in sexual as well as asexual organisms . Much work emphasizes ecological divergence and speciation [13]–[17] , but we propose to step back and ask basic questions about the dynamics of divergence and extinction , and how it depends on sexual reproduction . Before we understand the full impact of sex on evolution and diversity in an ecologically complex world , we need to understand well the basic dynamics of mutation , gene flow , drift and extinction underlying the process of speciation . Sex increases the rate of evolution [18]–[24] , although evidence of sex constraining genomic and epigenetic variation and slowing down evolution also exists [25]–[27] . Given these contrasting impacts of sex , the effects of reproduction mode on patterns of diversification , extinction and consequent species diversity are hard to predict , even without ecological opportunity . We here pose a basic question to connect the dynamics of sexual and asexual populations with biodiversity patterns: do sexually reproducing populations have similar biodiversity patterns as asexual populations in the absence of ecological differentiation , given equal mutation and identical definitions of the genetic divergence required for speciation ? How do mutation , genetic drift , ecological drift , and gene flow act in sexual , versus asexual , populations to produce diversity ? Research on diversification of species often emphasizes the process of genetic divergence , but extinction rates are also critical . Even in the absence of selection , the dynamics of diversification and diversity may thus be influenced by mutation , genetic drift , sexual recombination , colonization , as well as population size and its role in ecological drift and extinction [17] . Other than the direct impact of sexual recombination on genetic divergence , are other aspects of the dynamics of evolution the same in sexual and asexual populations ? We take here a theoretical approach to the genetics of speciation [28]–[32] in the context of neutral biodiversity theory [33] . Our goal is to model the emergence of new species using explicit genetic rules on a backdrop of individuals whose births and deaths determine abundances and extinction . This genetic model of speciation extends existing neutral models of community diversity [34]–[44] so that speciation , extinction , abundance , the population size of new species , and diversity emerge from assumptions on genetic divergence , and genetic and ecological drift . Modeling speciation as a neutral process is unrealistic , but this simplification may serve as a useful null model to compare speciation rate and species diversity in asexual vs . sexual communities , minimizing the number of assumptions about population-level patterns of speciation and extinction . To understand the effect of reproductive mode on patterns of diversification and species diversity , we need a model describing the dynamics of genes within populations within a model of populations within a community , and we need a definition of speciation that applies to sexual and asexual populations [8] , [45] . Our definition of species is used in the context of a population whose genomes diverge in a spatial landscape . In the model , a community without deme structure and no ecological differentiation [46] has individuals , and the geographic distance between each pair of individuals and is given by ; is the geographic distance matrix containing all the values . All individuals have identical and ( essentially ) infinite genomes of nucleotides at the outset ( see “Material and Methods” and “Table 1” for a summary of the mathematical terms used in this analysis ) . The genetic similarity , , between each pair of individuals and can be represented by a genetic similarity matrix , . At time 0 , all elements are , but there is a constant mutation rate per nucleotide per birth-death cycle , so the community evolves divergence under the combined influences of recombination in sexual populations or asexual reproduction , but not both , mutation , migration , and genetic drift . We assume asexual individuals are strictly asexuals , with no horizontal gene transfer . In sexual populations , pairs mate and exchange sections of their genomes . In both models , dispersal and colonization is incorporated because offspring appear near their parents , and mating is among neighbors . The crucial , final feature needed to make this a model of speciation is a minimum genetic similarity threshold , [47] , [48]: two individuals and for which are sufficiently different to be called different species . In the sexual population , this means those two individuals cannot mate; in the asexual community , it has no impact on dynamics for the obvious reason that there is no mating . In both cases , we imagine that a biologist observing these two individuals would be inclined to describe them as different species; in the sexual case , the same biologist would detect sufficient genetic incompatibilities that offspring would be inviable [49]–[53] , [53 , 54] . Defining critical divergence for a pair of individuals , however , is not yet a species definition , because species boundaries are a property of entire populations [31] , [53] , [55] . A species is defined as a connected component in a evolutionary graph: a group of individuals for which there is a path of genetic compatibility connecting every pair ( Fig . 1 ) . This means that two individuals in sexual populations can be conspecific while also being incompatible , as long as they can exchange genes indirectly through other conspecifics ( a ring species [56] ) . Using this definition , speciation will occur whenever the expected mean genetic similarity of the matrix at equilibrium reaches [28] , [57]; intuitively , this is straightforward: with mutation too low , so , the community reaches an equilibrium similarity , , so speciation does not start ( pp . 305 , [57] ) . In summary , the process of diversification starts with an initial phase during which genetic similarity gradually declines toward an equilibrium , . Individuals become more and more divergent from one another , particular those further away in space . Eventually , two clusters are formed with the special property that there is not a single individual in one that is compatible with any individual in the second: they form two species ( Fig . 1 ) . This is permanent , for once segregated , because given a very large genome with nucleotides , the universe of possible genome configurations is so large relatively to population size that it is essentially impossible for compatibility of genomes to become reestablished . The divergence process continues until each of those clusters divides further , and so on . Each speciation event leads to a loss of divergence within species , followed again by increasing divergence until another speciation event . Thus , genetic similarity within species is blocked from ever falling ( much ) below by the speciation process . But there is still more to the dynamics of diversification , due to extinction . Once we assume species formation , we must include ecological drift – random fluctuations of species abundances within a fixed metacommunity size – as an influence on dynamics , in addition to reproductive mode , mutation , migration and genetic drift . Species may start rare , or become rare due to drift , and then go extinct , and speciation should eventually be balanced by extinction , exactly as the neutral model of diversity describes [33] . We now examine this model in detail to ask whether sexual or asexual populations ( and metacommunities ) give rise to faster diversification or more species , considering the equilibrium , at which speciation and extinction have reached a balance , as well as the transient increase in diversity after a founding event . First , a theoretical analysis of the divergence process leads to important assertions about the speciation rate and how it relates to the mutation rate in sexual versus asexual communities . The full spatial model in which ecological drift controls the equilibrium diversity requires simulations , and we ran models with a wide variety of parameter combinations in order to answer the main questions: 1 ) Do species appear faster in a sexual or an asexual population ? 2 ) Is species richness higher at equilibrium in sexual or asexual communities ? 3 ) Are species abundances different , so does ecological drift play an important role in the extinction rate ?
We first consider analytically the time course of differentiation and speciation by considering the number of steps in a chain of descendents until the threshold of genetic divergence is reached . That is the stage at which a descendent is incompatible with a founding ancestor: speciation can only happen after that . Consider asexual populations and examine one individual and its descendants . Denote successive individuals , … , , where is the offspring of . We determine the number of steps until a descendant is sufficiently different from to be incompatible , so . Genetic similarity between and after the first offspring ( following equation 18 in “Material and Methods” ) is ( 1 ) where is mutation rate; after offspring , it is ( 2 ) The critical step , where , is therefore ( 3 ) A curious mutation rate is the one which produces a new cluster or species after the first offspring , so , a rate so high that offspring are “hopeful monsters”: different species from their parents [58] . We call this “mutation-induced” speciation , but such a model makes little biological sense . The model produces species with a different mechanism with much lower mutation rates: “fission-induced” speciation . Imagine a chain of descendants , … , in which every individual is alive . Ignoring the “hopeful monsters” of mutation-induced speciation , the entire chain belongs to the same cluster or species , even if and are distinct enough to be incompatible . After enough time , however , intermediate steps in the chain die , and eventually a subchain , … , is entirely dead . Once a subchain of consecutive steps , with , dies , the survivors in the chain become two separate species . Obviously , at some point there is a single critical individual whose death breaks the single cluster into two clusters – the last of the individuals in the subchain to die . With fissioning of genetic clusters , new species need not be singletons . Indeed , there is no upper limit on the abundance of a new species ( the parent population size is the upper limit ) . Incipient population size should depend on , and thus the minimum genetic similarity value , , that defines a species , and mutation rate ( ) : the higher , the more time it will take before we have a new cluster formation . With higher , we thus anticipate lower speciation rate , but lineages may have higher incipient abundances and thus be less prone to extinction . In an earlier work , we examined the dynamics of the number , , in sexual populations in the absence of a limited geographical distance for mating ( ) [32] . The critical number of steps where in a panmitic population with sexual reproduction is: ( 4 ) The extra term in the denominator compared to equation 3 reflects genetic difference between mated pairs , and thus genetic dissimilarity between offspring and parents beyond mutation . Equations 3 and 4 suggest that there should be a monotonic relationship between and the speciation rate . New species will form at the rate at which chains of length die . Comparing equations 3 and 4 , we observe that in all cases , so . With sex , it takes fewer steps , , before a descendent passes the critical genetic similarity , , relative to its ancestor and this should lead to a lower speciation rate in an asexual metacommunity at a given mutation rate . Simulations confirm this assertion . Soon after founding , the metacommunity with sexual reproduction produces species more rapidly at a given mutation rate , , than the asexual case ( Fig . 2a ) , but lineages have lower abundances and thus are more prone to extinction ( Fig . 2b ) . This pattern has strong consequences for species richness ( Fig . 3 ) . In the transient , at very high mutation rate , the number of species collapsed in both models ( Fig . 3b ) . At equilibrium , quite surprisingly , the opposite held , and the asexual model had higher number of species for low mutation rate values ( Fig . 3c ) . The sexual model was much less efficient at maintaining species despite the higher rate of species formation . At , for instance , there are 1–3 species in simulations with sex , compared to 2–10 species without sex ( Fig . 3c ) . These patterns remain the same after we compare the transient ( Fig . 3d ) and the steady-state ( Fig . 3e ) regardless of the maximum geographic distance for mating and dispersal , . This failure to maintain species richness in the sexual model could only have been due to extinction: at a given diversity , the sexual communities must have lost species at a higher rate than asexual communities . This would happen if incipient species abundances were more skewed in the sexual model . Fig . 4 shows that this is indeed the case . In sexual communities , there were more incipient species with low abundance in the transient ( red line , Fig . 4a ) and at equilibrium ( red line , Fig . 4b ) , very few highly abundant species , and many rare species ( Fig . 4c ) relative to asexual communities . This pattern remains qualitatively the same in all pairwise comparisons between sexual and asexual metacommunities with and ( Kolmorgorov-Smirnov test , ) .
In the present paper , we have explored a landscape population genetics model to understand the effect of reproductive mode on speciation and extinction rate and the connection between the abundance of new species and species richness . The approach uses processes of individual organisms with large genomes – birth , death , gene flow , mutation and genetic-ecological drift – to study macroecological patterns of biodiversity [28]–[32] . It allows a comparison of diversification rate and community diversity in sexual vs . asexual communities without recourse to any assumption about population-level patterns of speciation and extinction . By modeling speciation explicitly , genetic assumptions about the formation of species become necessary: in the present study , the constant mutation rate and threshold of genetic similarity defining the species boundary . These assumptions allow us to derive quantitative relationships between mutation rate , abundances , probability of extinction of new species , and species richness . For example , the number of species in a metacommunity increases monotonically with mutation rate in both sexual and asexual populations . But mutation alone cannot cause speciation , because the genetic similarity defining species is also essential , entering in the equations that drive the rate of species formation ( i . e . , and ) . The quantitative nature of the relationship between mutation rate , genetic similarity , and species formation is understood with : incipient population size and species richness are both functions of , and higher means more time between speciation events but also higher incipient abundances and lower extinction rate . Surprisingly , sexual populations , with low value and thus a high speciation rate , had greatly reduced species richness at equilibrium , relative to the asexual populations with otherwise similar processes . This highlights the importance of deriving the processes connecting the rate of evolution and incipient abundance – the number of individuals in newly formed clusters or species – because they both impact speciation [35] , [59] and the number of species that can coexist in metacommunities . Incipient species abundance was highly variable in both sexual and asexual populations , but especially so with sex . In the latter , newly formed species were often singletons and thus rapidly went to extinction . Most speciation events in nature are believed to have been driven by divergent selection and drift is thought to play a very small role [15] , [53] . But genetic and ecological drift can be strong contributors during speciation , especially in the early stages [17] . We have shown that a higher evolutionary rate in sexual populations does not guarantee more coexisting species , especially in the long term , because higher evolutionary rate may imply a lower abundance of new species and thus a higher extinction probability . Thus , even if drift plays a small role in driving differentiation and speciation , it can strongly influence the extinction dynamics driven by the low abundance of the incipient species in natural populations . How robust are these results after the addition of selection and ecological differentiation ? Sexual organisms might have faster rates of adapting to different ecological conditions [21]–[24] , [60] , because multiple beneficial mutations can spread simultaneously in the population [20] . This can trigger higher abundance and lower extinction probability in sexual populations because speciation is not being driven by mutation but rather by adaptations to ecological conditions . Sex can also constrain the rate of adaptation to new conditions [25] , [26] . For example , it removes major changes such as chromosomal rearrangements [27] , and in the process of finding a mate , it may increase the risk to predation or higher exposition to sexually transmitted diseases [61] , [62] . These processes may slow down the rates of evolution and speciation which , according to our results , may not necessarily decrease the number of species in the long term . Further research that connects genetic and ecological drift with selection in constant and fluctuating environments may shed light on the link between reproductive mode , the rate of speciation , the abundance of new species , extinction probability , and long term species richness [17] , [63] . A theory that covers the link between net diversification rates and biodiversity patterns in both sexual and asexual taxa is still lacking [17] , [21] , [64] , [65] , and our approach joining population genetics models with divergence criteria and macroecological patterns of biodiversity may be a way forward . One important advance would be to develop analytical relationships among the key parameters , particular mutation rate and the strength of selection in the context of several topologies of spatial networks , and subsequently spatial heterogeneity [29] , [42] , [57] , [66] , [67] . On the genetic side , more precise consideration of the mechanisms driving genome evolution , specifically in the context of rates of self-compatibility and outcrossing , might lead to different predictions about speciation and diversity [10] , [68]–[72] . We believe that our results may help to connect reproductive modes with the speciation rate in eco-evolutionary graphs , and the effect of the incipient species abundance on net diversification rate and extant diversity .
Models of DNA evolution based on simple base pair substitution have a long history ( i . e . , the infinite sites model , [73] , [74] ) , and several variants have been proposed [75] . More realistic extensions of those models include deletion , insertion , duplication and rearrangements of segments bases [70] . Recent models also take into account , as in the neutral theory of biodiversity [33] , instantaneous speciation but with explicitly evolving genomes ( i . e . , an identical copy of one root genome is made , each of the two genomes gets a new successor species name , and they each evolve independently thereafter , see [70] ) . In the models explored , the reproductive mode describes a population with evolving genomes . During asexual reproduction a mother is randomly selected while in the sexual populations , in addition to this randomly selected mother , potential mates are identified from among those within the specified geographic distance , . In case there are no potential mates the mother reproduces without a mate . This situation is especially relevant for the extreme case , = 0 . In the sexual and asexual models the offspring is then dispersed within the geographic distance , , and occupies the site of a randomly killed individual within the area . At the beginning of the simulation , all individuals are reproductively compatible , corresponding to a completely connected graph ( Fig . 1 ) . Genetic similarity among individuals in the sexual and asexual model can be represented by an evolutionary spatial graph in which nodes are individuals , distance edges capture the geographical separation of each pair of individuals and viable edges that connect individuals within the same species . We here describe formally the derivation of equation 1 in the main text for the asexual model . The dynamics of sexual populations in the absence of dispersal limitation ( ) has been considered elsewhere and will not be derived here [32] . Individuals are haploid . The genome of each individual is represented by a sequence of sites , each nucleotide residing in one of two states , or . Each individual in a population of size is represented as a vector: , where is the site in the genome of individual . The genetic similarity between individual and individual can be defined as: ( 5 ) with . The genetic similarity in equation ( 5 ) can be written in terms of the fraction of identical sites ( ) ( 6 ) and is: ( 7 ) Each nucleotide in the offspring is inherited at random , thus ignoring linkage between neighboring nucleotides , but with a small probability of error determined by the mutation rate . Assuming that the individual inherited the nucleotide at site from its parent we need the probability that individual will have exactly the same nucleotide ( i . e . , or ) as . We assume that the probability of undergoing mutations in site is Poisson distributed: ( 8 ) Each mutation switches the nucleotide ( i . e . , ) . Then the probability of observing an even number of mutations , so that the nucleotide at site does not change the nucleotide is ( 9 ) The probability of an odd number of mutations , changing the nucleotide , is ( 10 ) Note that we can have mutations in site of the new offspring , but because the mutation rate , , is small , most of the probability density is concentrated in the 0 and 1 point mutation cases . The probabilities can be found by solving the system: ( 11 ) thus , ( 12 ) This derivation is similar to those of Peliti , Serva , Higgs and Derrida [28] , [76] , [77] , but we consider here nucleotides instead of alleles . In the asexual model , each individual is generated by one parent , . The expected fraction of nucleotides in shared with each individual in the population ( ) is , using equation 12 , ( 13 ) Substituting 12 in 13 we have ( 14 ) Substituting = from equation 7 then gives ( 15 ) and after simplification we obtain ( 16 ) Substituting equation 16 into 6 leads to ( 17 ) and from 17 we get ( 18 ) and equation 3 is derived from this expectation . We used this equation to simulate the mean genetic similarity in the transients , and we also used it to calculate the mean genetic similarity of the matrix , , at steady-state for asexual populations , , where for small and is the effective number of individuals in the population [28] . Our simulation is a stochastic , individual-based , zero-sum birth-death model of a sexual and asexual population with overlapping generations . For the simulations reported in the paper , we considered haploid individuals where only one individual can exist in each site . Simulations were carried out with an initial population , = individuals , and this initial population size remained constant throughout the simulations . Results for Figs . 3 and 4 were obtained after replicates and generations of a single model run , where a generation is an update of time steps . Geographic distance between each pair of individuals and , , was sampled from a normal distribution , and negative values were discarded . Results were qualitatively the same after varying . In the transients each replicate stops after the mean of the genetic similarity matrix , , reached the values , with all the replicates satisfying ( Figs . 3b , 3d and 4a ) . In the last stage parameter values were chosen to satisfy the mathematical condition required for speciation , [28] ( Figs . 3c , 3e and 4b–c ) . Steady-state was verified by checking the constancy of the mean genetic similarity value during the last generations within each replicate regardless the initial value of . We explored a broad range of parameter combinations with mutation rate , , the maximum geographic distance for mating and dispersal , , and two cut-off values to count species richness in the transient and equilibrium dynamics: the minimum genetic similarity value to define a species in the transients , , and the minimum genetic similarity value to define a species at equilibrium , respectively .
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The role of sex in driving genetic variation and the speed at which new species emerge has been debated for over a century . There is experimental and theoretical evidence that sex increases genetic variation and the speed at which new species emerge , although evidence that sex reduces variation and slows the formation of new species also exists . Surprisingly , given the link between sex and genetic variation , little work has been done on the impact of sex on biodiversity . In the present theoretical study we show that a faster evolutionary rate can decrease the abundance of newly formed species and thus decrease long-term biodiversity . This leads to the paradoxical result that sexual reproduction can increase genetic variation but reduce species diversity . These results suggest that reducing the rate of appearance of genetic variation and the speed at which new species emerge may increase biodiversity in the long-term . This unexpected link between reproductive mode , the speed of evolution and biodiversity suggests that a high evolutionary rate may not be required to yield a large number of species in natural ecosystems .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"theoretical",
"biology",
"ecology",
"genetics",
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2012
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Does Sex Speed Up Evolutionary Rate and Increase Biodiversity?
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Alphaviruses are RNA viruses transmitted between vertebrate hosts by arthropod vectors , primarily mosquitoes . How arthropods counteract alphaviruses or viruses per se is not very well understood . Drosophila melanogaster is a powerful model system for studying innate immunity against bacterial and fungal infections . In this study we report the use of a novel system to analyze replication of Sindbis virus ( type species of the alphavirus genus ) RNA following expression of a Sindbis virus replicon RNA from the fly genome . We demonstrate deficits in the immune deficiency ( Imd ) pathway enhance viral replication while mutations in the Toll pathway fail to affect replication . Similar results were observed with intrathoracic injections of whole virus and confirmed in cultured mosquito cells . These findings show that the Imd pathway mediates an antiviral response to Sindbis virus replication . To our knowledge , this is the first demonstration of an antiviral role for the Imd pathway in insects .
Arboviruses are a large group of RNA viruses that are transmitted between vertebrate hosts by arthropod vectors , primarily mosquitoes . Several arboviruses including members of alphavirus and flavivirus genera are important human pathogens causing severe arthritis , encephalitis , and hemorrhagic fever . Arboviruses are distributed globally , but individual virus species tend to have a focused geographic range . In the recent past , some viruses have expanded globally , and have caused more frequent and larger epidemics . For example , a strain of Chikungunya virus ( an alphavirus ) endemic to Africa caused an epidemic outbreak in the Indian sub-continent and the Indian Ocean islands leading to more than a million cases of disease and hundreds of death [1] , [2] . Similarly West Nile virus ( a flavivirus ) originally isolated from Uganda has caused about 100 , 000 cases of neuroinvasive disease and numerous deaths in North and South America [3] . The periodic nature of the infections along with increasing morbidity and mortality in several parts of the world poses a persistent public health risk [4] , [5] . Restriction of arbovirus transmission may be accomplished by vector control , vaccination , and/or antiviral treatment . However , currently there are few vaccines and no effective antiviral therapies available , nor are there efficient and safe means of vector control , underscoring the need to understand how arboviruses interact with vertebrate and arthropod hosts . Alphaviruses form an important group of arboviruses that causes human disease . They are divided into two clinical groups; those that cause serious but primarily non life-threatening illness like rash and arthritis and those that cause fatal encephalitis . The arthritogenic viruses include Sindbis , Chikungunya , and O'nyong-nyong viruses , while the encephalitogenic viruses include Venezuelan , western , and eastern equine encephalitis viruses [2] , [4] , [6] , [7] . Alphaviruses replicate efficiently in both arthropod and vertebrate hosts , however the pattern of infection differs in a host-dependent fashion; in vertebrate cells alphaviruses cause an acute cytolytic infection , whereas in mosquito cells the infection is predominantly persistent and non-cytolytic . This observation strongly suggests that the virus interacts with the host cells in different ways . Most studies of alphavirus pathogenesis and host responses have been performed in mammalian systems and there is a great deal of information available regarding the antiviral response in vertebrates [8] , [9] . However , less is known about the antiviral immunity against alphaviruses in arthropods . Innate immunity plays an important role in limiting microbes in arthropods , through humoral responses ( production of effector molecules such as antimicrobial peptides [AMP] ) , physical barriers , phagocytosis , encapsulation , and melanization [10] . Drosophila melanogaster has been used as an excellent model to study innate immune responses against pathogens that infect insects . Immune responses to various bacterial and fungal pathogens have been well characterized in Drosophila and primarily consist of the Toll and Imd pathways . The Toll pathway is activated by Gram-positive bacteria and fungi . The pathogen associated microbial patterns ( PAMPs ) such as lysine type-peptidoglycan are recognized by peptidoglycan receptor proteins ( PGRPs ) and this binding initiates a serine protease cascade . The cleaved form of the cytokine Spätzle activates transmembrane Toll receptor , which directs the phosphorylation and degradation of Cactus , an IκB-like protein that inhibits the NF-κB like transcription factors Dorsal and Dif . Translocation of these transcription factors to the nucleus causes a rapid increase in expression of multiple AMPs including Drosomycin [10]–[15] . The Imd pathway is stimulated by Gram-negative bacteria . When bacterial PAMP's such as monomeric or polymeric diaminopimelic acid peptidoglycan , bind to the transmembrane PGRP-LC receptor [16] , a death domain adaptor protein Imd is recruited . Imd binds to dFadd , another death domain protein which in turn interacts with the apical caspase Dredd [17]–[19] . This caspase then cleaves phosphorylated Relish , a NF-κB-type transcription factor [20] . Relish is phosphorylated by the IKK signaling complex , which is itself thought to be activated by TGF-β activated kinase 1 ( Tak1 ) and its adaptor TAK1-associated binding protein2 ( Tab2 ) [21]–[23] . The cleaved N-terminal domain of Relish then translocates to the nucleus and leads to transcriptional activation of several AMPs including Diptericin [11] , [20] . In contrast to the abundant information available for fungal and bacterial infections , less is known about how insects respond to viral infections . Recent studies have pointed to the role of RNA interference ( RNAi ) in generating antiviral immunity in arthropods [24]–[28] . RNAi , is triggered by the recognition of intracellular long double-stranded RNAs ( produced during viral genome replication ) . The endoribonuclease Dicer-2 processes these into small interfering RNA ( siRNA ) . These siRNA duplexes are then separated by R2D2 , and incorporated into the RNA-induced silencing complex ( RISC ) [29] . The guide strand of siRNA targets the RISC complex to complementary single-stranded RNA , which is then cleaved by the RNaseH like enzyme Argonaute 2 ( Ago2 ) [30] . Flies deficient in Dicer-2 , R2D2 , or Ago2 exhibit increased sensitivity to infection by Flock house virus ( FHV ) ( Nodaviridae ) , Drosophila C Virus ( DCV ) ( Dicistroviridae ) , Drosophila X virus ( DXV ) ( Birnaviridae ) , and Sindbis virus ( Togaviridae , alphavirus ) [24] , [25] , [28] . In addition to RNAi , DCV activates the Jak/STAT pathway in Drosophila . Global transcription profiles of flies infected with DCV showed induction of a set of genes distinct from the Toll- and Imd-induced target genes . vir-1 ( virus-induced RNA 1 ) was strongly induced by DCV and its expression was dependent on Hopscotch , the sole Jak kinase of Drosophila . Also , flies deficient in Hopscotch , showed increased viral load and sensitivity to DCV infection [31] , [32] . Studies using DXV demonstrated the role of the Toll pathway in antiviral response . Infection with DXV leads to a strong induction of Drosomycin , a marker of the Toll pathway . Also a loss-of-function mutant in Dif ( NF-κB component of Toll pathway ) and gain-of-function mutant in the Toll receptor were more susceptible to viral challenge and allowed increased viral replication [33] . Even though some of the mechanisms by which Drosophila controls viral infections are known , the molecular mechanism by which the Jak/STAT and Toll pathways are triggered or the effector mechanisms that control viral infections through these pathways are not yet understood . The innate immune responses characterized in mosquitoes have been largely based on what is known in Drosophila . The mosquito genome has orthologs to the components of the innate immune machinery of Drosophila . Keene et . al . have shown that in Anopheles gambiae , ago2 and ago3 are required for defense against O'nyong-nyong virus [34] while ago2 , r2d2 and dcr2 are required for anti-dengue defense in Aedes aegypti [35] , [36] . RNAi is also important in defense against SIN; silencing RNAi components in Ae . aegypti resulted in transient increases in SIN replication [37] . In addition to RNAi , the Toll pathway is also implicated in antiviral defense in mosquitoes . SIN infection induced the expression of Toll pathway-related rel1 transcription factor ( ortholog of dif ) and genes involved in the vesicular transport in mid-guts of Ae . aegypti [38] . A recent study showed that Toll pathway regulates resistance to dengue virus . Microarray analysis of dengue infected Ae . aegypti resulted in up-regulation of Toll pathway associated genes . Activation of the Toll pathway through RNAi-mediated silencing of the negative regulator Cactus reduced dengue virus infection level while repression of the Toll pathway through gene silencing resulted in higher dengue virus infection levels [39] . Although studies have begun to address the antiviral response in insects , much more needs to be known in order limit the spread of alphaviruses and other arboviral infections . In the present study we have taken advantage of the genetic tools available in Drosophila to study what host factors effect SIN replication . We generated a transgenic fly line that expresses SIN replicon RNA capable of autonomous replication . Previously , transgenic animals expressing viral genomes have been generated and used to study antiviral responses [24] , [40] . In the system we generated primary transcription of the replicon is under the control of the UAS/GAL4 system and hence can be launched in a temporally and spatially specific manner that is dependent on the enhancer/promoter driving GAL4 transcription [41] . We have demonstrated that SIN RNA replication can be launched using this system , providing a powerful tool for the genetic analysis of host genes affecting virus RNA replication . The SIN replicon fly line was crossed to fly lines carrying mutations in the innate immune pathways ( Toll , Imd and Jak/STAT ) to determine the role these pathways play a role in curtailing SIN replication . SIN replication remained unchanged in flies that were heterozygous for NF-κB orthologs Dif and Dorsal ( activated by the Toll pathway ) however SIN replication was higher in flies heterozygous for Relish . SIN replication was also enhanced in flies heterozygous for upstream members of the Imd pathway . Furthermore , intrathoracic injections of SIN virus into relish−/− flies showed higher viral loads and enhanced replication in mutant flies compared to wild type . These findings demonstrate that the Imd pathway is involved in antiviral defense against SIN and provide the first direct evidence for the involvement of the Imd pathway in antiviral defense in insects .
The UAS/GAL4 system allows for targeted gene expression by selective activation of any cloned gene in a wide variety of tissue- and cell-specific patterns [41] . We utilized this system to introduce RNA analogous to the genome of the alphavirus- SIN into Drosophila . Alphavirus genomes can be engineered to express heterologous proteins by substituting the structural protein genes with the heterologous protein gene . This replicon RNA is capable of self-replication but is not able to produce infectious virus particles . The SIN replicon used contains the nonstructural protein genes encoding the viral replicase , the 5′- and 3′-UTRs and a subgenomic promoter that directs expression of green fluorescent protein ( GFP ) . A DNA copy of the SIN replicon genome was cloned behind five UAS enhancer sequences and a minimal heat shock promoter . Transcription from these upstream sequences is activated by the yeast transcriptional activator GAL4 expressed from a specific enhancer/promoter , hence primary transcription of the replicon RNA occurs in temporal and spatial pattern analogous to the gene from which the enhancer/promoter driving GAL4 expression was derived . We generated two transgenic fly lines; 1 ) UAS-SINrep:GFP encodes a SIN replicon RNA capable of GFP expression from the subgenomic mRNA , and 2 ) UAS-SINΔrep:GFP encodes a mutant form of SIN replicon lacking sequence coding for the nonstructural proteins and hence is incapable of replication . When these fly lines are crossed to “driver” lines expressing GAL4 RNA pol II-mediated transcription of the replicon RNA is activated in the progeny . A schematic of the RNAs encoded by these flies is shown in Figure 1A . While primary , cell-based , transcription of the SIN replicon RNA is under the control of the UAS/GAL4 system ( Figure 1B , steps 1 and 2 ) , GFP expression from this RNA is dependent on the replicon encoded viral RNA synthetic complex comprised of the viral nonstructural proteins . This complex copies the plus-strand replicon RNA ( analogous to the SIN genome ) into a minus-strand copy which in turn serves as a template for plus-strand RNA synthesis , both full-length replicon RNA and the subgenomic mRNA encoding GFP . Figure 1B shows the hypothesized launch of SIN replicon replication under the control of UAS/GAL4 ( Figure 1B ) compared to a natural virus infection ( Figure 1C ) . Following the introduction of the viral genomic plus-sense RNA into the cytoplasm , which differs for each system ( steps 1 and 2 ) , the process of genome replication and subgenomic mRNA expression is the same for each system ( steps 3 to 8 ) , meaning host factors that inhibit or support viral genome replication are the same in each case . We crossed UAS-SINrep:GFP line to an Act5C-GAL4 activator line to determine if alphavirus genome replication can be launched by Drosophila RNA polymerase II-mediated transcription . Primary transcription of the replicon RNA is dependent on the activity of the Act5C enhancer/promoter to drive expression of GAL4 . Act5C was chosen as the driver for these experiments as it has been shown to be broadly expressed during Drosophila development and hence provided the greatest opportunity for driving primary transcription of the replicon RNA in a tissue that was permissive for viral RNA replication [42] . Expression patterns of GFP in Act5C-GAL4 , UAS-SINrep:GFP flies ( hereafter referred to as SIN replicon fly ) were compared to those in the control Act5C-GAL4 , UAS-GFP flies ( hereafter referred to as control GFP fly ) . F1 progeny at various stages of development were examined for GFP expression . In the third instar of control GFP larvae , GFP expression was observed throughout the body with areas of high expression in the anterior end of the larvae , while SIN replicon-derived GFP expression was characterized by punctate areas of high expression throughout the body ( Figure 2A and 2B ) . The expression pattern however changed in late pupae . Act5C driven GFP expression in the control was predominantly in the abdomen with low levels of expression in thorax and head ( Figure 2A ) , whereas replicon derived GFP expression was predominantly in the thorax with little expression in the abdomen and head ( Figure 2B ) . The pattern of expression in adult flies was similar to that of pupae . This result suggests that once viral RNA replication is initiated , the pattern of GFP expression is no longer defined by the pattern of GAL4 expression , and , as long as the cell carries replicon RNA in the cytoplasm and is permissive for viral RNA replication there is no requirement for continuous primary cell-mediated transcription of replicon RNA . To determine the viral dependence of the GFP expression observed in the SIN replicon expressing flies we crossed the UAS-SINΔrep:GFP line to Act5C-GAL4 line and checked for GFP expression in F1 Act5C-GAL4 , UAS-SINΔrep:GFP flies ( hereafter referred to as mutant SIN replicon flies ) . Since the mutant SIN replicon RNA lacks a significant portion of the nonstructural protein coding region , it is not capable of replication and , therefore should be incapable of subgenomic mRNA synthesis and GFP expression . As hypothesized , we observed no GFP expression in the F1 progeny at any developmental stage ( Figure 2C ) . This result demonstrated that the GFP produced in the SIN replicon flies was dependent on the viral non-structural proteins and was therefore a consequence of viral genome replication . Results of observed GFP fluorescence were confirmed by qRT-PCR of GFP mRNA . Extremely low levels of GFP mRNA were detected in flies containing the DNA copy of the SIN replicon but lacking the GAL4 driver ( UAS-SINrep:GFP ) . In SIN replicon flies with the driver there was a ∼550-fold increase in the level of GFP transcripts ( Figure 2D ) . Additionally replicon derived minus-strand replication intermediates were detected in Act5C-GAL4 , UAS-SINrep:GFP by RT-PCR ( supplementary data , Figure S1 ) . These data suggest that binding of GAL4 activated primary transcription of the replicon RNA that then replicated autonomously leading to the production of high levels of GFP encoding RNA . In mutant SIN replicon flies without the driver there was again low levels of GFP mRNA detected , however in mutant SIN replicon flies with the driver there was a 2-fold change in GFP transcripts . This change in RNA levels can be attributed to GAL4 activation of primary transcription of mutant replicon RNA ( Figure 2E ) . Drosophila lines expressing replicon RNA , mutant replicon RNA , and GFP were stabilized in order to ensure the co-segregation of the GAL4 and UAS elements . These fly lines were used in the experiments that follow . A previous study showed that flies homozygous for a mutant allele of Dicer-2 ( dcr-2L811FXS ) were more susceptible to SIN infection . Dicer-2 mutant flies when infected with SIN , had increased viral RNA accumulation and higher viral loads compared to wild type flies [24] . To determine if Dicer-2 played a role in controlling the level of SIN RNA replication following host-derived launch we crossed the SIN replicon fly with Dicer-2 mutant flies . The F1 progeny heterozygous for the SIN replicon and Dicer-2 showed increased levels of GFP indicating increased viral replication ( Figure 3A ) . Viral RNA replication was measured by GFP fluorescence and GFP mRNA levels . The fluorescence and mRNA levels were 1 . 8 and 2- fold higher respectively in flies possessing only one functional copy of Dicer-2 when compared to SIN replicon flies homozygous for wt Dicer-2 ( Figure 3C and 3E ) . There was no change in GFP expression levels or mRNA levels in the control GFP flies heterozygous for Dicer-2 ( Figure 3B , 3D and 3F ) . Our results verified the previously reported role of Dicer-2 and RNAi in controlling SIN infection and confirmed that the UAS/GAL4 system for replicon launch could be used to genetically examine antiviral responses in Drosophila . The antimicrobial pathways in Drosophila play a very important role in combating infections . The Toll pathway results in the activation of NF-κB homologues Dif and Dorsal , the Imd pathway activates Relish , while Jak-STAT pathway triggers STAT . These transcription factors are central to the pathways , in the sense that they activate the antimicrobial effector molecules that eventually eradicate microbes . To determine if any of the known antimicrobial transactivators function to inhibit SIN RNA replication we crossed the SIN replicon fly with Dif , Dorsal , Relish and STAT mutants and examined their effects on SIN RNA replication . We measured GFP expression in F1 progeny as a gauge of viral RNA replication . In flies heterozygous for SIN replicon and dif or dorsal mutations there was no change in the levels of GFP . However , there was a 2 . 3-fold increase in GFP levels in SIN replicon flies heterozygous for a relish mutation ( Figure 4A and 4B ) . SIN replication measured by qRT-PCR of nsP1 mRNA showed similar results . Levels of nsP1 mRNA was 3- fold higher in flies heterozygous for relish mutation compared to wt fly background ( Figure 4C ) . This result suggests that Relish-dependent transcription may be involved in the suppression of SIN replication . Flies heterozygous for SIN replicon and stat also displayed increased SIN replication . GFP expression levels were 1 . 7- fold higher in STAT mutant flies compared to SIN replicon flies , suggesting that STAT might also play a role in inhibiting SIN RNA replication . Relish is activated as a result of signaling through the Imd pathway , therefore the data above indicated that this pathway is involved in an antiviral response . To determine the role of the Imd pathway in the suppression of SIN RNA replication we crossed the SIN replicon fly to flies mutant in upstream components of the Imd pathway and examined their effect on virus replication . SIN replication was measured by qRT-PCR of nsP1 mRNA . Replication of SIN RNA increased in flies containing mutations in the Imd pathway ( Figure 5A ) . The levels of nsP1 transcript were 2 . 8 , 2 . 7 , 4 . 5 and 3 . 3- fold higher in F1 progeny heterozygous for relish , imd , dfadd and dredd respectively when compared to the replication in a wt fly background . Similarly , nsP1 mRNA levels were also higher by 1 . 5 , 3 . 6 and 3 . 1- folds in flies heterozygous for tab2 , ird5 and key respectively compared to levels in wt flies . We also measured SIN replication via levels of GFP transcript . The levels of GFP transcripts were 2 . 3 , 2 . 4 , 3 . 7 and 3 . 9- fold higher in F1 progeny heterozygous for relish , imd , dfadd and dredd respectively when compared to the replication in a wt fly background . GFP mRNA levels were also higher by 2 . 4 , 3 . 1 and 2 . 1- folds in flies heterozygous for tab2 , ird5 and key respectively compared to levels in wt flies ( Figure 5B ) . These data demonstrate that the Imd pathway plays a role in the control of SIN genome replication . The activation of Relish leads to transcription of the AMPs Diptericin and Metchnikowin , while activation of Toll pathway leads to the expression of Drosomycin . Levels of these transcripts were used as markers of antimicrobial pathway activation . A large induction of both Diptericin and Metchnikowin was detected in SIN replicon flies . Diptericin was up by 4 . 8- fold and Metchnikowin by 9 . 2- fold as compared to w1118 flies . However , there was no difference in the levels of Drosomycin in SIN replicon flies when compared to w1118 flies . These results confirmed that SIN replicon was stimulating the Imd pathway that activated Relish and expression of AMPs . We also examined the expression of these AMPs in mutant SIN replicon flies expecting no increase in Relish-dependent mRNA expression of AMPs in these flies since viral replicon replication was not occurring . However , a 2 . 4 and 3 . 1- fold increase in diptericin and metchnikowin transcripts respectively was detected in the mutant SIN replicon flies ( Figure 5C ) . This suggests that viral replication is being detected through recognition of the viral RNA and replication is not necessary for stimulation of this pathway . It is important to note that significant increases in the levels of AMP encoding mRNAs were not observed as a consequence of UAS/GAL4 based GFP expression , demonstrating that Relish activation is not occurring simply as a consequence of over-expression of a heterologous gene ( Figure 5C ) . Finally to confirm the role of Relish in antiviral defense against alphavirus in Drosophila , we infected relish−/− flies , dif−/− intrathoracically with 200 pfu of SIN . Viral loads were measured by levels of RNA containing nsP1 sequence . Relish mutant flies had 9 . 3- fold higher level of viral RNA compared to w1118 flies 5 days post-infection ( Figure 6A ) . The levels of viral RNA however remained the same in dif−/− flies as compared to w1118 flies confirming that Imd but not Toll pathway is involved in controlling SIN infection . Also , SIN viral titers were 3-fold higher in relish−/− flies compared to w1118 and dif−/− flies ( Figure 6B ) . The anti-viral role of relish was also verified by overexpressing relish . UAS-Relish . his6 flies were crossed to a hemocyte GAL4 driver . The hemocyte driver was chosen to maximize expression of Relish [43] . The F-1 progeny overexpressing Relish were injected with 200 pfu of SIN virus intrathoracically and viral replication was measured five days post- infection . The levels of viral RNA in flies overexpressing Relish was down by 0 . 51-fold as compared to wt w1118 flies or UAS-Relish . his6 flies without GAL4 driver ( Figure 6C ) . The Drosophila Relish consists of an N-terminal Rel/NF-κB homology domain ( RHD ) and a C-terminal IκB-like domain with ankyrin repeats . Relish is activated by endoproteolytic cleavage , the RHD translocates to the nucleus and the IκB domain is retained in the cytoplasm . The RHD binds to DNA and activates the transcription of AMPs [44] . Although the exact mechanism of activation of mosquito Relish is still not known , mosquitoes produce three isoforms of Relish from the rel2 gene by differential mRNA splicing . The first Relish isoform resembles the Drosophila Relish; it contains the RHD and IκB -like domain . The second isoform has a RHD but lacks the IκB-like domain but has a unique 3′-UTR . The third isoform lacks the RHD but has an intact IκB-like domain . To verify the relevance of our findings in Drosophila we examined the cellular localization of the N-terminal RHD in uninfected and SIN infected cultured mosquito cells ( c6/36 ) . Infected and uninfected cells were fractionated into cytoplasmic and nuclear fractions and quantities of the RHD were examined by western blot . While levels of the RHD were consistently high in the cytoplasm of both infected and uninfected cells , we observed that SIN infection repeatedly resulted in an increase in the amount of the RHD in the nucleus of infected cells following 48 h of infection ( Figure 7 ) . This strongly implies that SIN activates Relish-mediated transcription during persistent infection of cultured mosquito cells .
In the present study we have developed a powerful system to genetically examine the effects of host factors in suppressing SIN RNA replication in Drosophila . SIN RNA replication was launched by cellular transcription of the DNA copy of the viral genome using the UAS-GAL4 system . We crossed the SIN replicon fly to flies carrying mutations in specific components of antimicrobial pathways to determine their role in anti-SIN defense . Replication of SIN RNA was higher in flies heterozygous for a mutation in relish ( Imd pathway ) but not for dif or dorsal ( Toll pathway ) . Additionally , SIN replication was higher in flies heterozygous for upstream components of the Imd pathway . Furthermore , intrathoracic injections of SIN virus into relish−/− flies showed higher viral loads and enhanced replication whereas SIN replication was unchanged in dif−/− flies . These findings indicate that the Imd pathway is involved in antiviral defense against SIN . This is the first report of the Imd pathway's involvement in antiviral defense in Drosophila . The data presented in this manuscript demonstrated that SIN replicon mediated RNA synthesis could be launched from the Drosophila genome using the UAS-GAL4 system . Using the Act5C-GAL4 driver we observed robust SIN RNA replication at numerous stages of development . The replicon replication was not pathogenic , although there was 1–2 day delay in the developmental cycle . The pattern of GFP expression resulting from the SIN replicon was different from that observed in the control UAS-GFP fly and was not ultimately defined by the pattern of driver expression . We hypothesize that cells that contained replicating replicon RNA at an early stage in development , when act5C expression is ubiquitous [42] , continued to host viral RNA synthesis at later developmental stages even in the absence of primary transcription of the replicon RNA from the fly genome . The pattern of GFP expression in the SIN replicon containing flies also showed that not all tissues are permissive for virus genome replication . For instance while Act5C-GAL4 drives primary UAS-dependent transcription in the abdomen , the lack of GFP signal in the abdomen of replicon-containing flies suggests tissues in this body segment are significantly less permissive for virus replication than thoracic muscle in which replicon derived GFP expression was high . This system for the launch of SIN RNA replication allows a significant amount of control over where and when replicon RNA is produced in the developing fly . By using different GAL4 drivers we can launch replicon RNA production in a temporal and spatially specific fashion . This provides a greater degree of control than with other transgenic systems of virus launch [24] , [40] in which a generalized heat-shock is used to induce cell-mediated transcription of the viral RNA . This flexibility has allowed us to begin to map the permissivity of tissues for viral RNA replication during fly development , including salivary glands , muscle , mid-gut , and CNS ( data not shown ) . A consistent finding when analyzing viral RNA replication using different drivers to launch SIN replicon replication has been that thoracic muscle is highly permissive for virus replication confirming our initial findings with the Act5C driver . Additionally this system allows us to genetically screen for host factors that are both pro- and antiviral . Our confirmation of the previously reported involvement of the RNAi pathway in the control of SIN replication led us to examine the role other antimicrobial pathways play in the control of SIN RNA synthesis [24] . Examination of the effects of transcription factors associated with antimicrobial signaling pathways revealed SIN replicon replication was 2 . 3- fold higher in flies heterozygous for Relish mutation . Relish is the terminal transcription factor in the Imd signaling cascade . This pathway is usually activated by Gram-negative bacteria , however activation can also occur by some fungi that do not activate the Toll pathway [45] . Our observation of enhanced RNA replication in flies deficient in components of the Imd pathway , in combination with the observed increase in Relish dependent transcription in flies harboring SIN replicon RNA , has for the first time demonstrated that Imd/Relish pathway is activated by a virus . Earlier studies have indirectly implied a role for viruses in activation of the Imd pathway but have not found specific antiviral effects . Zambon et . al . found that DXV activates sets of AMPs transcribed by both Relish and Dif but only the Toll pathway was involved in anti-DXV defense [33] . Sanders et . al . studied the transcriptional expression profiles of mosquito midguts infected with SIN . Based on the expression pattern they hypothesized that early innate immune responses to SIN infection was through Toll pathway , which is later shut-off and the Imd pathway is activated later in infection [38] . Also , RNAi mediated silencing of native regulators- Cactus ( Toll ) and Casper ( Relish ) in mosquitoes followed by infection with Dengue virus activated a considerable number of genes by Relish , however only genes activated by Dif had anti-Dengue effect [39] . Our analyses of Dif and Dorsal imply that the Toll pathway does not have an antiviral effect at the level SIN RNA replication . While we have strong evidence that the Imd pathway is activated in response to SIN RNA , how the Imd pathway is activated by SIN remains an open question . Gram-negative bacteria activate the Imd pathway when bacterial proteoglycans are recognized by host transmembrane receptors - PGRP-LC and PGRP-LE . This binding leads to the recruitment of Imd by an unknown protein [16] . The SIN replicon does not produce proteins resembling the peptidoglycans of bacteria . We therefore assumed that PGRPs have no role in SIN replication . To address this assumption we measured SIN replication in PGRP-LE and LC mutant flies and we found no difference in the replication of SIN in flies heterozygous for PGRP-LE and LC ( Figure S2 ) . We believe that in the SIN replicon flies , the activation of the Imd pathway occurs within the cell since the replication complexes and the replicating RNA are intracellular . We therefore hypothesized that an intracellular receptor recognizes either viral RNA or replication complexes and feeds a signal into the Imd pathway that ultimately activates Relish . Dicer-2 serves as a cytoplasmic sensor of viral RNA similar to mammalian RIG-I and Mda5 and induces the expression of antiviral protein Vago [46] . Since our data indicate that viral RNA is responsible for Relish activation , we examined the role of Dicer-2 , and also Dicer-1 in activation of the Imd pathway . However to this point , we have observed no role for Dicer-2 or Dicer-1 in induction of Relish-mediated transcription in SIN replicon flies ( Figure S3 ) . It is currently unclear what the effectors of the Relish-mediated anti-SIN response might be . While increased transcription of the AMP genes diptericin and metchnikowin was used as a measure of Relish activation , a role for these AMPs in antiviral immunity seems unlikely . The induction of AMP expression as a consequence of SIN replication may however be an important prophylactic immune response preventing secondary bacterial infection due to virus-induced tissue damage . We are currently performing comparative transcriptome analyses to identify differences in transcript levels unique to the SIN replicon flies in order to facilitate the identification of antiviral effector molecules . Another variable that may significantly affect the antiviral response of Drosophila is the presence of Wolbachia . Wolbachia are Gram-negative bacteria that manifest intracellular , inheritable infections . In Drosophila melanogaster the infection is transmitted vertically through the female , and previous studies have reported that Drosophila infected with Wolbachia are less susceptible to infections with RNA viruses [47] , [48] . We tested all the lines used in this study for presence of Wolbachia by PCR . Among the lines tested two lines were positive for Wolbachia , the Dif mutant line ( w[1118];Df ( 2L ) Exel8036/CyO ) and PGRP-LC mutant ( w67c23 P{lacW}l ( 1 ) G0414G0414/FM7c ) line ( Figure S4 ) . In our crosses we used females from the SIN replicon expressing line ( Act5C-GAL4/UAS-SINrep:GFP ) that were negative for Wolbachia and males from the mutant lines . Since Wolbachia is transmitted maternally , the progeny resulting from these crosses are not infected with Wolbachia , and hence virus replication was consistently analyzed in a Wolbachia negative background . SIN replication was enhanced in STAT mutant flies suggesting that the Jak/STAT pathway may also be involved in controlling SIN replication . Previous data have shown DCV infection induces the expression of vir-1 and expression of vir-1 is dependent on Hopscotch- the Jak kinase . Further genetic experiments suggested that Hopscotch was required but not sufficient for the induction of DCV -regulated genes [31] . It is possible that SIN infection activates both Imd and Jak/STAT pathways and that multiple pathways are required for effective viral clearance . However the potential role of Jak/STAT in SIN infections needs to be completely understood . While Drosophila represents a genetically accessible model organism , alphaviruses are naturally transmitted between vertebrate hosts by mosquitoes . The mosquito genome has orthologous genes for dif and relish; rel1 and rel2 respectively [49]–[52] . We verified the relevance of our findings in Drosophila by infection of cultured mosquito cells . The results indicated that Rel-2 is activated during SIN infection of c6/36 cells . The RHD containing isoforms of Rel-2 localize to the nucleus later during infection . These results imply that Relish-mediated transcription may be important in controlling virus replication during the persistent phase of infection in mosquitoes . These results also suggests that the results generated by using Drosophila as model organism can be compared and verified in mosquito cells . In summary , we have developed a system for the controlled launch of SIN RNA replication from the genome of Drosophila . Using this system we have demonstrated that , in addition to RNAi , Jak/STAT , and Toll , the Imd pathway plays an important role in the antiviral response in flies . Further characterization of how the virus is recognized by the host and what downstream effector molecules are required for the control of virus replication will provide additional insights into the role of this pathway in particular and the antiviral response of arthropods in general .
BHK-21 and C6/36 cells ( American Type Culture Collection ) were grown in Alpha MEM and L15 media respectively ( Invitrogen ) supplemented with vitamins 10% of fetal bovine serum or heat inactivated FBS ( C6/36 ) . SIN:GFP is wild type SIN expressing GFP from a second subgenomic promoter was generated by transfection of BHK-21 cells with in-vitro transcribed infectious SIN:GFP TE RNA [53] . The pUAST- SINrep: GFP plasmid was constructed by replacing the Sbf1 and Not1 fragment of pUAST vector with pSINrep/GFP that encodes the non-structural proteins and GFP from a sub-genomic promoter preceded by 5 UAS sequences . The pUAST- SINΔrep: GFP construct was made by deleting 5 . 7 kb fragment in the non-structural region of pUAST- SINrep: GFP . RsrII and KpnI were used to remove the 5 . 7 kb fragment , and the remaining product gel purified and treated with DNA polymerase I large ( Klenow ) fragment ( NEB ) to remove the 3′ overhang and fill-in the 5′ overhang . The plasmid was the phenol/chloroform extracted , precipitated and ligated using T4 DNA ligase ( NEB ) . Stable transgenic lines harboring the UAS-SIN constructs were generated via standard methods [54] . We obtained a transformant line for SINrep: GFP that mapped to the third chromosome and one line for SINΔrep: GFP that mapped to the second chromosome . Fly lines ( listed in Table S1 ) were obtained from the Bloomington stock center . dcr-1Q1147X and dcr-2L811FXS flies were provided by R Carthew ( Northwestern University ) . Dif1 were provided by D Ferrandon ( Institut de Biologie Moléculaire et Cellulaire ) . Fly stocks were raised on standard cornmeal–agar medium at 25°C . Live flies , pupae and larvae were anesthetized with CO2 and viewed under on a Nikon SMZ1500 dissecting microscope . Photographs were taken using Nikon DXM1200 camera . Five adult flies were homogenized in 10 mM Tris ( pH 8 . 4 ) , 100 mM NaCl , 1 mM MgCl2 , 10 mM dithiothreitol [55] . Homogenates were centrifuged at 15000 g for 5 min to remove debris and fluorescence was detected using a Synergy 4 HT Multi-Detection Microplate Reader ( Biotek ) with excitation filter set to 485 nm and emission filter at 520 nm . For viral injections , flies were anesthetized with CO2 and injected with SIN:GFP virus or control alpha MEM media in the thorax using a glass capillary needle . To estimate the number of viral plaque forming units injected into flies , injected flies were immediately flash frozen in liquid nitrogen , homogenized in PBS , centrifuged at 15000 g for 15 minutes to remove the debris and viral titers determined by plaques assays of homogenates . Approximately 200 pfu of Sin:GFP virus was injected into the flies . Five days post-infection flies were collected and viral titers determined as mentioned above . For the survival experiments , the injected flies were put on fresh food , and the number of surviving flies was counted at regular intervals . RNA was extracted by homogenizing flies in TRIzol reagent ( Invitrogen ) . cDNA was made using AffinityScript QPCR cDNA synthesis kit ( Stratagene ) , and PCR amplification was done using Brilliant II SYBR Green QPCR master mix ( Stratagene ) following manufacturer's protocol . Gene expression was normalized to the actin mRNA expression . The comparative threshold cycle ( CT ) method was used to determine fold changes of transcript present in samples . Oligonucleotides used are listed in Protocol S1 . C6/36 cells were not infected or infected with SIN:GFP virus at MOI of 0 . 1 for 6 h and 48 h . Western blot analysis was performed using standard procedures . Rabbit anti-N Rel antibody ( kindly gifted by S . Stoven of Umea University ) was used to detect Relish . The FlyBase ( http://flybase . org/ ) accession numbers for the genes used in the text include actin5C ( CG4027 ) , dfadd ( CG12297 ) , dicer1 ( CG4792 ) , dicer2 ( CG6493 ) , dif ( CG6794 ) , diptericin ( CG12763 ) , dorsal ( CG6667 ) , dredd ( CG7486 ) , drosomycin ( CG10810 ) , imd ( CG5576 ) , ird5 ( CG4201 ) , kenny ( CG16910 ) , metchnikowin ( CG8175 ) , pgrp-lc ( CG4432 ) , pgrp-le ( CG8995 ) , relish ( CG11992 ) , stat92E ( CG4257 ) , tab2 ( CG7417 ) .
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Alphaviruses are arthropod-borne viruses maintained primarily in an endemic cycle between mosquitoes and rodents or birds . Transmission to humans may result in wide ranging symptoms from subclinical to fatal encephalitis . While infection of vertebrates causes disease , infection of mosquitoes results in a life-long , persistent infection . In order to examine arthropod host pathways involved in controlling alphavirus infections , we have employed a novel system for the controlled launch of Sindbis virus RNA replication from the genome of the fruit fly , Drosophila melanogaster . We present data showing robust replication of a Sindbis virus RNA following its cell-mediated transcription in flies using the UAS-GAL4 misexpression system . Using this system we have genetically demonstrated that the immune deficiency pathway ( Imd ) suppresses viral RNA replication as a consequence of the activation of the transcription factor Relish . Additionally , we confirmed the activation of the Relish ortholog as a consequence of Sindbis virus infection of mosquito cells . Our work is the first direct demonstration that the Imd pathway plays a role in arthropod antiviral immunity .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"virology/effects",
"of",
"virus",
"infection",
"on",
"host",
"gene",
"expression",
"virology/host",
"antiviral",
"responses",
"virology/viral",
"replication",
"and",
"gene",
"regulation",
"virology"
] |
2009
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A Novel System for the Launch of Alphavirus RNA Synthesis Reveals a Role for the Imd Pathway in Arthropod Antiviral Response
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The debilitating human disease schistosomiasis is caused by infection with schistosome parasites that maintain a complex lifecycle alternating between definitive ( human ) and intermediate ( snail ) hosts . While much is known about how the definitive host responds to schistosome infection , there is comparably less information available describing the snail’s response to infection . Here , using information recently revealed by sequencing of the Biomphalaria glabrata intermediate host genome , we provide evidence that the predicted core snail DNA methylation machinery components are associated with both intra-species reproduction processes and inter-species interactions . Firstly , methyl-CpG binding domain protein ( Bgmbd2/3 ) and DNA methyltransferase 1 ( Bgdnmt1 ) genes are transcriptionally enriched in gonadal compared to somatic tissues with 5-azacytidine ( 5-AzaC ) treatment significantly inhibiting oviposition . Secondly , elevated levels of 5-methyl cytosine ( 5mC ) , DNA methyltransferase activity and 5mC binding in pigmented hybrid- compared to inbred ( NMRI ) - B . glabrata populations indicate a role for the snail’s DNA methylation machinery in maintaining hybrid vigour or heterosis . Thirdly , locus-specific detection of 5mC by bisulfite ( BS ) -PCR revealed 5mC within an exonic region of a housekeeping protein-coding gene ( Bg14-3-3 ) , supporting previous in silico predictions and whole genome BS-Seq analysis of this species’ genome . Finally , we provide preliminary evidence for parasite-mediated host epigenetic reprogramming in the schistosome/snail system , as demonstrated by the increase in Bgdnmt1 and Bgmbd2/3 transcript abundance following Bge ( B . glabrata embryonic cell line ) exposure to parasite larval transformation products ( LTP ) . The presence of a functional DNA methylation machinery in B . glabrata as well as the modulation of these gene products in response to schistosome products , suggests a vital role for DNA methylation during snail development/oviposition and parasite interactions . Further deciphering the role of this epigenetic process during Biomphalaria/Schistosoma co-evolutionary biology may reveal key factors associated with disease transmission and , moreover , enable the discovery of novel lifecycle intervention strategies .
With over 200 million people at risk of infection and approximately 200 , 000 deaths per year , schistosomiasis is the second most significant human parasitic disease on the planet [1] . This devastating and chronic illness , caused by trematode flatworms , is endemic across 78 countries of tropical and subtropical regions , with the majority of cases occurring in sub-Saharan Africa [1] . The prevalence of schistosomiasis depends on the geographical range of susceptible snail species , which serve as the obligatory intermediate host of the parasite . Three genera of pulmonate snails , Bulinus , Oncomelania and Biomphalaria , represent the most important intermediate hosts of medically important schistosome species ( Schistosoma haematobium , Schistosoma japonicum and Schistosoma mansoni respectively ) . Anthropogenic activities , such as the construction of dams or development of irrigation schemes are commonly responsible for the population expansion of these snails [2 , 3] and , hence , result in the spread of this neglected tropical disease into previously unaffected regions . The high reproductive rate of these monoicous snails and their tolerance to temperature fluctuations [4] are additional factors contributing to further expansion into new geographical ranges . Indeed , Biomphalaria spp . have recently been found in the Ukraine [5] , as well as Romania [6] and a Bulinus sp . has been documented in Corsica [7] . This spread northward into more temperate climates will likely accelerate based on global climate change predictions , thereby facilitating the spread of the ferrying disease [8 , 9] . Despite the success of intermediate host elimination in restricted schistosomiasis-endemic areas via chemical [10] or biological [11–13] measures , large-scale eradication has been difficult to implement [14] . In the absence of a prophylactic vaccine and the challenges associated with sustaining single-compound , anti-schistosomal chemotherapy [15 , 16] , the future of integrated schistosomiasis control will increasingly rely on developing novel strategies to eliminate the intermediate host . However , in order to accomplish this objective , a deeper understanding of the intermediate host’s underlying biology and molecular processes is urgently needed [17] . In metazoans , epigenetic processes , such as those facilitated by DNA methylation , play an important and well-recognised role in basic biological phenomena including development , genome stability and phenotypic plasticity [18 , 19] . While our current understanding of DNA methylation has been transformed by vertebrate studies , there likely are significant differences in the conservation and function of the underlying DNA methylation machinery components in invertebrates; these are slowly being unravelled across phyla [20–23] . Within molluscs , the role of DNA methylation has only been extensively investigated in the economically important Pacific oyster Crassostrea gigas [24] where it was recently found that intragenic regions of moderately expressed genes and derived mobile genetic elements are predominantly targeted by this epigenetic machinery [25] . Expanding DNA methylation studies to other molluscan species would increase our understanding of this important epigenetic process within the phylum . Here , owing to the biomedical importance of schistosomiasis and the need to further understand the molecular biology of an intermediate host responsible for disease transmission , we characterise the core DNA methylation machinery components found within the B . glabrata genome . The components identified include a maintenance DNA methyltransferase ( BgDNMT1 ) , a DNA/tRNA methyltransferase ( BgDNMT2 ) and a methyl-CpG-binding domain protein ( BgMBD2/3 ) . Detecting DNMT and MBD activity in two different B . glabrata strains suggest that these core DNA methylation machinery components are functional , with BgDNMT1/BgDNMT2 likely responsible for the 5-methyl cytosine ( 5mC ) modifications observed here , in addition to previous studies [26 , 27] . BgDNMT1 and BgMBD2/3 transcription is elevated in gonadal tissues , as well as in response to S . mansoni parasite products , indicating a role for this epigenetic process in both snail reproduction and parasite interactions . 5-azacytidine mediated inhibition of B . glabrata oviposition further supports a physiological role for DNA methylation in reproductive biology . Novel anti-schistosomal strategies targeting these DNA methylation machinery components await further investigations as an element of future integrated schistosomiasis control efforts .
Several different B . glabrata ( Bg ) isolates used in this study include the NMRI ( Naval Medical Research Institute ) strain , the BB02 ( Biomphalaria from Barreiro , Brazil caught in 2002 ) strain , the BgBRE strain originally sampled in Recife in 1975 ( Brazil ) , and a pigmented hybrid line obtained from Prof Michael Doenhoff’s laboratory ( Nottingham University ) produced by crossing numerous known susceptible isolates ( Bg-Swansea , Bg-Brazil , Bg-Egypt and Bg-Belo Horizonte ) . Bg-Swansea snails ( provenance unknown ) were obtained in the early 1990s from Dr B . James of Swansea University . Bg-Belo Horizonte snails were originally collected in Belo Horizonte ( 1967 ) by W . Haas ( University of Erlangen , Germany ) . Bg-Egypt snails ( provenance unknown ) were obtained from the Behring Institute for Medical Research in 1980 . Bg-Brazil snails were collected in Brazil in the early 1970s and obtained from Colonel W . Radke . Full-length B . glabrata DNMT and MBD homologs were predicted by performing tBLASTn searches of the snail’s genome v4 . 3 using a range of DNMT ( Mus musculus DNMT1—GenBank: P13864 . 5 , Apis mellifera DNMT1—GenBank: NP_001164522 . 1 , Ciona intestinalis DNMT1—XP_002122948 . 1 ) and MBD ( Aplysia californica—GenBank: XP_005103642 . 1 , Crassostrea gigas MBD2/3—GenBank: EKC32831 . 1 ) query sequences . The exon-intron structures of Lottia gigantea DNMT1 ( transcript name: 114987 ) , DNMT2 ( transcript name: 119453 ) and MBD2/3 ( transcript name: 112523; all [28] ) were used to finalise the B . glabrata gene structures . Two day-fasted , laboratory bred specimens of NMRI strain were dissected and RNA subsequently isolated using TRIzol Reagent ( Invitrogen ) according to the manufacturer’s protocol . Following treatment with DNaseI ( Ambion ) , 1 μg of RNA was reverse-transcribed using random hexamer primers and SuperscriptIII ( Invitrogen ) . Oligonucleotide pairs ( S1 Table ) , designed from the predicted sequences , were used to amplify full-length ( ATG to stop ) BgMBD2/3 ( 729 bp ) and BgDNMT2 ( 1182 bp ) sequences from cDNA derived from the head/foot of an individual NMRI snail . PCR products were subsequently cloned into pGEM-T Easy vector ( Promega ) before being sequenced . In the case of BgDNMT1 , a 1652 bp product ( containing the catalytic domain within its C-terminus ) was amplified and subjected to pGEM-T Easy vector cloning as well as DNA sequencing . Following sequence confirmation , the translated sequences of BgDNMT1 , BgDNMT2 and BgMBD2/3 were subsequently submitted to a Pfam domain search [29] and the identified domains of BgDNMT1 ( PF12047 , PF02008 , PF01426 , PF00145 ) , BgDNMT2 ( PF00145 ) and BgMBD2/3 ( PF01429 , PF14048 ) were extracted . Furthermore , the presence of a nuclear localisation signal ( NLS ) within the ORF of BgDNMT1 was examined and confirmed using cNLS mapper [30] . Multiple sequence alignments of BgDNMT1 , BgDNMT2 and BgMBD2/3 were generated using MUSCLE v3 . 8 [31] . The catalytic domain ( PF00145 ) of BgDNMT1 and BgDNMT2 was aligned to the sequences of the following organisms ( GenBank accession number ) : A . californica DNMT2 ( XP_005095276 . 1 ) , L . gigantea DNMT2 ( transcript name: 119453 [28] ) , Capitella teleta DNMT2—ELU13416 . 1 , Helobdella robusta DNMT2 ( transcript name: 89038 [28] , S . mansoni DNMT2 ( HM991456 . 1 ) , A . mellifera DNMT2 ( XP_393911 . 3 ) , M . musculus DNMT2 ( AAC53529 ) , A . californica DNMT1 ( XP_005104649 . 1 [28] ) , L . gigantea DNMT1 [28] , C . teleta DNMT1a ( ELT93682 . 1 ) , H . robusta DNMT1 ( transcript name: 116156 [28] ) , A . mellifera DNMT1 ( NP_001164522 . 1 ) and M . musculus DNMT1 ( P13864 . 5 ) . In the case of BgMBD2/3 , an alignment was created using the following sequences ( GenBank accession number ) : A . californica ( XM_005103585 . 1 ) , L . gigantea MBD2/3 ( transcript name: 112523 [28] ) , C . gigas MBD2/3 ( EKC32831 . 1 ) , H . robusta MBD2/3 ( transcript name: 185546 [28] ) , C . teleta MBD2/3 ( ELT95247 . 1 ) , S . mansoni ( HM991455 ) , Paragonimus westermani MBD2/3 [32] , S . japonicum MBD2/3 ( AAW26585 . 1 ) , Hymenolepis microstoma MBD2/3 [32] , Echinococcus multilocularis MBD2/3 [32] , Echinococcus granulosus DNMT2 [32] , Taenia solium MBD2/3 [32] , Schmidtea mediterranea MBD2/3 [32] , Hemicentrotus pulcherrimus MBD2/3 ( EU590662 ) , M . musculus MBD2 ( NP_034903 ) , M . musculus MBD3 ( NM_013595 ) . For phylogenetic analysis of BgMBD2/3 , BgDNMT1 and BgDNMT2 based on Bayesian ( MrBayes v3 . 1 . 2 [33] ) and Maximum Likelihood ( MEGA v5 . 2 . 2 [34] ) approaches , amino acid sequences were aligned using MUSCLE v3 . 8 [31] . The six highly conserved motifs within the catalytic domain ( PF00145 ) of BgDNMT2 and BgDNMT1 were aligned with sequences from ( GenBank accession number ) : A . californica DNMT2 ( XP_005095276 . 1 ) , L . gigantea DNMT2 ( transcript name: 119453 [28] ) , C . teleta DNMT2 ( ELU13416 . 1 ) , H . robusta DNMT2 ( transcript name: 89038 [28] ) , C . intestinalis DNMT2 ( XP_002128135 . 1 ) , M . musculus DNMT2 ( AAC53529 . 1 ) , S . mediterranea DNMT2 [32] , E . multilocularis DNMT2 [32] , S . mansoni DNMT2 ( HM991456 . 1 ) , Fasciola hepatica DNMT2 [32] , A . mellifera DNMT2 ( XP_393911 . 3 ) , Culex quinquefasciatus DNMT2 ( XP_001867327 . 1 ) , C . intestinalis DNMT3a ( XP_002123461 . 1 ) , M . musculus DNMT3a ( O88508 . 2 ) M . musculus DNMT3b ( O88509 . 2 ) , H . robusta Dnmt3 ( transcript name: 162653 [28] ) , A . mellifera DNMT3 ( NP_001177350 . 1 ) , L . gigantea Dnmt3 ( transcript name: 171288 [28] ) , Bombyx mori DNMT1 ( NP_001036980 . 1 ) , A . mellifera DNMT1 ( NP_001164522 . 1 ) , H . robusta DNMT1 ( transcript name: 116156 [28] ) , A . californica DNMT1 ( XP_005104649 . 1 [28] ) , C . teleta DNMT1a ( ELT93682 . 1 ) , L . gigantea DNMT1 [28] , C . intestinalis DNMT1 ( XP_002122948 . 1 ) and M . musculus DNMT1 ( P13864 . 5 ) . For the phylogenetic analysis of BgMBD2/3 , an amino acid sequence alignment of the following MBD sequences was used ( GenBank accession number ) : Clonorchis sinensis MBD2/3 [32] , Opisthorchis viverrini MBD2/3 [32] , S . mansoni ( HM991455 ) , P . westermani MBD2/3 [32] , S . japonicum MBD2/3 ( AAW26585 . 1 ) , F . hepatica MBD2/3 [32] , E . multilocularis MBD2/3 [32] , E . granulosus DNMT2 [32] , Taenia solium MBD2/3 [32] , H . microstoma MBD2/3 [32] , S . mediterranea MBD2/3 [32] , Macrostomum lignano MBD2/3 [32] , H . pulcherrimus MBD2/3 ( EU590662 ) , A . californica ( XM_005103585 . 1 ) , L . gigantea MBD2/3 ( transcript name: 112523 [28] ) , C . gigas MBD2/3 ( EKC32831 . 1 ) , C . teleta MBD2/3 ( ELT95247 . 1 ) , Xenopus laevis MBD3 ( BAC22082 . 1 ) , M . musculus MBD3 ( NM_013595 ) , X . laevis MBD2 ( NP_001083787 . 1 ) , M . musculus MBD2 ( NM_010773 ) , X . laevis MBD1 ( NP_001104183 . 1 ) , M . musculus MBD1 ( NM_013594 ) , X . laevis MeCP2 ( AAD03736 . 1 ) , M . musculus MeCP2 ( NM_010788 ) , Xenopus tropicalis MBD4 ( NP_001037916 ) and M . musculus MBD4 ( NM_010774 ) . In the case of the MBD homologs , ambiguously aligned regions were removed with Gblocks v0 . 91b [35] . Maximum Likelihood analysis was conducted with the Jones-Taylor-Thornton ( JTT ) substitution model and 500 bootstrap replicates . Bayesian inferences were computed using the WAG substitution model , performing four independent Markov Chain Monte Carlo runs for 1 , 000 , 000 generations . Graphical output of the final Bayesian consensus phylograms was then obtained via Figtree v1 . 3 . 1 [36] and further manual annotations were made in Adobe Illustrator v13 . 0 . 2 . First , a diverse set of tissues including albumen gland ( AG ) , buccal mass ( BUC ) , central nervous system ( CNS ) , digestive gland/hepatopancreas ( DG/HP ) , head/foot ( FOOT ) , heart/amebocyte producing organ ( HAPO ) , kidney ( KID ) , mantle edge ( MAN ) , ovotestes ( OVO ) , salivary glands ( SAL ) , stomach ( STO ) and terminal genitalia ( TRG ) was dissected from adults of the B . glabrata BB02 strain and pooled from 4–5 individual snails . Thereafter , total RNA was isolated using TRIzol Reagent ( Invitrogen ) and subsequently DNase treated following the manufacturer’s protocol ( Ambion ) . Poly ( A ) + RNA was isolated from total RNA ( Ambion MicroPoly ( A ) Purist kit ) , quality controlled using an Agilent 2100 Bioanalyzer ( RIN score = 7–8 ) and used to generate a non-normalised cDNA library by the NuGEN Ovation RNA-Seq System V2 ( NuGEN ) . Finally , each cDNA library was sequenced on an Illumina HiSeq instrument ( ~36Gb per lane ) . The raw RNA-Seq reads of each sample are available in the NCBI BioProject repository ( PRJNA12879 ) . Prior to mapping of the raw sequence data , adaptor and primer sequences were removed from the Illumina paired-end reads with FASTX-Clipper [37] and a quality control check was performed using FastQC [38] . Thereafter , reads were mapped to the B . glabrata genomic scaffolds available at VectorBase [39] with TopHat2 [40] . Subsequently , the Samtools mpileup program [41] was employed for SNP/INDEL calling and the variants encountered were filtered for quality as previously described in Jia et al . [42] . A normalised gene expression count matrix was generated using the R statistical programming language v3 . 1 . 2 [43] , the Bioconductor packages GenomicRanges and GenomicAlignments [44] , as well as DESeq2 following the protocol of Anders and colleagues [45] . DESeq2 was also used to conduct differential expression analyses ( cut-offs included a 10% false discovery rate [46] and a minimum log2 fold change of 1 amongst different snail tissue types [45] ) . Using BLAST2GO [47] , gene ontology ( GO ) terms [48] were assigned to differentially expressed transcripts and the relationships between genes was represented as a network where a node ( vertex ) represents a gene and a line ( edge ) connecting two genes represents neighbours [49] . Using the igraph library [50] in R [51] , differentially expressed genes were represented in the form of a graph . Two genes are associated ( i . e . were connected by a line ) if they shared a ‘Biological Process’ GO term category and their expression profiles were correlated ( 0 . 6 ≤ Pearson Correlation ≤ -0 . 6 ) . Samples from AG , STO , FOOT , DG/HP and OVO were dissected from 3–4 BgBRE snails under a binocular dissection microscope ( three biological replicates for each tissue ) . Haemocytes from 10 snails were collected from haemolymph after centrifugation at 10 , 000 x g for 10 min at 4°C . Total RNA was subsequently isolated from the five different tissues and haemocytes using TRIzol Reagent ( Invitrogen ) according to the manufacturer’s protocol . Thereafter , RNA samples ( 10 μg ) were treated with DNaseI ( Ambion ) and 1 μg was reverse-transcribed using random hexamer primers and Revertaid H minus M-MuLV reverse transcriptase ( Fermentas ) . qRT-PCR was then performed on cDNAs ( diluted 20-fold with nuclease-free water ) using the Light Cycler System 480 ( Roche ) . Primer sequences used for amplification of Bgmbd2/3 ( BgMBD2/3 qRT-PCR1 ) , ribosomal protein BgS19 and Bgdnmt1 can be found in S1 Table . Ct-values of the target genes were normalised to the transcript level of the reference gene BgS19 ( GenBank: CK988928 ) using the Pfaffl method as described in Chalmers et al . [52] . Each qRT-PCR experiment was performed at least twice and biological duplicates were used for each tissue and technical triplicates performed for every qRT-PCR reaction . In the case of haemocytes , technical duplicates of one sample were used . NMRI B . glabrata snails ( 1–1 . 2 mm in size ) were maintained in artificial freshwater ( 0 . 378 mM CaCl2 , 0 . 5 mM MgSO4-7H2O , 0 . 025 mM K2SO4 , 0 . 5 mM NaHCO3 , 0 . 0002 mM FeCl3-6H2O in dI water ) in the presence ( 491μM ) or absence of the demethylating agent 5-AzaC ( Sigma ) at 28°C for eight days . Two replicate experiments were performed ( experiment one = 10 snails/condition; experiment two = 12 snails/condition ) with the 5-AzaC replaced at day four and the total number of egg sacs laid/condition recorded at day eight . The Student’s two-tailed t-test was used to determine statistical differences in egg sacs laid between the treatments . Nuclear proteins were extracted from the head/foot of starved NMRI and pigmented hybrid adult snails ( 20 mg of tissue derived from 4 individuals/strain ) using the Epiquik Nuclear Extraction Kit ( Epigentek ) . DNA methyltransferase activity contained within 7 μg of nuclear protein extracts was subsequently measured using the EpiQuik DNA Methyltransferase Activity/Inhibition Assay Kit ( Epigentek ) . Fluorescent readings ( 530EX/590EM nm ) were obtained using a POLARstar Omega ( BMG Labtech ) microtiter plate reader and data were normalised as previously described [27] . Snail MBD activity was measured in 10 μg of nuclear protein extracts using an EpiQuik MBD2 Binding Activity/Inhibition Assay Kit ( Epigentek ) . Fluorescent readings were obtained as above and data was subsequently normalised to both negative control ( 10 μg of BSA ) and positive control ( MBD2 , supplied by kit ) samples . Data are presented as means ± standard deviation ( SD ) and each assay was repeated at least twice . gDNA was isolated from a pool of either four starved individual NMRI or pigmented hybrid snails using the DNeasy Blood and Tissue Kit ( Qiagen ) . A treatment step with RNase ( Invitrogen ) followed and 5mC abundance was subsequently fluorometrically determined from 100 ng of RNA-free gDNA using the SuperSense methylated DNA Quantification Kit ( Epigentek ) as previously described [27] . The assay was performed in duplicate , repeated twice and readings are presented as means ± standard deviation ( SD ) . 5mC abundance was calculated based on the B . glabrata genome GC content ( 35% ) using the following equation: 5mCpercentage= ( sample−negcontrol ) GCcontentRFU ( poscontrol−negcontrol ) ×10×100% Bisulfite conversion was performed as previously described by Fneich et al . [26] . Briefly , 300 ng gDNA ( derived from a pool of 10 individual snails of the BgBRE strain ) was denatured with 3M NaOH and subsequently treated with a solution of sodium-bisulfite and hydroquinone at pH 5 in the dark for 4 hr at 55°C . Thereafter , the gDNA was desalted ( Amicon Ultra column , UFC510024 Millipore ) , desulfonated by the addition of 350 μl of 0 . 1M NaOH and finally dissolved in 50 μl of 10 mM Tris/Cl ( pH 8 ) . A nested PCR was then performed to amplify regions of the Bg14-3-3 ( Scaffold 8484:17058–17923 ) gene . Primer pairs were designed using MethPrimer [53] on genomic sequences extracted from the preliminary genome assembly v4 . 3 [54] as indicated in S1 Table . The initial PCR amplification was performed using 1 μl of the bisulfite converted gDNA samples as templates with external primer set as follows: 94°C for 2 min , 5 cycles of 94°C for 1 min , 46°C for 2 min and 72°C for 3 min , followed by 25 cycles of 94°C for 30 sec , 46°C for 2 min and 72°C for 1:30 min and finally 72°C for 10 min . The nested PCR was performed on a 10 fold dilution of the first PCR product using the internal primer set in the same condition as for the first PCR except for the annealing temperature which was increased to 50°C . The subsequent PCR reaction was performed in 25 μl using 1 . 25 units of Go Taq DNA polymerase ( Promega ) , dNTPs at 0 . 4 μM for each deoxynucleotide and primers at 0 . 4 μM . PCR products were separated by electrophoresis through 1% agarose gels to check for the specific amplification of each target gene . For high-resolution analysis , 1 μl of each PCR product was cloned into pCR4 ( TOPO TA Cloning kit , Invitrogen ) and positive clones were sequenced with vector specific primers ( S1 Table ) using GenoScreen sequencing facilities ( Campus de l'Institut Pasteur de Lille , France ) . Sequences obtained from the bisulfite treated gDNA were aligned with their respective genomic reference sequence in Bioedit v7 . 2 . 5 [55] to identify methylated cytosines . MethTools v2 . 0 software [56] was used to generate a graphical view of each target region containing the methylated sites . The Whole Genome Bisulfite-Seq ( WGBS ) data set , performed as part of the B . glabrata genome project ( Genome Publication , under review ) , was then inspected for the presence of methylated CpG sites within the Bg14-3-3 gene using the genome browser IGV v2 . 3 [57] . In order to test the effects of naturally produced larval products on expression of the epigenome machinery in snail cells , the B . glabrata embryonic ( Bge ) cell line was exposed in vitro to S . mansoni larval transformation products ( LTP; [58] ) for 24 hr at 26°C and subjected to qRT-PCR analyses . Briefly , mRNA was isolated from control and LTP-treated Bge cells as well as Bge cells treated with S . mansoni larval transformation products ( LTP ) as previously described [59] . qRT-PCR was subsequently employed to investigate Bgdnmt1 and Bgmbd2/3 transcript abundance between samples derived from LTP-treated versus control cells . Amplifications were performed on a StepOnePlus ( ABI ) qRT-PCR machine using SYBR Green ( ABI ) chemistry; primer sequences can be found in S1 Table ( BgMBD2/3 qRT-PCR1 ) . The Ct-values of the target genes were normalised to the transcript level of the reference gene Actin ( GenBank: Z72387; [58] ) using the Pfaffl method as described in Chalmers et al . [52] . Results are based on two biological replicates and each qRT-PCR reaction was performed in technical duplicates . No amplification was observed in negative control reactions ( H2O instead of cDNA template ) .
A tBLASTn search against the preliminary B . glabrata genome assembly v4 . 3 [54] , using known molluscan MBD homologs ( A . californica—GenBank: XP_005103642 . 1 , C . gigas MBD2/3—GenBank: EKC32831 . 1 ) , revealed the presence of a single MBD protein . A subsequent BLASTp search against the NCBI database using the predicted sequence demonstrated 75% identity with the A . californica homolog ( NP_00510364 . 2; E-value of 2e-156 ) . This confirms findings by Fneich et al . [26] , who had previously identified a partial MBD2/3 homolog in the preliminary B . glabrata genome assembly , as well as in available RNA-Seq datasets . The transcript sequence encoding the 242 aa predicted ORF of the B . glabrata MBD2/3 ( BgMBD2/3 ) homolog was subsequently amplified from adult NMRI head/foot cDNA and PFAM domain search analysis of the cloned product ( GenBank: KJ951055 ) revealed the presence of a N-terminal MBD domain ( PF01429 ) , as well as a C-terminal domain conserved amongst proteins of the MBD2 and MBD3 family ( PF14048 ) . Multiple sequence alignment of BgMBD2/3 with related proteins further demonstrated high levels of sequence conservation over the entire N-terminal MBD domain and high similarity to invertebrate-specific MBD2/3 proteins , as well as the murine MBD2 and MBD3 homologs ( Fig 1 ) . Furthermore , unlike the mammalian MBD3 , which contains limited 5mC binding capability due to a single amino acid substitution [60] , the presence of crucial residues ( indicated in alignment by asterisk: R14 , K22 , Y26 , R36 ) , essential for the binding of the protein to methylated DNA [32] , enables us to propose that the snail homolog would be a functional member of this protein family . The presence of a C-terminal region unique to MBD2 and MBD3 proteins ( PF14048 ) , in addition to the absence of a glycosylase domain ( characteristic for MBD4 ) and Zn-finger motif ( found in MBD1 ) suggests that the B . glabrata MBD is a novel MBD2/3 homolog . Phylogenetic analyses based on Bayesian and Maximum Likelihood inferences of BgMBD2/3 with characterised MBDs , provides additional supporting evidence that the B . glabrata MBD is a de facto MBD2/3 homolog ( Fig 2 ) . As expected for an invertebrate organism , BgMBD2/3 clusters with invertebrate-specific MBD2/3 proteins as well as closely related vertebrate MBD2 and MBD3 members . Nevertheless , the B . glabrata MBD2/3 homolog is placed with great reliability ( bootstrap value of 99 , posterior probability of 1 . 00 ) outside a distinct clade of vertebrate MeCP2 , MBD1 and MBD4 homologs and is most similar ( bootstrap value of 89 , posterior probability of 1 . 00 ) to another molluscan MBD2/3 exemplar ( A . californica AcMBD2/3 ) . Following the identification of a MBD homolog within the B . glabrata genome ( BgMBD2/3 ) , a subsequent tBLASTn against the genome assembly using eukaryotic Dnmt homologs ( M . musculus DNMT1—GenBank: P13864 . 5 , A . mellifera DNMT1—GenBank: NP_001164522 . 1 , C . intestinalis DNMT1—XP_002122948 . 1 ) revealed the presence of a Dnmt1 as well as a Dnmt2 candidate in B . glabrata ( Fig 3 ) . Thereafter , a BLASTp search against the NCBI database with the predicted B . glabrata DNA methyltransferase sequences revealed 54% identity of BgDNMT2 with the L . gigantea homolog ( XP_009052047 . 1; 2e-134 ) and 75% identity of BgDNMT1 with the A . californica DNMT1 sequence ( XP_00509576 . 1; E-value 0 . 0 ) . Using the preliminary genome assembly and available RNA-Seq datasets , partial DNMT1 and DNMT2 sequences had previously been identified by Fneich et al . ( 2013 ) . Similar to BgMBD2/3 , the sequences of the two predicted DNA methyltransferases were confirmed using cDNA derived from the head/foot of adult NMRI snails . We were able to confirm the complete 393 aa ORF of BgDNMT2 ( GenBank: KJ951056 ) , as well as a 550 aa C-terminal region of BgDNMT1 , which includes the catalytic domain . A subsequent Pfam domain search revealed the presence of a DNA methylase domain ( PF00145 ) containing six highly conserved motifs ( I , IV , VI , VIII , IX and X ) and the target recognition domain ( TRD ) in both BgDNMT2 ( aa residues 3–415 ) and BgDNMT1 ( aa residues 863–1 , 314 ) members ( Fig 3A ) . In contrast , a regulatory domain containing a nuclear localisation signal ( NLS ) , a cytosine-specific DNA methyltransferase replication foci domain ( RFD; PF12047 ) , a Zinc Finger CXXC domain ( PF02008 ) and two bromo-adjacent homology ( BAH ) domains ( PF01426 ) were only found in BgDNMT1 ( Fig 3A ) . Subsequent alignment of BgDNMT1 and BgDNMT2 C-terminal DNA methylase domains ( PF00145 ) with known DNMT enzymes revealed strong sequence similarity across the six most conserved motifs ( I , IV , VI , VIII , IX and X ) ( Fig 3B ) . Specifically , the catalytically crucial proline/cysteine dipeptide [61] is present in both BgDNMT2 ( P77 & C78 ) and BgDNMT1 ( P949 & C950 ) . To discriminate the two enzyme families , DNMT2-specific residue substitutions within BgDNMT2 were noted: tyrosine ( Y ) to phenylalanine ( FXGXG ) in motif I and asparagine ( N ) to glutamine ( QXGXG ) in motif VIII [61] . Moreover , the DNMT2-specific cysteine/phenylalanine/threonine ( CFT ) tripeptide within the target recognition domain ( TRD ) is uniquely present in BgDNMT2 , but not in BgDNMT1 . A phylogram based on sequence alignment of 29 representative eukaryotic members of all three DNA Mtase families ( DNMT1 , DNMT2 and DNMT3 ) clearly separates BgDNMT2 and BgDNMT1 into their distinct clades ( Fig 4 ) . Despite being the most conserved of all DNA methyltransferases , the biological function of DNMT2 enzymes is highly debatable and its ability to methylate a DNA target has been questioned on numerous occasions [62 , 63] . Nevertheless , its dual biological activity and substrate specificity is now becoming more commonly accepted . For example , in mammals , DNMT2 predominantly serves as a tRNA methyltransferase [64] . However , in lower eukaryotes , DNMT2 commonly functions as the sole DNA methyltransferase [27 , 65 , 66] . Nevertheless , and in line with other molluscs ( i . e . the pacific oyster [67] ) , the B . glabrata genome encodes , in addition to a DNMT2 protein , a DNMT1 homolog . The latter is commonly referred to as a maintenance DNA methyltransferase , as members of this enzyme family preferentially methylate hemimethylated DNA [68] . Unlike DNMT2 homologs , DNMT1 enzymes additionally have a large regulatory N-terminal domain comprised of several notable elements ( Fig 3A ) . As BgDNMT1 contains these domains in the conserved order: 1 ) a DNMT1-replication foci domain ( RFD; PF12047 ) , a zinc finger domain ( CXXC; PF02008 ) and two bromo adjacent homology domains ( BAH; PF01426 ) , 2 ) has a predicted nuclear localisation signal ( NLS ) between residues 40–48 ( QGSAKRIKLQ ) and 3 ) includes the KG-repeat linker ( ( KG ) 4; [69] ) connecting the catalytic domain and N-terminal regions ( between residues 833–843 ) , we propose that this B . glabrata homolog is a functional member of this DNA methyltransferase family . Despite exhaustive searches , no DNMT3A or B homolog was found within the genome of S . mansoni’s intermediate snail host , suggesting that BgDNMT1 ( and to a lesser extent BgDNMT2 ) functions as the main cytosine methyltransferase within this invertebrate species . Our identification of both DNMT1 and DNMT2 ( but not DNMT3 ) DNA methyltransferase in the B . glabrata genome is in line with results recently obtained for A . californica , but is in contrast to the detection of a full set of DNMTs ( DNMT1 , DNMT2 and DNMT3 ) in C . gigas and L . gigantea [70] . This differential inclusion/exclusion of DNMTs in molluscan genomes has also been observed in the phylum Arthropoda where some members contain all three DNA methyltransferase families ( e . g . Apis mellifera [71] and Nasoni spp . [72] ) , others ( e . g . Locusta migratoria [73] ) , B . mori [71] ) , Tribolium castaneum [74] and Schistocerca gregaria [75] only contain DNMT2 and DNMT1 homologs , while others ( Drosophila melanogaster [76] ) only contain a single DNMT2 enzyme responsible for all 5mC modifications . Similar to arthropods , the significance of DNMT3 exclusion in specific molluscan species ( e . g . B . glabrata ) awaits further investigations . By taking advantage of RNA-Seq datasets generated as part of the B . glabrata genome project ( Genome Publication , under review ) , we were able to examine the transcript abundance of the snail’s DNA methylation machinery across a range of twelve distinctive tissues ( albumen gland , buccal mass , central nervous system , digestive gland/hepatopancreas , head/foot , heart/APO , kidney , mantle edge , ovotestes , salivary glands , stomach and terminal genitalia ) . For the purposes of examining DNA methylation machinery expression between gonadal vs . somatic tissues , samples 1 to 10 ( albumen gland , buccal mass , central nervous system , digestive gland/hepatopancreas , head/foot , heart/APO , kidney , mantle edge , salivary glands and stomach ) were treated as one population ( Group 1 ) , sample 11 ( ovotestes ) was regarded as a second population ( Group 2 ) and sample 12 ( terminal genitalia ) was considered as a third population ( Group 3 ) ( Fig 5 ) . Differential analyses of Bgmbd2/3 , Bgdnmt1 and Bgdnmt2 transcription amongst snail tissues ( Group 2 vs . Group 1 or Group 3 vs . Group 1 ) revealed statistically significant ( p < 0 . 05 ) increased expression of Bgmbd2/3 in both ovotestes and terminal genitalia , Bgdnmt1 in ovotestes and Bgdnmt2 in terminal genitalia ( Fig 5A and S1 Fig ) . These results were subsequently confirmed by qRT-PCR ( Fig 5B ) . Tissue-enriched expression of Bgdnmt1 , Bgdnmt2 and Bgmbd2/3 genes in gonadal structures ( compared to the somatic ones ) is consistent with the observations of Riviere et al . who demonstrated elevated transcript abundance of DNMT1 , DNMT2 and MBD orthologues in C . gigas oocytes ( compared to other tissues ) [67] . These data collectively suggested a prominent role for these core epigenetic machinery components in molluscan gonadal tissues and cells derived from or populating them . Significant inhibition of B . glabrata egg production/embryo development , mediated by the DNA demethylating agent 5-azacytidine ( 5-AzaC ) ( Fig 5C ) , further supported these transcriptional results and confirmed a physiological role for DNA methylation in snail reproductive processes . In addition to these 12 distinct tissues , Bgdnmt1 , Bgdnmt2 and Bgmbd2/3 mRNA abundance was also measured by qRT-PCR in haemocytes derived from haemolymph ( Fig 5B ) . As circulating defense cells , haemocytes are part of the snail’s innate immune system and , therefore , are involved in the host’s immune response to parasite infection [78] . Several studies have previously demonstrated that snail stress-response genes ( e . g . heat shock proteins ) are significantly modulated following trematode infection [79 , 80] . DNA methylation is commonly linked with transcriptional regulation during stress responses in eukaryotes [81 , 82] , and indeed Ittiprasert et al . [83] have recently shown that this epigenetic modification plays a significant role during schistosome infections via the modulation of heat shock proteins . Therefore , elevated expression of the core B . glabrata DNA methylation machinery in haemocytes suggests an epigenetic link to hsp70 transcription and possibly host defense mechanisms . Since our data support the presence of a functional B . glabrata methylation machinery , we expected to identify additional epigenetic-associated genes to be co-expressed with Bgdnmt1 , Bgdnmt2 and Bgmbd2/3 in the twelve tissues analysed . Therefore , using DESeq2 [51] , a pairwise differential expression analysis was performed between Group 2 ( ovotestes ) vs . Group 1 ( somatic tissues ) and Group 3 ( terminal genitalia ) vs . Group 1 samples to identify Bgdnmt1 , Bgdnmt2 and Bgmbd2/3 co-regulated genes . Using a FDR cut-off of 10% [46] and an absolute log2 fold change of at least 1 in either of the two comparisons , over 1000 genes were significantly over- and 180 genes were significantly under- represented in ovotestes , while 850 genes were significantly over and 440 genes were significantly under- represented in terminal genitalia . Both Bgdnmt1 and Bgmbd2/3 passed these stringent FDR and log fold change criteria ( confirming the t-distribution analysis in Fig 5A ) in ovotestes ( Group 2 vs . Group 1 ) , but not in terminal genitalia . In contrast , when applying the same stringent FDR and log fold change cut-offs , Bgdnmt2 did not display significant differential expression in either tissue . Gene network analyses were performed to further classify the differentially expressed genes that share biological functions and similar tissue-associated transcript abundances to Bgdnmt1 and Bgmbd2/3 . Since the transcripts of only two of the DNA methylation machinery components ( Bgdnmt1 and Bgmbd2/3 ) were significantly up-regulated in gonadal ( OVO ) vs . somatic tissues , subsequent gene-network relational analyses only concentrated on these two genes . GO terms of the 1180 identified ovotestes transcripts were assigned and the relationships between these gene-products were then depicted in the form of a network of positively ( R ≥ 0 . 6 ) or negatively ( R ≤ -0 . 6 ) correlated genes sharing ‘Biological Process’ GO terms . Using the analogy of ‘guilt by association’ suggested by Merico and colleagues [49] , the neighbourhood of Bgdnmt1 and Bgmbd2/3 showed a highly interconnected cluster of 53 genes ( S2 Fig and S2 Table ) and the expression of these genes across all 12 tissues is illustrated in the heat map in Fig 5C . Not surprisingly , the list includes genes that have been previously associated with epigenetic mechanisms or chromatin remodeling and are known for their interaction with DNMT1 homologs . For instance RBL1 , a protein involved in transcriptional repression via the formation of heterochromatin by stabilising histone methylation has also a recognised function in DNMT1 transcript regulation [84] . Additionally to RBL1 , the network illustrated in S2 Fig also indicates a strong link of Bgdnmt1 with histone methyltransferases ( HMT ) , namely SUV39H2 , SETD8 and SETDB1 . These findings are in line with studies reported for mammalian HMTs , which are known to associate with or modulate DNA methyltransferases [85 , 86] . While a functional DNA methylation machinery has previously been reported in B . glabrata , direct comparisons of DNA methyltransferase and MBD activity between different snail populations ( e . g . inbred vs . outbred individuals ) are lacking . This prompted us to measure both DNA methyltransferase [87] and MBD binding activity [88] within nuclear protein extracts derived from the head/foot of adult NMRI inbred and pigmented outbred snail populations as well as to quantify m5C levels in their gDNA pools ( Fig 6 ) . Firstly , using an ELISA-based assay , measurable amounts of DNMT activity were present in nuclear extracts of both strains ( Fig 6A ) . This data corroborates our description of putative functional BgDNMT1 and BgDNMT2 family members ( Fig 3 ) and confirms the observations of others [26 , 27 , 83] . Interestingly , these DNMT activity levels were elevated in the pigmented hybrid strain when compared to the NMRI inbred strain . We secondly assessed MBD binding activity ( again using an ELISA-based assay ) in the same samples , which revealed that the nuclear protein extracts of both snail strains additionally contain MBD proteins capable of binding to methylated DNA ( supporting the bioinformatics identification of a putative functional BgMBD2/3 , Fig 1 ) . Similar to the DNMT assay , MBD activity is higher in the pigmented hybrid snail samples ( Fig 6B ) . Finally , total 5mC levels were fluorometrically quantified within gDNA samples derived from both NMRI and pigmented B . glabrata populations ( Fig 6C ) . Based on a genomic CG content of 35% , ( Genome Publication , under review ) the amount of total cytosine methylation was estimated at 1 . 34% and 4 . 28% for the NMRI and the pigmented hybrid strain respectively . These values are within the range of DNA methylation levels detected in other invertebrates [89] , similar to the percentage of 5mC found in another mollusc [90] and close to the 2% previously reported by Fneich et al . [26] in the BgBRE strain using an LC-MS-based approach . Interestingly , the significantly higher levels ( p < 0 . 05 ) of detectable 5mC within gDNA pools of the pigmented hybrid in comparison to the NMRI strain is in line with the MBD and DNMT activity assays ( Fig 6A & 6B ) . It is commonly accepted that plant and animal hybrids frequently display different traits and increased fitness in comparison to inbred populations ( e . g . increased fecundity [91 , 92] ) . This boost in performance is generally referred to as hybrid vigour or heterosis , and so far , epigenetic mechanisms underlying this phenomenon have not been thoroughly characterised [93 , 94] . Recently , however , the role of epigenetics has been implicated with several studies demonstrating the importance of small RNA-directed DNA methylome dynamics in increasing hybrid performance ( e . g . Groszmann et al . [95] ) . Additionally , and more pertinent to our current findings were those reported by Shen and colleagues , who discovered that elevated 5mC levels in hybrid individuals led to global transcriptional changes and contributed to heterosis in Arabidopsis thaliana [96] . While our observations could simply reflect differences in life history traits , more thorough analyses of DNA methylation in B . glabrata populations that display different susceptibilities to schistosome infection , maintain different geographical distributions or are subject to diverse laboratory pressures may shed additional light on the proposed role of this epigenetic process in molluscan heterosis . While Fneich et al . [26] have previously demonstrated that the non-LTR repetitive element Nimbus ( BgI ) is either highly methylated or unmethylated , the same authors have proposed that the B . glabrata genome consists of densely methylated regions , interspersed with stretches of unmethylated DNA , due to the bimodal distribution the CpG observed to expected ratio ( CpGo/e ) within protein coding genes . This so-called mosaic DNA methylation pattern was further confirmed by a Whole Genome Bisulfite-Seq ( WGBS ) experiment as part of the B . glabrata genome project ( Genome Publication , under review ) . This observation is in line with numerous invertebrate studies [73 , 97–99] and describes the existence of two types of methylated genes , those that are highly methylated ( coding for house-keeping proteins ) and those that are lowly methylated ( encoding inducible gene products ) . Therefore , to support the WGBS analysis of the snail’s genome and to confirm the in silico CpGo/e predictions of Fneich et al . [26] as well as to maximise our chances at identifying robust 5mC signals within a B . glabrata protein coding gene ( similar to that recently detected for Bg-hsp70 [83] , we analysed the methylation status of a 451 bp region of the house-keeping Bg14-3-3 gene ( Scaffold 1582:42425–42875 ) ( Fig 7A ) . 14-3-3 genes code for highly conserved proteins ubiquitously expressed in eukaryotes and due to their interaction with signalling molecules , are involved in various biological pathways [101] . By analysing 14 sub-cloned BS-PCR amplicons of Bg14-3-3 and assessing the methylation status of 13 CpG sites contained within this single exon gene , we were able to confirm the CpGo/e prediction and WGBS detection of DNA methylation within the exonic region ( 451bp ) of this housekeeping B . glabrata gene . Specifically , ~30% of the total CpG sites within this region of Bg14-3-3 contain a methylation mark , and four CpG positions are methylated across nearly all clones ( Fig 7A ) . The DNA methylation status of these four CpG sites ( CpG10-CpG13 ) was also conserved in the WGBS data set ( Genome Publication , under review ) ( Fig 7B ) , confirming the stability of these epigenetic marks within this specific locus . Intragenic ( gene-body ) methylation has been positively linked to transcription [74 , 89 , 102] . Hence , congruent with other invertebrate species , [99 , 103 , 104] and supported by both WGBS and in silico analyses of the B . glabrata genome [26] , snail DNA methylation appears predominantly directed towards transcriptional units of house-keeping function ( e . g . 14-3-3 in the current study ) . The mammalian 14-3-3 homolog is known to be regulated by epigenetic modifications and aberrant DNA methylation patterns have been linked to tumourgenesis [105] . Relevantly , we were able to demonstrate 5mC within an exonic region of Bg14-3-3 and hence propose a similar regulatory role of DNA methylation for the B . glabrata homolog as well . Additionally , in contrast to some organisms , such as D . melanogaster [76] , Dictyostelium discoideum [65] and Entamoeba histolytica [106] , where non-CpG ( i . e . CpH ( H = T , A or C ) ) methylation is frequently observed , but in common with other molluscs ( e . g . C . gigas; [107] and Chlamys farreri [25] ) , DNA methylation in B . glabrata appears to be generally restricted to a CpG context ( all cytosines in a non-CpG context were converted after bisulfite treatment ) . These findings are in line with recent observations by Ittipraset et al . [83] and the recently reported B . glabrata genome paper ( Genome Publication , under review ) . As it is generally believed that genomes containing a Dnmt1 homolog mainly display methylation within CpG dinucleotides [71 , 89] , our results ( B . glabrata contains a DNMT1 homolog , Fig 3 and harbours CpG methylation , Fig 7A & 7B ) are in line with this view .
To successfully parasitise the molluscan intermediate host , schistosomes have to overcome the snail’s immune response . While the exact mechanisms by which schistosome parasites accomplish this feat are incompletely understood , the Bge cell line provides a powerful in vitro culture model to investigate the complex host-parasite interplay [108 , 109] . Bge incubation with larval transformation products ( LTP ) derived from miracidia to sporocyst transformation is thought to mimic the events that normally occurs inside the molluscan host [110] . Several studies have previously demonstrated that parasite-mediated modulation of various snail genes occurs [77 , 111 , 112] with Knight et al . [113] further demonstrating that gene repositioning within the snail nucleus occurs post parasite exposure . These nuclear reorganisation events , which are non-random , are known to impact gene expression and can be trigged by the presence of methylated CpGs [114 , 115] . Here , to explore whether schistosome products impact the transcriptional regulation of snail DNA methylation machinery components , Bge cells were cultured in the presence or absence of schistosome LTP [58] and assessed for Bgdnmt1 and Bgmbd2/3 abundance ( Fig 8 ) . Interestingly , Bge cells exposed to schistosome LTP significantly increased their expression of both Bgdnmt1 ( Fig 8A ) as well as Bgmbd2/3 ( Fig 8B ) indicating that the snail’s epigenetic machinery is responsive to biotic stress and is specifically reactive to parasite products . While translation of our data from a cellular system to whole organisms must be cautiously tempered , a recent study demonstrated that tissue-specific DNA methylation of snail Bg-hsp-70 is temporally affected by natural schistosome exposure and infection [83] . Collectively , these data would support the plasticity of the schistosome-modulated B . glabrata DNA methylation machinery in both cell ( Bge ) and whole organism ( snails ) systems . To further explore the functional relevance of DNA methylation-mediated processes during the snail’s response to parasite infection and to identify which specific pathways are epigenetically modulated , genome-wide DNA methylation/transcriptome analysis of infected vs . non-infected individuals ( or cells derived from them ) should be considered .
The increasing risk of S . mansoni transmission due to territory extension of its molluscan host B . glabrata poses a great concern even for developed countries in temperate regions . Since current mass drug administration programmes have limitations [15 , 16] and past intermediate host eradication programmes were largely unsuccessful [14 , 117] , the development of novel lifecycle intervention strategies is instrumental for the future control of schistosomiasis . Using a multidisciplinary approach , this study comprehensively characterised the core DNA methylation machinery of a gastropod mollusc as well as illustrated that it is more abundantly expressed in gonadal vs . somatic tissues , is differentially active in hybrid vs . inbred snail populations and is responsive to schistosome soluble products . This extended knowledge of B . glabrata epigenetics importantly provides new targets and molecular processes that could be instrumental in the development of integrated ways to combat a major neglected tropical disease .
|
Members of the genus Biomphalaria represent air-breathing ( pulmonate ) aquatic gastropod molluscs of great medical importance . The majority of species are obligatory intermediate hosts of the trematode flatworm Schistosoma mansoni , a pathogen responsible for the devastating neglected tropical disease schistosomiasis . Since the spread of the disease is governed by the temperature tolerance of its molluscan host , the envisaged rise in global temperatures will allow for the further expansion of the snail outside the native range into temperate regions . While schistosomiasis is currently predominantly controlled by praziquantel-mediated chemotherapy of infected individuals , novel strategies are needed in the longer term . The availability of the new Biomphalaria glabrata genome importantly now enables the design of next-generation schistosomiasis control strategies focused on the intermediate host . Here , using a variety of diverse approaches , we functionally characterise the critically important molecular process DNA methylation in B . glabrata , which is mediated by a suite of biological participants and is involved in a wide range of metazoan functions . Importantly , we confirm the presence of 5mC within the snail’s genome using global as well as locus-specific methodologies and we further provide the first evidence for a S . mansoni-provoked modulation of the intermediate’s host DNA methylation system .
|
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2017
|
The Biomphalaria glabrata DNA methylation machinery displays spatial tissue expression, is differentially active in distinct snail populations and is modulated by interactions with Schistosoma mansoni
|
Invasion of human erythrocytes is essential for Plasmodium falciparum parasite survival and pathogenesis , and is also a complex phenotype . While some later steps in invasion appear to be invariant and essential , the earlier steps of recognition are controlled by a series of redundant , and only partially understood , receptor-ligand interactions . Reverse genetic analysis of laboratory adapted strains has identified multiple genes that when deleted can alter invasion , but how the relative contributions of each gene translate to the phenotypes of clinical isolates is far from clear . We used a forward genetic approach to identify genes responsible for variable erythrocyte invasion by phenotyping the parents and progeny of previously generated experimental genetic crosses . Linkage analysis using whole genome sequencing data revealed a single major locus was responsible for the majority of phenotypic variation in two invasion pathways . This locus contained the PfRh2a and PfRh2b genes , members of one of the major invasion ligand gene families , but not widely thought to play such a prominent role in specifying invasion phenotypes . Variation in invasion pathways was linked to significant differences in PfRh2a and PfRh2b expression between parasite lines , and their role in specifying alternative invasion was confirmed by CRISPR-Cas9-mediated genome editing . Expansion of the analysis to a large set of clinical P . falciparum isolates revealed common deletions , suggesting that variation at this locus is a major cause of invasion phenotypic variation in the endemic setting . This work has implications for blood-stage vaccine development and will help inform the design and location of future large-scale studies of invasion in clinical isolates .
Plasmodium falciparum is an obligate intracellular parasite , unable to replicate outside a host cell . During the blood stages of its complex life cycle , P . falciparum parasites must transition from one erythrocyte to another . The process by which P . falciparum merozoites recognize and invade human erythrocytes is therefore critical for parasite survival , but also represents a brief window when it is extracellular and vulnerable to the host immune system . To guarantee its replication , the parasite has evolved a series of strategies to evade the host immune response during erythrocyte invasion . These strategies include using multiple alternative pathways to recognize erythrocytes , which are thought to allow the parasite population to survive if a specific invasion route is blocked by the immune response , or to adapt to different human erythrocyte surface polymorphisms . These alternate invasion pathways , and the receptor-ligand interactions that specify them , have been the subject of intensive research . Studies have employed in vitro invasion assays , inhibitory antibodies targeting specific ligands , enzyme treated or genetically deficient erythrocytes lacking specific receptors , and P . falciparum lines that have been genetically manipulated to delete specific ligands ( reviewed in [1] ) . Together , these studies have suggested that alternative invasion is largely specified by members of the P . falciparum Erythrocyte Binding Antigen ( PfEBA ) and Reticulocyte binding Homologs ( PfRh ) multi-gene families ( reviewed in [2] ) . Erythrocyte receptors have been identified for several PfEBAs and PfRhs , including three members of the Glycophorin family that are receptors for PfEBAs [3–5] , and Complement Receptor 1 ( CD35 ) that acts as a receptor for PfRh4 [6 , 7] . However , the P . falciparum genes encoding these ligands can each be deleted without compromising parasite viability , implying that these receptor-ligand interactions are not absolutely required for invasion to occur [4 , 8 , 9] . By contrast , other receptor-ligand interactions , specifically those between PfRh5 and its receptor Basigin [10] , and between AMA1 and RON2 [11] , do appear to be essential . This , coupled with detailed video microscopy studies , has led to a model where the receptor-ligand interactions that are redundant operate at a relatively early stage during invasion [12] , and perhaps provide the parasite with alternative pathways by which to invade , while those that are essential operate at later stages that are absolutely required in all strains [13] . While this picture of alternate invasion pathways involving PfRh and PfEBA ligand families is well established , it is not at all clear what molecular mechanisms underpin the phenomenon . In one case , a P . falciparum strain can be made to switch between invasion pathways under selection pressure or when exposed to gentle shaking , and this switch is accompanied by transcriptional up-regulation of PfRh4 [14–16] . However , this switching phenomenon is far from universal among lab-adapted isolates [15] and whether it occurs in clinical isolates is not known . Invasion phenotyping studies with P . falciparum clinical isolates from Africa , Asia and South America show that variation in invasion pathways , as measured by sensitivity to enzyme treatment , is universal ( reviewed in [17] ) . However , although some of these studies have linked alternative invasion pathways to variation in the sequence or expression of specific PfRhs or PfEBAs [18–23] , the relative contribution of each ligand to a given pathway is not clear; it is likely that most such pathways are controlled by multiple genes . Reverse genetics , deleting candidate genes one at time , has been used to great effect in dissecting the relative contribution of individual PfRh and PfEBA ligands ( reviewed in [1] ) . However , this approach by definition only considers known candidate genes . In other eukaryotic systems genetic variants affecting complex phenotypes have been identified using forward genetic approaches such as quantitative trait loci ( QTL ) mapping . In such approaches , two strains of known phenotype are crossed , and the phenotype and genotype of the parents and progeny are compared to identify QTLs associated with a given phenotype . Due to technical challenges and ethical considerations , only a limited number of experimental genetic crosses have been carried out in P . falciparum [24–26] , although the recent development of humanized mouse models may make such crosses more routine [27] . P . falciparum genetic crosses have previously been used to identify genes responsible for drug resistance [26 , 28 , 29] and primate host tropism [25] . We used the parents and progeny of two experimental crosses , 7G8 ( Brazil ) x GB4 ( Ghana ) and HB3 ( Honduras ) x Dd2 ( Laos ) in order to take an unbiased approach to identify genes underpinning alternative invasion pathways . Detailed phenotyping using a two-colour flow cytometry assay to quantitate alternative invasion [30] was combined with whole genome sequencing data [31] to perform QTL analysis . Despite the presumed complexity of invasion phenotypes , in the 7G8xGB4 cross a single major locus was identified as responsible for the majority of variation in two of the most variable alternative invasion pathways .
The parental strains of the 7G8 ( Brazil ) x GB4 ( Ghana ) experimental genetic cross were phenotyped using a two-colour assay that sensitively measures parasite invasion into enzyme treated and untreated erythrocytes [30] . There was little difference in the ability of the two parental strains to invade erythrocytes treated with trypsin ( TRY ) at either high or low concentrations ( Fig 1A ) , but there was a major difference in their ability to invade chymotrypsin ( CHY ) -treated erythrocytes . CHY treatment had no detectable effect on GB4 invasion , but reduced invasion of 7G8 parasites by >60% ( p-value = 0 . 00013 ) . By contrast , neuraminidase ( NEU ) treatment appeared to affect the GB4 parental line more than 7G8 ( p-value = 0 . 003 ) ( Fig 1A ) . To establish whether these phenotypes are genetically inherited , the same assay was used to phenotype all 27 available independent recombinant progeny clones from the 7G8 x GB4 genetic cross . The invasion phenotypes of the progeny clones were variable , with many intermediate between the two parental lines ( Figs 1B , 1C and S1 ) , implying that these enzyme-sensitive invasion pathways are complex multigenic traits . However , ordering clones based on the effect of CHY treatment showed evidence of discrete groups , with the phenotypes of some clones being similar to 7G8 ( JE11-WC4 in Fig 1C ) , others similar to GB4 , while the remainder had intermediate phenotypes . This suggests that at least in the case of a CHY-responsive pathway , there may be one or two genes with strong effect sizes underlying this complex phenotype . Given the inverse link between CHY and NEU invasion observed in the parental clones , we ranked the clones in ascending order based on their CHY phenotypes , and assessed the sensitivity of invasion to NEU treatment ( Fig 1B ) . The two invasion phenotypes showed a clear negative correlation across all clones ( R2 = -0 . 504 , Fig 1D ) . While this relationship was not absolute , of the five progeny clones least affected by CHY treatment , four were highly affected by NEU treatment ( XF12-DEV in Fig 1B ) , indicating that the same gene ( s ) may underpin both phenotypes within this cross . Some of the other phenotypes were also correlated ( S2 Fig ) , indicating that the same genes may underly multiple pathways in this cross . The parents of another genetic cross , Dd2 ( Laos ) and HB3 ( Honduras ) are known to differ significantly in their susceptibility to NEU treatment [12] , so we also tested the parents and progeny of this cross using the same assay . The effect of NEU of the progeny clones followed an almost continuous distribution between the parental phenotypes ( S3 Fig ) and subsequent QTL analysis identified no significant loci associated with either the NEU or CHY variable invasion phenotype ( S4 Fig ) . This null result could be the combined effect of several genes each with small effect sizes , or epigenetic regulatory mechanisms that escape detection by the sequencing and analysis methods used here , and invasion phenotypes were not pursued further in this cross . Illumina sequencing data from the parents and progeny of three P . falciparum experimental genetic crosses have been previously used to call single nucleotide polymorphisms ( SNPs ) , insertion/deletions ( indels ) and copy number variation ( CNVs ) with high accuracy [31] . We used QTL mapping with a curated set of SNPs from this dataset to search for loci associated with all four invasion phenotypes in the 7G8xGB4 cross . A genome-wide scan identified a locus on chromosome 13 with a highly significant LOD score for both NEU and CHY treatment phenotypes ( NEU LOD = 4 . 2 , CHY LOD = 6 . 7; Fig 2 ) . The same locus was identified for both phenotypes , and variation within this locus explains the majority of phenotypic variation for both CHY ( 68 . 9% of observed variation ) and NEU ( 51 . 2% of observed variation ) treatment phenotypes . No other statistically significant loci were identified for CHY treatment . For NEU treatment , several minor peaks were detected on chromosomes 7 , 9 and 10 but none passed the whole genome significance threshold , and only the minor peak on chromosome 10 ( discussed below ) contained genes previously associated with invasion ( Fig 2 ) . No significant loci were identified for the remaining enzyme treatment phenotypes . There was limited variation amongst clones after High-TRY treatment , which affected our ability to identify genetic signals . By contrast there was extensive variation after Low-TRY treatment , but no QTL signal was detected . To fine map the major locus on chromosome 13 underpinning the NEU and CHY treatment phenotypes we used Illumina sequence data to identify recombinant crossover breakpoints in the region ( Fig 3 ) and compared SNPs and phenotype data between the different progeny clones to identify crossovers . This approach narrowed down the locus to a region of 66 . 57kb , located between 1 . 406 and 1 . 473 Mb on chromosome 13 . Five progeny clones have the GB4 parental allele across this region , and all have identical CHY phenotypes to the parent GB4 , with erythrocyte invasion completely unaffected by CHY treatment . Four of these same clones are also those most strongly affected by NEU treatment , as highlighted in Fig 1C . None of the remaining clones were completely unaffected by CHY treatment , and all carry the 7G8 allele at this locus . These results show that this 66 . 57kb segment contains the primary determinants of both NEU and CHY invasion phenotypes in this genetic cross . To search for loci that could explain the remaining unassigned fraction of variation for the NEU and CHY phenotypes , a secondary genome-wide scan was conducted , controlling for the main effects on chromosome 13 identified in the primary scan . No other genomic regions reached statistical significance for the CHY treatment phenotype , suggesting that the chromosome 13 locus contains the primary gene ( s ) for this phenotype in these genetic backgrounds . For the NEU treatment phenotype , a region on chromosome 10 identified as a minor locus previously became statistically significant ( LOD = 4 . 1 , threshold = 3 . 6 , 29 . 3% of remaining observed variation ) . This locus was mapped to a region between 1377067bp and 1569779bp , containing 57 genes , several very polymorphic , including the Merozoite Protein 3 ( PfMSP3 ) gene family ( S5 Fig ) , two of which ( PF3D7_1035700 and PF3D7_1036300 ) have been shown to bind erythrocytes [32 , 33] . However , given the number of genes within this secondary locus , and its relatively low effect size on only one phenotype , we focused our subsequent analysis on the major locus on chromosome 13 affecting both NEU and CHY phenotypes . The locus on chromosome 13 contains fourteen annotated genes and two pseudogenes ( Fig 3 ) . Seven of these genes are expressed during late stages of the intra-erythrocyte cycle ( >35 hours ) , including several previously implicated in erythrocyte invasion , most notably P . falciparum Merozoite Surface Protein 7 ( PfMSP7 and a cluster of PfMSP7-related genes , and two genes encoding invasion ligands from the P . falciparum Reticulocyte Binding Protein Homologue family ( PfRh2a and PfRh2b ) . Illumina sequencing data identified only 5 SNPs that differed in PfMSP7 between the two parental clones , and none in any of the PfMSP7 homologues . Calling variation in PfRh2a and PfRh2b genes is more complex , as the two genes are positioned head-to-head and are identical over >8kb of their sequence , differing only at their 3’ ends , which encodes the carboxy-terminal 400–500 amino acids of the expressed proteins [34] . As a result , it is impossible to assign short sequence reads unequivocally to one gene or the other through the majority of their length . To identify polymorphisms specific to each gene , we sequenced each gene in the GB4 and 7G8 parental strains using Sanger/capillary technology . We also used PacBio whole genome sequencing data for these two strains to obtain long fragment reads that cover both genes [35] . These approaches identified several SNPs and two deletions in the GB4 strain . One of the deletions , spanning 156bp , was present in a repeat region in the PfRh2b gene close to the 3’ end , which marks the boundary between the region shared by PfRh2a/2b and the region unique to PfRh2b . Indels in this region have been reported previously and do not affect the reading frame of the encoded protein [36 , 37] . A novel deletion ( 681bp ) was identified in the non-coding region between the PfRh2a and PfRh2b genes . This deletion could only be identified in PacBio data emphasizing the difficulty of calling variants in the extremely AT-biased non-coding regions of the P . falciparum genome . Using the PacBio data as a reference for Illumina mapping , we confirmed that this deletion is present in all five clones that have the same CHY phenotype as GB4 and which inherited the GB4 chromosomal segment , while it was absent from all clones that inherited the 7G8 chromosomal segment . This deletion in the PfRh2a/2b promoter region has not been previously reported and due to its position , we hypothesized that it might affect transcription of one or both genes . Quantitative RT-PCR was used to quantitate expression of PfRh2a , PfRh2b , members of the neighboring PfMSP7 family as well as control invasion genes . Comparing gene expression between the two parental lines , 7G8 and GB4 , revealed a >80-fold decrease in the expression of PfRh2a and PfRh2b in GB4 relative to 7G8 ( Fig 4A ) . At an absolute level , there was almost no expression of PfRh2a in GB4 parasites , and expression of PfRh2b was extremely low . By contrast , there was no significant difference in expression of PfMSP7 or two other neighboring PfMSP7-like genes between the two parental lines ( Fig 4A ) . To test whether the differences represented a general shift in the expression of invasion ligands , we compared gene expression between the lines for three other major invasion ligands previously associated with alternative invasion pathways , PfEBA175 , PfEBA181 and PfRh4 . All showed only minor variation , with expression increased 1 . 6- ( PfEBA175 ) , 1 . 8- ( PfEBA181 ) and 2 . 5- ( PfRh4 ) fold in 7G8 relative to GB4 . Extending the analysis to progeny clones from the 7G8xGB4 genetic cross showed that all five progeny that had inherited the PfRh2a/2b intergenic deletion found in GB4 also had very similar PfRh2a and PfRh2b expression levels to GB4 ( AL12-QE5 , Fig 4B ) , with transcription of both genes almost absent . By contrast , four other clones that lack the intergenic deletion , like 7G8 , all had PfRh2a and PfRh2b expression levels comparable to the 7G8 parent ( JB8-AUD , Fig 4B ) , much higher than GB4 . There is therefore a clear and strong association between a novel indel in the PfRh2a and PfRh2b intergenic region , specific and extreme down-regulation of expression of these two genes , and use of a CHY-resistant and NEU-sensitive invasion pathway . In order to confirm the role of PfRh2a and PfRh2b expression in the invasion phenotype differences between 7G8 and GB4 , we used a CRISPR/Cas9 strategy to modify these genes in the 7G8 parent parasite line . Because the deletion in the GB4 parent line affects expression of both genes , we took advantage of the identity between PfRh2a and PfRh2b to target a resistance cassette to the coding region common to both genes , in a dual disruption strategy ( S6 Fig ) . After selection , drug resistant parasites were cloned and genotyped by PCR using a forward primer placed in the resistance cassette and reverse primers specific for the unique regions of either PfRh2a or PfRh2b . Genotyping established that all clones had integrated the resistance cassette in both genes ( S7 Fig ) . This integration was confirmed by whole genome sequencing using Illumina technology; coverage plots showed an absence of reads aligning to the central region of each gene , corresponding to the locations targeted for deletion ( S8 Fig ) . Expression of both PfRh2a and PfRh2b in the edited clones was greatly decreased compared to the parental line 7G8 and matched levels detected in GB4 ( Fig 5A ) , suggesting the dual disruption strategy recapitulated the expression effect of the intergenic deletion in the GB4 parent . Expression of PfRh2b protein was investigated in the parent strains and the edited clones by immunofluorescence , which showed clear PfRh2b labeling in 7G8 , but with no detectable protein in GB4 or the edited clones ( Fig 5B ) . To establish whether disruption of these genes impacted the pattern of erythrocyte invasion pathway usage , the two disrupted clones were phenotyped using the two-colour invasion assay . Both clones had phenotypes similar to the GB4 strain , being significantly more CHY-resistant and NEU-sensitive than their parental 7G8 line ( Fig 5C ) . Deletion of PfRh2a and PfRh2b in 7G8 therefore phenocopies the invasion preferences of GB4 , and confirms that these genes are responsible for the major variation in alternative invasion pathway usage between these two lines . It should be noted that the strategy used here , by recapitulating the GB4 parent and decreasing expression of both PfRh2a and PfRh2b , cannot distinguish between individual effects of these two genes . These data suggest that a previously undiscovered mechanism , a deletion within the PfRh2a/2b intergenic region , affects expression of these genes and can radically influence invasion phenotypes . Establishing whether such deletions are also present in clinical isolates is complicated by the fact that , as noted above , the large extent of identical sequence shared by both genes affects read mapping and hence indel calling . The presence of multiple strains within clinical infections , such as occurs commonly in high transmission regions , would also significantly impact mapping . We therefore first analysed 15 high quality genomes that had been generated from long-read PacBio data , which includes both laboratory and field isolates [35] . One isolate ( KH01 ) had a deletion of the entire PfRh2b gene as well as a neighboring pseudogene PfRh6 ( Fig 6 DelA ) , a significant change that has been previously identified in clinical isolates [36] . A second previously identified deletion was also confirmed in three PacBio sequenced samples ( KE01 , GA01 , GN01 ) ; a 585bp deletion in the unique 3’ end of PfRh2b that maintains the PfRh2b reading frame [37] ( Fig 6 Del B ) . Deletions in the repeat regions between the shared and unique regions of PfRh2a and PfRh2b ( Fig 6 Del C ) were also present in multiple isolates . Finally , a larger intergenic deletion ( 676bp ) , partially overlapping the region with the deletion present in GB4 , was identified in the IT strain ( Brazil ) ( Fig 6 Del D ) . Searching publicly available Illumina data from the Pf3k project revealed that more than 10% of clinical isolates had one of these deletions , making indels at this locus very common . Markedly different distributions were observed , with the deletion of the whole PfRh2b gene ( Del A ) present exclusively in Asia , and the deletion in the unique region of PfRh2b ( Del B ) found primarily in Africa . Accurate estimation of intergenic deletions such as Del D with Illumina data is difficult due to the repetitive nature of the sequence and the high AT content noted above , so frequency of Del D or other intergenic deletions could not be estimated .
Differential usage of alternative invasion pathways is one of the most widely described variable phenotypes in clinical P . falciparum isolates , alongside drug resistance and cytoadherence . Previous attempts to identify the genes responsible for specifying alternate invasion have almost exclusively taken a reverse genetics approach , interrogating candidate genes one by one . These studies have been highly informative , but given the likely multigenic nature of the phenotypes , will always lead to only partial conclusions about the relative importance of different genes . We have applied forward genetics to this problem for the first time , using the parents and progeny of a P . falciparum experimental genetic cross . Combining sensitive phenotyping of invasion into enzyme treated erythrocytes with Illumina sequencing data revealed a single locus responsible for 69% of variation in invasion into chymotrypsin ( CHY ) treated erythrocytes , and 51% of variation in invasion into neuraminidase ( NEU ) treated erythrocytes . This locus contained 15 genes , including the PfRH2a and the PfRh2b genes . The phenotypes correlated with the presence of a deletion in the intergenic region between the two genes , which lie head to head on chromosome 13 , and with a concomitant severe down-regulation of expression of both genes . The primary role of these genes was confirmed by CRISPR-Cas9 genome editing , where disruption of the PfRh2a and PfRh2b genes converted the 7G8 strain to a GB4-like invasion phenotype . While forward genetics is by definition not limited to studying only previously identified candidate genes , it is striking that the unbiased forward genetics approach identified two candidate genes , PfRh2a and PfRh2b . However , while PfRh2a and PfRh2b have been associated with alternative invasion in the past , the central nature of their role in this case would not necessarily have been predicted . The strongest candidate might have been predicted to be the sialic acid dependent ligand , EBA-175 , which binds to its receptor Glycophorin A in a NEU sensitive and CHY resistant manner [5] and when deleted leads to a switch to NEU-resistant invasion pathways [9] , but we found no evidence for an association at this locus . Our data does however fit with previous experimental studies that have focused on the PfRh2a/2b locus . Initially associated with invasion nearly two decades ago [34 , 38] , both PfRh2a and PfRh2b are extensively proteolytically processed , and bind to erythrocytes in a chymotrypsin sensitive and neuraminidase resistant manner [39 , 40] . Antibodies raised against both proteins can inhibit erythrocyte invasion , and it has been extensively studied as a potential vaccine candidate [39 , 41] . In this study , low PfRh2a/2b expression was associated with an increase in invasion into CHY-treated erythrocytes and a decrease in invasion into NEU-treated erythrocytes . Deleting the PfRh2b gene in both the sialic acid-dependent 3D7 strain and the sialic acid-independent W2mef strain resulted in very similar phenotypes , increasing invasion into CHY-treated erythrocytes and decreasing invasion into NEU-treated erythrocytes [8 , 42] . Deletion of PfRh2b alone is also associated with changes in CHY and NEU invasion phenotypes , and domain swap experiments subsequently revealed that it is specifically the transmembrane and cytoplasmic domains of PfRh2b that are functionally important in specifying variable invasion pathways [43] . Therefore , while the indel identified in our QTL analysis is associated with decreased transcription of both PfRh2a and PfRh2b , it is likely that it is specifically the decrease in PfRh2b expression that leads to the change in invasion phenotypes in this genetic cross . These results also concur with previous studies of variation in PfRh2a and PfRh2b in clinical isolates . A naturally occurring 582bp deletion in the unique region of PfRh2b , first identified in Senegalese P . falciparum strains and then found to be common in multiple locations around the world [37] , has been associated with decreased invasion into NEU-treated erythrocytes , although only among parasites isolated from blood group O donors [20] . In addition , single nucleotide variants and other smaller indels within PfRh2b have been associated with NEU sensitive invasion in Brazilian isolates [19 , 44] and decreased PfRh2b expression levels have been associated with an increase in CHY resistant invasion in Tanzanian isolates [22] . There is therefore strong evidence that variation at PfRh2b in clinical isolates is widespread and likely to play a primary role in determining the CHY and NEU alternative invasion pathways . P . falciparum genetic crosses offer a robust tool to identify loci that have a large effect size on a given phenotypic trait . However , the number of progeny for each cross is relatively small , which reduces the statistical power to identify loci with small effects . Controlling for variation at the major locus on chromosome 13 identified a second locus on chromosome 10 that was specifically in linkage with the NEU treatment phenotype , but not the CHY treatment phenotype . This locus contains most notably the PfMSP3 gene family [45] , two members of which , MSP3DBL1 ( MSP3 . 4 ) and MSP3DBL2 ( MSP3 . 8 ) have been shown to bind erythrocytes [32 , 33] as well as IgM [46] . These genes are amongst the most polymorphic in the P . falciparum genome and are under extremely strong balancing selection , which may be due to host acquired immunity or interaction with variable erythrocytes receptors [47] . While these , and other genes within the PfMSP3 family , have been widely associated with invasion , they have not been previously implicated in specifying alternative invasion pathways , emphasizing the utility of unbiased forward genetics for exploring such multigenic traits . Confirmation of which genes within this large locus contribute to invasion into NEU treated erythrocytes will require further work , but members of the PfMSP3 gene family will be the obvious place to start . The suggestion that alternative pathways are generally multigenic is emphasized by the fact that for several phenotypes , progeny clones were more affected by enzyme treatment than either parent ( Fig 1C , S1 Fig ) . While this could be because there are multiple genes each of which has a minor effect , it might also indicate that epigenetic effects may be involved and would not be detected using our sequence-based approach . At least some invasion-associated genes are known to be clonally variant [48] , with repression of invasion genes including PfRh4 associated with H3K9me3-based heterochromatin [49] , and activation associated with H3K9ac [50] . Knockdown of a specific bromodomain protein predicted to bind H3K9ac , PfBDP1 , results in up-regulation of a wide range of invasion-associated genes , including PfRhs and PfEBAs [51] . Epigenetics therefore clearly play a strong role in regulating invasion associated-genes , and as the progeny of a genetic cross have gone through both mosquito and liver stages after the parental lines were crossed , there has been ample opportunity for resetting of the epigenetic code . Given this , the fact that the PfRh2b/ PfRh2a effect reached genome-wide significance in the 7G8xGB4 cross is in some ways surprising , and indicative of its importance . Alternative approaches , such as characterizing the transcriptome , histone modifications and epigentic landscape , may be required to uncover other genes underpinning alternative invasion in future studies . While large-scale systematic studies will be needed to completely disentangle the relative contribution of all genes involved , both reverse and forward genetic studies , and lab and field studies , are clearly converging on PfRh2b as a primary ligand controlling alternative invasion phenotypes . Variations within the PfRh2b gene exist globally , some strongly differentiated in frequency between geographic regions . The receptor for PfRh2b is not known , and it is therefore not clear what selection pressure is driving differentiation at the PfRh2b locus , but it is presumably linked to variation in the expression or sequence of an erythrocyte surface protein . Establishing the specific host-parasite interaction involved will be the critical next step in order to establish why variable erythrocyte invasion is such a common , and globally distributed , phenotype among P . falciparum strains , and to inform the design of strain-transcending vaccines .
Erythrocytes for culture of Plasmodium falciparum parasites were sourced from NHS Blood and Transplant , Cambridge , UK . All samples were anonymized . Use of erythrocytes from human donors for P . falciparum culture was approved by the NHS Cambridgeshire 4 Research Ethics Committee ( REC reference 15/EE/0253 ) and the Wellcome Trust Sanger Institute Human Materials and Data Management Committee . P . falciparum clones from the 7G8 x GB4 ( n = 27 ) and HB3 x Dd2 ( n = 35 ) crosses were kindly provided by Dr . Karen Hayton , Professor Thomas Wellems and Dr Mike Ferdig . Parasites were cultured in complete medium containing 10% human sera , at 5% hematocrit in O+ erythrocytes . Cultures were maintained at 37°C under an atmosphere of 1% O2 , 3% CO2 , and 96% N2 . Before the performance of the invasion assay , parasite cultures were synchronized with 5% D-sorbitol ( Sigma-Aldrich , Dorset , UK ) . DNA extraction for sequencing was undertaken using the QIAamp DNA Blood Midi Kit ( Qiagen ) or the Genomic tip ( Qiagen ) to obtain high-molecular-weight DNA . Erythrocyte invasion assays were performed as described previously [30] . Briefly , uninfected erythrocytes were treated with proteolytic or glycolytic enzymes to remove a subset of cell-surface receptors: neuraminidase ( NEU ) ( final concentration of 20 mU/mL ) , trypsin ( TRY ) ( 50 μg/mL for low trypsin or 1 mg/mL for high trypsin ) , chymotrypsin ( CHY ) ( 1 mg/mL ) and a combination of NEU and TRY . Treated erythrocytes were labeled with the intracellular dye DDAO-SE ( Invitrogen ) , washed and incubated for 48 hours with an equal volume of P . falciparum ring stage parasites ( c . 2% parasitemia ) . After co-incubation , parasites were labeled with the fluorescent DNA-intercalating dye Hoechst 33342 ( Invitrogen ) , and parasites that had invaded labeled cells quantified by two color flow cytometry ( BD LSRII- BD Biosciences ) . Stained samples were examined with a 355 nm UV laser ( 20 mW ) and a 633 nm red laser ( 17 mW ) . Hoechst 33342 was excited using the UV laser and detected with a 450/50 filter and the DDAO-SE was excited using the red laser and detected with a 660/20 filter . A total of 100 , 000 events were collected for each sample . The data collected was analyses with FlowJo . Three technical replicates were performed for each enzyme/strain combination ( i . e . three wells of the same combination performed in a given assay ) , and a minimum of 2 biological replicates were performed ( i . e . each strain was assayed at least twice , on two different days ) . Data is presented as the mean ± the standard error of the mean . Invasion efficiency is represented as the percentage of invasion into enzyme treated labeled cells relative to the percent invasion into untreated labeled cells . All parent and progeny samples were sequenced on the Illumina Genome Analyzer platform ( www . illumina . com ) at the Wellcome Sanger Institute . QTL analysis was performed using a subset of quality-controlled SNPs ( 86 , 158 SNPs ) that were selected from an initial set of potential SNPs by applying a series of quality filters , as described [52] , as well as a subsequent set of SNPs called specifically on these experimental genetic crosses [31] . The PfRh2a and PfRh2b genes were sequenced by Sanger / capillary technology using specific primers that amplify each gene independently [53] . Fifteen P . falciparum strains , including the 7G8 and GB4 parental strains were also sequenced using the PacBio sequencing system as part of the Pf3k Project ( https://www . malariagen . net/projects/pf3k ) . Genome sequence data are available in ftp://ftp . sanger . ac . uk/pub/pathogens/Plasmodium/falciparum/PF3K/PilotReferenceGenomes/DraftAnnotation/ and is summarized in [35] . Illumina data for 2 , 500 P . falciparum isolates , part of the Pf3k project , was screened to identify deletions in the Pfrh2b and PfRH2a genes . Genome wide linkage scans were performed using 5 , 433 SNPs for the 27 clones of the 7G8 x GB4 cross and 4 , 407 SNPs for 30 clones of the HB3 x Dd2 cross . Five clones within the latter cross were removed due to low sequence coverage . The SNP sets were all polymorphic between the parental clones , and there were at least two minor alleles in progeny clones . QTL analysis was performed using the R/QTL software ( www . rqtl . org ) . An average recombination of 13 . 5 kb/cM has been described for the crosses used here [31] , giving an estimate of at least 3 markers per uniquely inherited genome segment ( excluding telomeres , centromeres and sub-telomeres ) . Thresholds for statistical significance across the genome scans were determined using permutation analysis . To identify any additional loci , secondary scans were performed and the models included loci identified in the first QTL . Synchronized cultures enriched in schizonts were used to obtain RNA from the samples . The Ambion Ribopure blood kit and Isolate II RNA mini kit ( Bioline , UK ) was used to perform RNA extraction , followed by DNAse treatment and cDNA synthesis ( Ambion ) . Gene expression for PfRh2b , PfRh2a , PfAMA1 , PfEBA175 , PfEBA181 and PfRh4 was measured using the conditions and primers/probes listed in [19] using qPCR Roche equipment and solutions . For PfMSP7 , PfMSRP1 and PfMSRP2 primers and probes ( labeled with 6-FAM ) were: PfMSP7F: 5’- tgtcgattctcctccttg-3’ , PfMSP7R: 5’- gcacaaagtgaaacagatac-3’ , PfMSP7P: 5’- tcttgtccttgtgttgatatctcttgt-3’ , PfMSRP1F: 5’- tcctcttggttgtgattc-3’ , PfMSRP1R: 5’- gtcccgatgtatcatcaa-3’ , PfMSRP1P: 5’- atgccagaatcaccaagaccaga -3’ , PfMSRP2F: 5’- gtggtgtacttaaatttgatg-3’ , PfMSRP2R: 5’- gggaatcagaagataatacaa-3’ , PfMSRP2P: 5’- ccaaagtccaaggtgctcaagtt-3’ . Relative differences in gene expression were calculated using the ΔΔCq method , with expression of the PfAMA1 gene acting as a control to confirm that parasites were at a similar stage of development . The GB4 parental strain was used as the reference strain . Air-dried thin films of late-stage schizonts were fixed in 4% formaldehyde . Fixed parasites were permeabilised in 1% Triton X100/PBS and blocked in 3%BSA/ 10% goat serum/PBS . Parasites were probed with polyclonal rabbit anti-Rh2b ( 4D1 , [34] ) primary antibody at 1:200 overnight at 4°C . After three washes , the parasites were incubated with a fluorescent secondary antibody ( Goat anti-Rabbit IgG ( H+L ) Highly Cross-Adsorbed Secondary Antibody , Alexa Fluor 488 #A11034 ) diluted 1:500 for 1 hr at room temperature . The samples were washed an additional three times and mounted in Prolong Gold ( Molecular Probes ) with DAPI . Antibody probing and wash steps used 3% BSA/PBS buffer . Images were captured on a Leica DMi8 fluorescent microscope and processed using Leica LAS X software and Photoshop . The genome sequences ( PacBio data ) for GB4 , 7G8 and the 13 other strains were mapped to the reference P . falciparum genome ( 3D7 version 3 . 0 ) using bwa-mem [54] . The alignments were visualized in BAMview [55] to identify indel variants . For field isolates , mapped Illumina reads from the Pf3k samples ( n = 2 , 500 ) were counted within and surrounding candidate regions ( e . g . PfRH2a and PfRH2b genes ) using Samtools software [56] . The coverage counts were normalized to 100-fold for presentation . Deletions were scored by generating ratios of coverage between regions within and flanking the potential deletion . Score cut-offs were chosen based on empirical distributions , with calibration using known deletions , and accounting for any mixed infections . 7G8 parasites were transfected by erythrocyte preloading [57] . Plasmids were obtained from PlasmoGEM: pCC1 , containing the selection cassette , a barcode and homology arms for the common PfRh2a/PfRh2b region; pDC2 with two gRNAs targeting this region of the two genes and Cas9 ( S6 Fig ) . 50μL of each plasmid were used to electroporate 300μL of packed RBCs at 0 . 31kV and 950μF with a Gene Pulser ( BioRad , Watford UK ) . The transfected RBCs were mixed with a parasite culture of 5–6% parasitaemia containing approximately 1 . 5% schizonts . Targeted parasites were selected with 2 . 5nM WR99210 48 hours after transfection for 6 days and the culture was followed until the appearance of parasites . To ensure editing and eliminate plasmid episomes , the culture was treated with 2 . 5nM WR99210 together with 40μM 5FC for a week , after which the parasites were cloned by serial dilution . The clones obtained were confirmed by genotyping with the following primers and sequencing: CAM5 F 5’-ccaatagataaaatttgtagag-3’ , AR1 R 5’-aggtttaatatcgacgagtc-3’ , AR2R 5’-gaacatcatcattcggttc-3’ , BR1 R5’-cgctttctgtaatttcactg-3’ , BR2R 5’-ctagcatcacgttggtc-3’ .
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Plasmodium parasites cause more than 200 million cases of malaria each year . All the symptoms of malaria are caused after Plasmodium parasites invade human red blood cells . Once inside , they grow , multiply and break open the red blood cells to release new parasites . This cycle is repeated every 48 hours , rapidly amplifying the number of parasites and causing severe anemia and other complications . Plasmodium falciparum , the parasite species responsible for almost all malaria deaths , can use multiple different pathways to invade human red blood cells , but the relative importance of each is not well understood . We tested the invasion pathways used by a collection of closely related parasites and compared their genome sequences to identify the genes responsible . This analysis revealed that expression differences in two neighboring genes of the Reticulocyte Binding Homologue family are responsible for most of the variation in two invasion pathways . P . falciparum may use variation in these genes to avoid the immune system or adapt to specific blood groups , which has important implications for vaccine development against malaria .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
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"groups",
"genetic",
"hybrids",
"quantitative",
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"loci",
"plasmodium",
"cloning",
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"apicomplexa",
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] |
2018
|
A forward genetic screen reveals a primary role for Plasmodium falciparum Reticulocyte Binding Protein Homologue 2a and 2b in determining alternative erythrocyte invasion pathways
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Differentiation into well-defined patterns and tissue growth are recognized as key processes in organismal development . However , it is unclear whether patterns are passively , homogeneously dilated by growth or whether they remodel during tissue expansion . Leaf vascular networks are well-fitted to investigate this issue , since leaves are approximately two-dimensional and grow manyfold in size . Here we study experimentally and computationally how vein patterns affect growth . We first model the growing vasculature as a network of viscoelastic rods and consider its response to external mechanical stress . We use the so-called texture tensor to quantify the local network geometry and reveal that growth is heterogeneous , resembling non-affine deformations in composite materials . We then apply mechanical forces to growing leaves after veins have differentiated , which respond by anisotropic growth and reorientation of the network in the direction of external stress . External mechanical stress appears to make growth more homogeneous , in contrast with the model with viscoelastic rods . However , we reconcile the model with experimental data by incorporating randomness in rod thickness and a threshold in the rod growth law , making the rods viscoelastoplastic . Altogether , we show that the higher stiffness of veins leads to their reorientation along external forces , along with a reduction in growth heterogeneity . This process may lead to the reinforcement of leaves against mechanical stress . More generally , our work contributes to a framework whereby growth and patterns are coordinated through the differences in mechanical properties between cell types .
Organismal development relies on both the progressive differentiation of cells according to specific spatial patterns and the growth of tissues and organs towards their target shapes . On the one hand , numerous studies have addressed differentiation mechanisms , leading to a framework where differentiation patterns depend on the establishment of biochemical gradients , see e . g . [1] . On the other hand , it has been shown that simple growth rules can lead to complex morphologies , such as for tumors [2] or ruffled leaves [3–5] . However , the coordination between patterning and growth has received much less attention [6–8] . Are patterns passively stretched by growth like drawings on an inflated rubber balloon , or do patterns remodel during tissue growth ? This question is central to the present study . As growth entails dynamic changes in the structural elements that define shape , such as the cytoskeleton or the extra-cellular matrix , it is essential to address the physical properties of these elements and how these properties are controlled at the cellular level [9–17] . In this framework , cell mechanics would provide a direct link between biochemical activity and growth . Accordingly , the question above can be reformulated as follows . Do the patterns of cell differentiation correspond to patterns of changes in mechanical properties ? If so , do changes in mechanical properties predict the geometry of the patterns when the organ reaches its target shape ? In addition , what would be the functional role of such changes in the geometry of patterns ? Here we use a combination of experiments and mechanical modeling of growth to address these questions within the context of leaf vasculature . The leaves of dicotylodonous flowering plants and their vasculature provide a fitting context for the study of patterns on growing tissues . Leaves grow manyfold from a sub-millimetric size to several centimeters [18 , 19] . They are amenable to genetic [20] or physical manipulation; finally , they can be analyzed quantitatively , being almost two dimensional [21 , 22] . Vasculature in dicotyledons is an elaborate reticulated network with striking geometrical and statistical properties , as revealed by advanced mathematical quantification [23–29] . Throughout the leaf’s growth , the network multiplies its size by orders of magnitude while maintaining its crucial structural and functional properties [30 , 31]: due to their rigidity [32] , veins are the main carriers of mechanical loads in the mature leaf . On the other hand , veins are responsible for the transport of nutrients and water . With this respect , the leaf’s ability to withstand damage of one vein is often ensured by redundancy: the network is reticulated ( featuring loops ) , allowing for alternative routes . Consequently , the venation network , through its topology and geometry , is thought to optimize both its mechanical [33] and transport properties [34] . Finally , vasculature and leaf development appear to be tightly coupled [35 , 36] . In many species , the differentiation of ground cells into provascular cells is completed when the leaf is millimetric in size [30] . This process of differentiation is dependent on a biochemical field: the distribution of the phytohormone auxin . The canalization model [37] suggests that the salient features of venation networks are due to instabilities of this field—an initially homogeneous concentration field evolves into a hierarchical network of localized concentrated flow of transported auxin , which eventually becomes the vein system . Canalization has received genetic and molecular support [38–40] , while numerical simulations showed that the model accounts for many features of vasculature [41–43] . However , additional hypotheses on transport or on auxin production are needed to account for loops [44 , 45] . An alternative model [46] proposed that the mechanical stress field regulates differentiation into provascular cells , motivated by the resemblance between the vascular network seen in leaves and the network of cracks in drying mud , which is known to be created by instabilities of the stress field . Numerical simulations of this model [47 , 48] reproduced many features of the network geometry . However the stress field model of differentiation has not received mechanistic support so far . Here , we do not investigate the process of differentiation of veins , but rather how the vascular network reaches its final geometry . Indeed , after vein formation has ceased the leaf may continue to grow in area by an order of magnitude . Plant growth is driven by the osmotically generated turgor pressure and restrained by cell walls ( the extracellular matrix ) ; therefore , mechanical stress can accumulate: for instance , slits made in stems tend to open , indicating that the epidermis is in tension . In leaves , since veins are stiffer than their surrounding environment [32] , the vascular network is expected to carry most of the accumulated stress , which might lead to geometrical deformations of the network . This led to the ‘force model’ describing the final geometry of junctions in vasculature [23]: each vein pulls with a force that is proportional to its diameter , and the requirement of local equilibrium at vein junctions leads to a statistical correlation between veins’ diameter and the angles between veins; this correlation was found to hold in the leaves of many cotyledons [23] . More recently , a cell-based mechanical model was developed to describe the time-evolution of the vascular network [49] . The tissue was modeled as a network of viscoelastic cell walls , and vein cells were distinguished from ground cells by their higher rigidity . This yielded realistic venation patterns and reproduced the experimental findings of [23] , in line with the force model . However , these studies remain correlative and do not prove that mechanical forces shape the vascular network . Here , we probe the force model by perturbing mechanically a growing leaf and making predictions about the effect of such a perturbation on the vascular network . We use the texture tensor [50] to quantify this effect; we simulate networks on a tissue that grows anisotropically and predict how leaf vasculature is affected by stretching; we apply external forces to growing leaves after veins have differentiated , and compare observations with predictions .
We consider situations in which a leaf grows anisotropically as the result of the application of external forces . More generally , we are interested in the evolution of patterns , here vascular networks , on a growing tissue ( Fig 1A ) : how does the pattern change with growth ? Is it merely stretched passively or does its geometry change in a more complex manner ? This question is reminiscent of the nature of deformations in elastic solids; in homogeneous solids , elastic deformations are affine , i . e . the local strain is the same as the large-scale strain , whereas in heterogeneous solids , elastic deformations are non-affine , i . e . the local strain differs from large-scale strain [51] . Among biological materials , non-affinity was observed for collagen fibers [52] . Our question therefore amounts to whether growth ( an irreversible deformation ) is affine or not . Fig 1B shows a portion of a leaf that was subject to external mechanical stress during two weeks of growth . It is clear qualitatively that the network in the region of the leaf that was grown under tension looks stretched while on the other side it is unaffected . Yet one needs a mathematical method to quantify the strength and orientation of this deformation . Deformation is a tensor , meaning that at each point of the leaf , deformation can occur in many directions: imagine that we draw small circles on the leaf . After growth , each circle will become an ellipse . In order to fully characterize growth , one needs to quantify the orientations and areas of the ellipses , as well as their anisotropies ( i . e . how elongated the ellipse is ) . Since we are interested in the quantification of the geometrical properties of a network , we use the texture tensor . The time-derivative of this tensor was proposed as an equivalent of the elastic strain tensor for the quantification of local deformations in materials with a cellular-like structure [50] and has been used to analyse epithelial morphogenesis [53] . It measures the local geometry of a network , and its time-evolution is a measure of the network’s deformation . Using the texture tensor enables capturing both the averaged , continuum-like deformation as well as the local , discrete deformation of the network’s elements . We give here a qualitative description of the tensor’s definition and properties ( see Materials and Methods for details ) . The texture tensor , which we denote by M , is defined for materials that have a network-like structure , and therefore has a natural application in our case . A network ( a graph , in mathematical language ) is composed of nodes and links that connect between them . In this paper we define the graph by using the areoles ( areas surrounded by veins ) as the nodes , and we define two areoles as linked ( neighbors ) if they share a common vein on their boundary . The local texture tensor is defined from the vectors linking the center of an areole to the centers of its neighboring areoles , as sketched geometrically in Fig 1C ( see Eq 1 in Methods for the exact definition ) . Thus , the texture tensor contains information not only about the geometry of a single areole , but also about the local topology . In order to obtain properties averaged at the scale of a few areoles , we also define the averaged texture tensor from a spatial smoothing ( with a constant Gaussian weight ) of the local tensor . Since the texture tensor is a symmetric 2nd order tensor , it describes an ellipse , which is a measure of the local shape of the network . The area of the ellipse ( the determinant of texture tensor det ( M ) ) quantifies the size of areoles , while anisotropy corresponds to the ratio of the greater axis to smaller axis of the ellipse ( ratio of eigenvalues of M ) ; note that in this definition anisotropy is always larger than unity . We now turn to testing the model in experiments . To do so , we chose to work with leaves in which the vein network has already formed , so as to avoid a direct coupling with differentiation mechanisms . We sought a species such that ( i ) a large number of veins would improve the statistics and ( ii ) veins are apparent on photographs to allow for a non-perturbative time-lapse analysis of the geometry of the network . It turned out that bay laurel ( Laurus nobilis ) was appropriate as seen in Fig 1A . Each leaf was loaded by a U-shaped spring , glued to two points on its edge , typically 3mm apart ( Fig 1 ) . The loaded leaf was allowed to grow for 15 days , during which it multiplied its area by about one order of magnitude . The vascular system of the entire leaf was repeatedly photographed . The images were processed and the geometry and topology of the vascular network were extracted . The unstretched half of the leaf was considered as a control . The robust qualitative results were observed in a dozen bay leaves . The detailed mathematical analysis was preformed on three bay leaves . Qualitatively similar results were obtained with tobacco leaves ( Nicotiana benthamiana , S2 Fig ) . We sought to reconcile simulations with experimental data . The broad distribution of non-affinity ( q ) with no stretching ( Fig 6C ) suggests that the venation network is affected by noise . Indeed , the ‘force model’ was observed to hold only approximately and vein thickness is broadly distributed [23] . We therefore modified the initial state of the rod network by adding noise in rod thickness; each value of thickness was multiplied by a random number uniformly distributed between 1 − r and 1 + r . We started the simulations from this state and observed non-affine growth . The distribution of q is shown in Fig 7A for a noise of r = 40% ( ratio of standard deviation of thickness to its average ) , a value that was chosen to match the observed distribution on the unstretched side of the leaf ( Fig 6C ) . Consequently , a frozen noise in vein thickness is sufficient to retrieve observations of non-affinity in unstretched leaves . Finally , non-affinity decreases with vein thickness ( S3 Fig ) meaning that areoles surrounded by thick veins tend to grow less than their neighborhood in the presence of noise . However , when we added external stress to the simulation , we found again that the distribution of q was broadened ( Fig 7A ) , in contrast with the experimental trend . Thus , we hypothesized that the growth equation , according to which vein elongation rate is proportional to vein tension , was not sufficient to model the system . The narrowing of non-affinity distribution in stretching experiments suggests that high tensions have relatively more effects on vein growth . Accordingly , elongation rate should be a concave function of vein tension . We then recalled that the commonly accepted plant growth law , the Lockhart equation [10 , 16 , 56] , is nonlinear and concave: elongation occurs only above a threshold stress and is then an affine function of stress . We incorporated this into our model and added a threshold to the growth equation ( Eq 3 in Methods ) in the form ν h i l i 0 d l i 0 d t = max 0 , T i - h i η where ν is the effective viscosity , l i 0 is the rest-length of the i-th rod , Ti is the tension in the i-th rod , and η is the threshold stress for elongation . We first chose the value of η equal to η0 = 6 that corresponds to the average vein stress at the first step of simulations . We repeated the stretching simulations with this new growth law and we found that the non-affinity ( q ) distribution narrowed under unidirectional external stress ( Fig 7B ) , as in experiments . Moreover the distributions of q for σx = 0 and σx = 2Ptur are quite similar to experimental distributions . In addition , we found that other growth laws ( quadratic , with a maximum , with a saturation ) yield a broadening of the distribution of q when tensile external stress is applied ( S4 Fig ) . Finally we investigated the robustness of the model by studying the sensitivity of this behavior to the value of the stress threshold . The control parameter was the normalized stress threshold η⋆ = η/η0 , η⋆ = 1 corresponding to our first successful trial . We did not consider values of η⋆ < 0 . 1 as the model converges to the initial model with no threshold , as well as η⋆ > 1 . 3 as the tissue stopped growing because the tension in all rods remains below the threshold . With no external stress , increasing the threshold broadens the non-affinity distribution , as shown in Fig 7C . With high external stress ( σ = 2Ptur ) , the distribution of q is insensitive to α as the behavior of the system is then dominated by external stress . Importantly , the distribution of q is broader with no stress , as in experiments , when η⋆ > 0 . 7 . We therefore conclude that our model reproduces experimental observations when including noise and a Lockhart-like growth law as long as the mean vein tension is not much higher than the threshold tension .
In order to address the coordination between patterns and growth during the course of organismal development , we studied the response of leaf vasculature to external stress . More specifically , we investigated whether leaf vasculature is merely dilated by growth , like a drawing on a balloon that is inflated , or whether growth is non-affine . We combined the simulations of a two-dimensional mechanical model of vasculature with the experimental manipulation of leaves in which veins have formed . The main assumption of the model was that veins are much stiffer than ground tissues . The application of anisotropic external mechanical stress resulted in elongated areoles in simulations; on average , the long axis of the areole corresponded to the direction of the maximal stress . To quantify this effect , we used the texture tensor , which is a good measure of the local geometry of the network . We found that , overall , the anisotropy of the texture tensor increased with the level of stress . We then used the texture tensor to quantify experiments on leaves . While measuring the geometry of leaves before stress application , we retrieved known features of leaf geometry and growth . On the one hand , areoles are bigger and more anisotropic near the base of the leaf , which might be ascribed to an enhanced growth , reflecting the gradient in maturation along the leaf axis that occurs at the later stages of leaf development [22 , 57] . On the other hand , the anisotropy of areoles follows the left-right symmetry of the leaf and its local geometry; the major axis of areoles parallels secondary veins and the margin , while on average , it is aligned with the leaf axis . When external mechanical stress is applied to the leaf , areoles become elongated in the direction of the largest stress , as in simulations . Nevertheless , the elongation of areoles might only be a passive consequence of the largest growth in the direction of external force . To test this possibility , we used the texture tensor to quantify non-affinity . We found that , both in simulations and experiments , the local change in texture tensor differed from the average change , demonstrating that growth is heterogeneous and non-affine . However the simulated distributions differed in behavior from experimental distributions . Therefore we modified the model by incorporating noise in thickness and a threshold in the growth law . Both this model and experiments featured heterogeneity in growth , which was reduced upon external stretching . A threshold in the growth law was introduced by Lockhart [56] to describe experimental data showing that a minimum turgor pressure was needed for growth to occur . This model is well-supported in situations with growth along one axis , as in single cells or in cylindrical plant organs [10 , 16] . Our results further support this model in a two-dimensional setting . While we cannot exclude more complex hypotheses involving biochemical feedbacks , it is more parsimonious to ascribe our observations to the vein mechanics that induce non-affine growth . It is still left to find out whether they can be explained by a simple viscoelastic behavior of the veins as implemented in the model , or whether they also involve a more sophisticated regulation process . In the former case , it suffices that veins have a specific ‘mechanical identity’ , being stiffer than ground tissues , as is obvious in mature leaves [32] . If additional regulation existed , it might be manifested , for example , by softening of cell walls in correlation with stress , or by preferential thickening of veins that carry higher loads . However none of the growth laws that we tried yields results that agreed with observations , except the one with a threshold . In this context , one should note that the effect of stress exists only when the leaf is growing: we did not observe any measurable effect when we applied stress to mature leaves that do not grow in area , or to areas that stopped growing within a growing leaf . Pursuing this direction , we wondered whether non-affinity was also applicable to the earlier stages of leaf development . We thus examined leaf primordia in Arabidopsis thaliana . While this species does not fulfill the requirements stated above for an experimental investigation of the effects of external forces , many molecular and genetic resources are available , such as a reporter for early vascular identity ( pVH1::GUS , see Materials and Methods ) . Using this reporter , we visualized veins in dissected leaf primordia ( S5 Fig ) ; the midvein appears to be smooth and almost straight before tertiary veins have formed , while at later stages it features kinks at the junctions with secondary veins ( S5 Fig ) . This observation indicates that the shape of the midvein does not change according to a simple dilation of the leaf but rather that growth is inhomogeneous and influenced by the local geometry of vasculature , consistently with our observations on older leaves . This might seem at odds with the work in [8] , showing that growth fields in early leaves can be accounted for by the affine dilation of an initial polarity pattern , but this work considered younger primordia: tertiary veins appear only at the end of the periods monitored there . To conclude , we showed that , in leaves in which the vasculature has formed , veins reorient in the direction of applied external forces , and that the geometry of the midvein suggests that this also applies to leaves in which vasculature is differentiating . It would be interesting to investigate whether this is relevant to vasculature in animals [58 , 59] , to veins in insect wings , or more generally to netted patterns of differentiation in other growing tissues . Our results further support the force model [23 , 49] , according to which most of the mechanical load is carried by the veins ( or equivalently , that the veins are stiffer ) and that the tension in each vein is proportional to its thickness . Our results may imply that the network changes so as to become reinforced in the direction of the main stress . This reinforcement would be reminiscent of Wolff’s law according to which bone remodels so as to resist changes in mechanical stress , or of the reorientation of cortical microtubules in plant cells according to the direction of highest stress [60–62] . Similarly to these studies , applying external stress helped us identify a response to internal stress , which can result from differential growth . However , we note that the reduction in growth heterogeneity with higher anisotropy of mechanical stress differs from work in the shoot apex showing that the reorientation of cortical microtubules according to external forces induces growth heterogeneity at the cell scale [63] . These mechanisms operating at different scales might reflect a form of homeostasis , in which the tissue becomes anisotropically stiffer so as to resist the effect of external forces , and which would also underlie the coordination between patterning and tissue growth .
Deformations and growth are associated with the mathematical concept of a second rank tensorial field . The texture tensor was proposed in [50] for quantifying local geometry in materials with cellular-like geometry; the time-derivative of the texture tensor allows the quantification of geometry . In this paper we define a graph by taking the areoles to be the graph’s nodes , and defining two areoles as connected if they share a common vein . One can also define the dual graph , whose nodes are the vein junctions , linked by veins . This method gives similar , yet more noisy , results . For each node i , located at r → i , the local texture tensor is defined as M i = 1 N ∑ k ( r → k - r → i ) ⊗ ( r → k - r → i ) ( 1 ) where the summation runs over all the neighbors if the site i , N is the number of r → i’s neighbors , and ⊗ denotes the standard 2D tensor product , defined by ( u → ⊗ v → ) α β = u α v β where α , β are Cartesian coordinate indices . In experiments , the texture tensor is undefined for areoles that are on the boundary of the leaf . The process is sketched geometrically in Fig 1C . This gives the local texture tensor , which is defined only on the graph’s nodes r → i . To get the averaged texture tensor , M ( r → ) , which is a continuous field defined everywhere on the leaf , the local tensor is averaged over the whole leaf with a Gaussian weight centered at r → . The width of the Gaussian , σ , is chosen so that the area πσ2 is 30 times the mean area of an areole . This value of 30 was determined to reveal general trends , but the results were insensitive to the width of the Gaussian , in a range around this value . When the averaged tensor is used for an areole , we take its value M ( r → i ) at the areole center r → i . At each point the texture tensor field describes an ellipse , which is a measure of the local shape of the network . The determinant measures the area of the ellipse and the directions of the tensor’s eigenvectors indicate the ellipse’s orientation . The eigenvalues are the lengths of the ellipse’s axes , and we define the anisotropy of the tensor to be the ratio of the larger to the smaller axis . Note that by definition the anisotropy is always larger than 1 . At each time step , we compare the local texture tensor Mi ( t ) of each areole i to the averaged texture tensor M ( r → i , t ) , using the ratio of their determinants . If dilation were locally homogeneous , or equivalently if growth were affine , this ratio would be independent of time , because the geometry of the network would be the same up to a magnification factor . Therefore we define the non-affinity index of areole i between time t1 and t2 as q i = det M i ( t 2 ) det M ( r → i , t 2 ) / det M i ( t 1 ) det M ( r → i , t 1 ) . ( 2 ) If the network was affinely dilated , then q = 1 in all areoles because the ratio of detMi ( t ) to det M ( r → i , t ) would be time-independent . The deviation of q from unity quantifies the differences between the local and averaged behavior of the areole . An equivalent index can be defined using areal growth [64] , see S6 Fig , that is related to the coefficient of variability of growth introduced in [63] . As described earlier , the simulations were built upon the work of F . Corson et al [49 , 54] . We give here a brief description of the model , and refer the reader to [49] for details . Corson’s model consists of an array of interconnected viscoelastic rods , modeling the cell walls , in a two-dimensional periodic boundary condition space . The difference between cell walls of the background tissue and cell walls of the vascular tissue is manifested in their elastic properties—vein cell walls are stiffer when oriented with the direction of the vein . The simulations were divided to two stages: In the creation stage , a ‘reference’ network was created using Corson’s model , which yields networks statistically similar to real venation networks [49] . In the reorganization stage , creation of new veins was arrested , and the network was transformed into an ‘effective’ network , where each vein was replaced by a viscoelastic rod , with the same thickness hi and rest length l i 0 , given by those of the vein that it represents . The background tissue was erased . The process is shown in S7 Fig . In order to have an ideal initial configuration , we further optimized vein thickness so that the tension carried by each vein is proportional to its thickness . The linear viscoelastic behavior of the rods is manifested in the change of the rods’ rest length , given by T i = μ h i l i l i 0 - 1 = ν h i l i 0 d l i 0 d t ( 3 ) where Ti is the tension in the i-th rod , μ is the vein’s Young modulus , ν is its viscosity , and l i , l i 0 are its length and rest-length , correspondingly . The network was grown in quasi-static conditions , at each time step minimizing the elastic energy of the network , which is given by E e l = ∑ i ∈ veins 1 2 μ h i l i l i 0 - 1 2 - P t u r S - E a n i , ( 4 ) where is the turgor pressure , and S is the total area of the network . While Corson’s model was restricted to isotropic stress , we introduced an external stress by an anisotropic term in the energy: E a n i = W H ϵ i j σ i j = H 0 ( W - W 0 ) σ x x + W 0 ( H - H 0 ) σ y y ( 5 ) where W , H , W0 , H0 are the network’s width and height , reference width and reference hight , respectively . The definition of the reference width and hight is done by calculating the equilibrium configuration of the network without the term Eq ( 5 ) in the energy equation . The rod model was implemented in C . The energy is minimized according to the BFGS algorithm using the NLopt library . The system of ordinary differential equations is solved using the GNU Scientific Library . All parameters were set to 1 except for μ = 300 . Thus the typical strain was around 0 . 02 in the initial conditions . The energy was minimized every Δt = 10−5 . The experimental set-up consists of attaching a U-shaped steel wire stretcher to a growing leaf , using epoxy glue . After polymerization , the glue was attached to the leaves’ trichomes . The leaves showed no pathologic behavior in response to the glue as could be checked in leaves where two glue drops were deposited with no spring . The applied stress is of same order of magnitude as the turgor pressure . We present a rough estimation: The order of magnitude of the stress is σ ≈ F/S where F ≈ 1 grams ≈10N and S is the surface of the stretched area , perpendicular to the applied force . We estimate the affected area to be about 1 cm wide . The leaf thickness is of the order of 1mm . Therefore we have σ = F S ≈ 10 N 10 - 3 m 10 - 2 m = 10 6 P a = 10 atm which is of the same order of magnitude as the turgor pressure . In the numerical model , the external stress was in the range 0 < σ/Ptur < 2 . In order to quantify the vascular network , the leaf was photographed using a commercial digital camera ( Nikon CoolPix 8800VR ) , with strong back-light . The different optical properties of the vascular network allow it to be easily distinguishable from the rest of the leaf . The vascular network was then extracted from the image either by semi-automated image processing methods ( written in Matlab ) or manually . During several repetitions we noticed that the effect of external force is much more pronounced when stretching close to the base of the leaf , which might be ascribed to the fact that in later stages of development , growth is concentrated near the base of the leaf [22 , 57] . We used Arabidopis thaliana Col-0 transgenic plants expressing β-glucuronidase under the control of the promoter of VASCULAR HIGHWAY 1 ( pVH1::GUS ) , an early vascular marker [65] . Plants were grown in soil in long day ( 16hrs day/8hrs night ) conditions and at 20–22°C and harvested two weeks after sowing . The plants were stained for GUS activity in 10mM sodium phosphate buffer ( pH 7 ) , 10mM EDTA , 0 . 1% Triton X-100 , 0 . 5g/L X-glucuronic acid , and 10mM ferri- and ferro-cyanide for 24hrs at 37°C and subsequently cleared in 70%-100% ethanol for 2 days . Leaves were dissected and mounted in 70% glycerol and pictured with a Zeiss Axiophoto microscope and Axiovision software .
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The development of an organism involves a coordination between the differentiation of cells in well-defined spatial patterns and the growth of tissues towards their target shapes . While extensive research has addressed each of these key processes , their coordination has received less attention . In particular , when a pattern has formed and the tissue continues growing , is the pattern passively dilated like a drawing on an inflated balloon , or does the pattern remodel during tissue expansion ? We address this question in the context of leaf vasculature and examine the role of mechanics in leaf growth . We model the growing vascular network and identify quantities that compare network growth to background tissue growth . We apply this quantification to mature leaves that are stretched mechanically; we find that vasculature does not dilate passively and that veins reorient in the direction of external forces . This is reminiscent of the reinforcement of bones or of the cytoskeleton so as to resist to mechanical stress . In a developmental context , this might be an essential process to match patterns and growth .
|
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"Abstract",
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2016
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Mechanical Stress Induces Remodeling of Vascular Networks in Growing Leaves
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Nutrigenomics investigates relationships between nutrients and all genome-encoded molecular entities . This holistic approach requires systems biology to scrutinize the effects of diet on tissue biology . To decipher the adipose tissue ( AT ) response to diet induced weight changes we focused on key molecular ( lipids and transcripts ) AT species during a longitudinal dietary intervention . To obtain a systems model , a network approach was used to combine all sets of variables ( bio-clinical , fatty acids and mRNA levels ) and get an overview of their interactions . AT fatty acids and mRNA levels were quantified in 135 obese women at baseline , after an 8-week low calorie diet ( LCD ) and after 6 months of ad libitum weight maintenance diet ( WMD ) . After LCD , individuals were stratified a posteriori according to weight change during WMD . A 3 steps approach was used to infer a global model involving the 3 sets of variables . It consisted in inferring intra-omic networks with sparse partial correlations and inter-omic networks with regularized canonical correlation analysis and finally combining the obtained omic-specific network in a single global model . The resulting networks were analyzed using node clustering , systematic important node extraction and cluster comparisons . Overall , AT showed both constant and phase-specific biological signatures in response to dietary intervention . AT from women regaining weight displayed growth factors , angiogenesis and proliferation signaling signatures , suggesting unfavorable tissue hyperplasia . By contrast , after LCD a strong positive relationship between AT myristoleic acid ( a fatty acid with low AT level ) content and de novo lipogenesis mRNAs was found . This relationship was also observed , after WMD , in the group of women that continued to lose weight . This original system biology approach provides novel insight in the AT response to weight control by highlighting the central role of myristoleic acid that may account for the beneficial effects of weight loss .
The main function of adipose tissue ( AT ) is to store excess energy as triglycerides and to release non-esterified fatty acids ( FAs ) for other tissues during periods of energy demand . AT also releases numerous peptidic/proteic and lipidic factors with signaling functions [1–3] . Obesity is characterized by an excess fat mass with deleterious health consequences . AT expansion results in dysfunctional non-esterified FA release and imbalance in production of anti/pro-inflammatory mediators [4] . Most of the obesity-related metabolic disturbances are reversible with weight loss [5] . However in obese individuals , weight fluctuations are frequent since individuals involved in dieting-induced weight loss are often unsuccessful at long last [6 , 7] . Adaptations occurring in AT during dietary weight management programs remain unclear especially regarding weight control after dieting [8] . The FA composition of AT reflects balance between exogenous FAs from food , triglyceride hydrolysis/synthesis and FA synthesis from glucose-derived acetylCoA , so-called de novo lipogenesis ( DNL ) . Studies on FA composition of AT during weight control trials are scarce [9 , 10] . Low 16:1 ( cis-9 ) ( palmitoleic acid ) and 14:1 ( cis-9 ) ( myristoleic acid ) may predict favorable weight control outcome [11] . Omics , especially transcriptome studies , have proved great potential in clarifying the role of AT biology with respect to response in weight controlling trials [12] . However , analyses based on single omics often do not provide enough information to understand biology . The integration of multiple omics may give a better understanding of a biological system as a whole . Global network-based approaches authorize multiple datasets analyses and carry the advantage of highlighting functionally related pathways and biological entities of potential relevance as hubs [13] . Networks are valuable models to dissect complex traits [14] . However , integrative analysis of datasets of different data types raises the issue of different scales of the multiple datasets . In gene expression networks , clusters are more robust than individual interactions [15] . Multivariate statistical approaches were recently developed to jointly analyze omics datasets , dealing with high dimension and using variable selection [16] . The present study aimed at revealing the characteristics of AT biological networks relevant to clinical traits during a long-term dietary intervention ( DI ) including calorie restriction and ad libitum follow-up after weight loss . Studies on human AT gene expression or lipidomic profiles from a systems biology point of view have only been reported at baseline [17 , 18] but not during DI . Network modeling has recently been applied using metagenomic , plasma and AT inflammatory markers to predict weight changes during stabilized weight loss [19] . To our knowledge , no study has jointly investigated AT lipidic and gene expression profiles , especially during long-term DIs . Here , the global AT networks were computed using FAs , mRNA levels , clinical risk factors and biochemical markers according to weight changes in the same individuals . Our purpose was to identify common as well as differential signatures with relationship to bio-clinical factors . The identification of novel AT features associated with weight regulation may influence our understanding of weight control and authorize new advances in obesity management .
Ethics statement . The samples investigated in this paper were collected from 2006 to 2007 during the DiOGenes study , a pan-European randomized DI trial which was approved by the ethics committees of each of the 8 European centres participating to the program ( registration no . NCT00390637 ) . Written informed consent was obtained from each patient according to the local ethics committee of the participating countries: 1 , Medical Ethics Committee of the University Hospital Maastricht and Maastricht University , The Netherlands; 2 , The Committees on Biomedical Research Ethics for the Capital region of Denmark , Denmark; 3 , Suffolk Local Research Ethics Committee , UK; 4 , University of Crete Ethics Committee , Greece; 5 , the Ethics Commission of the University of Potsdam; 6 , Research Ethics Committee at the University of Navarra , Spain; 7 , Ethical Committee of the Institute of Endocrinology , Czech Republic; 8 , Ethical Committee to the National Transport Multiprofile Hospital in Sofia , Bulgaria . Study design . The data presented in this paper are part of those collected during the DiOGenes study ( contact information at www . diogenes-eu . org ) The DiOGenes project investigated the effects of diets with different content of protein and glycemic index on weight-loss maintenance and metabolic and cardiovascular risk factors after a phase of calorie restriction , in obese/overweight individuals . The trial protocol and supporting CONSORT checklist are available as supporting information; see S1 Protocol and S1 Checklist . Healthy overweight ( body mass index ( BMI ) ≥27 kg/m2 ) individuals , aged <65 years were eligible for the study . Exclusion criteria were BMI 45 kg/m2 , liver or kidney diseases , cardiovascular diseases , diabetes mellitus ( type 1 or type 2 ) , special diets/eating disorders , systemic infections/chronic diseases , cancer within the last 10 years , weight change >3 kg within the previous 3 months , and other clinical disorders or use of prescription medication that might interfere with the outcome of the study . A detailed description of inclusion and exclusion criteria has been published previously [20] . BMI was calculated by dividing weight in kilograms by the square of height in meters . Waist circumference was measured between the bottom of the ribs and the top of the hip bone . A detailed description of the DiOGenes intervention trial and main outcomes can be found in previous core publications [20–22] . Briefly , after the first clinical investigation day ( baseline ) , eligible individuals followed an active weight loss phase of 8-week low calorie ( 3 . 3–4 . 2 MJ/d ) diet ( LCD ) using commercial meal replacements ( Modifast , Nutrition et Santé ) . The individuals with ≥ 8% of initial body weight loss during LCD were randomized into one of five ad libitum weight maintenance diets ( WMD ) for 6 months: 4 diets combining high and low protein content with high and low glycemic index of carbohydrates , and a control low fat ( 25–30% energy ) diet according to National dietary guidelines on healthy diets [22] . During WMD , the individuals were provided dietary instruction as described in [22] . Dietary intake was assessed at screening , 4 weeks after the beginning and at the end of WMD . The subjects were asked to complete a 3-day weighed food record , including 2-week days and 1 weekend day . Dietary records were validated by a nutritionist . Clinical investigations including anthropometric measures ( height , weight , waist circumference , body composition ) , blood pressure measurements , fasting blood sampling , and subcutaneous AT biopsies were performed at baseline ( BAS ) and at the end of each phase . All procedures were standardized between the 8 study centers across Europe [21] . Fig . 1 displays the organizational flowchart through the trial protocol and the individuals’ selection from the DiOGenes cohort for the present study . Patients and adipose tissue study . Biopsy samples were stored at -80°C until total RNA and FA extractions . The lipid fraction was extracted from the fat cake produced during total RNA extraction using gas chromatography as described in [11] . The list of FA extracted from the lipid fraction is presented in S1 Table . After RNA extraction the mRNA levels of a panel of 221 genes selected from previous published and unpublished DNA microarray analyses on limited number of individuals as described in [23] was assessed using high throughput real-time PCR as described in [24] . S2 Table describes these genes according to biological pathways and the biological function of the protein encoded . The list includes 68 genes previously shown as markers of subcutaneous AT from obese insulin resistant subjects with metabolic syndrome [25] , 65 genes described as markers of subcutaneous AT from lean individuals [25] , 33 genes selected from previous caloric restriction induced weight loss studies [26 , 27] , 27 markers of weight changes after caloric restriction [28] , and 28 unpublished predictors of weight change to distinguish between those subjects that will regain weight after LCD from those that will succeed weight maintaining based on the AT transcriptome at baseline or after the caloric restriction phase . These genes encoded proteins involved in various pathways such as metabolism ( 47 . 5% of the transcripts ) , immune response ( 19 . 5% ) , transport ( 4 . 5% ) , cell and tissue structure ( 3 . 6% ) , signal transduction ( 2 . 3% ) and response to stress ( 1 . 4% ) . A subgroup of the DiOGenes cohort was selected based on the availability of the FA and gene profiling quality data . Here , among the 214 individuals with both AT gene expression and FA content available at all steps of the DI , i . e . BAS , LCD and WMD , only premenopausal women were studied ( n = 135 ) . After LCD , the women were classified a posteriori into 3 separate groups according to weight changes during WMD , calculated by subtracting body weight at LCD to body weight at WMD . Subjects who experienced a weight loss or a weight regain greater or equal to 2 kg during WMD were classified as weight losers ( WL ) ( n = 45 ) or weight regainers ( WR ) ( n = 51 ) , respectively . Individuals with weight change of less than 2 kg were classified as stable weight ( WS , n = 39 ) . Data availability statement . Raw and processed RT-qPCR data files were deposited at the Gene Expression Omnibus depository and are available under series accession number GSE60946 . Other data data are available upon request . Data were first analyzed by multivariate statistical methods using principal component analysis to detect center or diet group biases and mean-centered transformed if needed . Gaussian distribution of data was tested using the Kolmogorov–Smirnov test and log transformed adequately . Differences in clinical data , mRNA and FAs between BAS , end of LCD and end of WMD were tested using one-factor repeated measure ANOVA with Bonferroni post-hoc test . The differences between each group ( WL , WR and WS ) at each step of the DI were tested with one-factor ANOVA and Bonferroni post-hoc test . Fatty acids and gene expression data were controlled for multiple testing by using Benjamini-Hochberg P value correction ( q-value ) [29] . Analyses were performed with SPSS Statistics 17 . 0 software ( SPSS Inc . , Chicago , Ill ) . The network analysis was performed as illustrated in Fig . 2: for BAS , the end of LCD and the 3 groups at the end of WMD ( WR , WL and WS ) , a system model was designed using a global network . The network was built using a 3 step approach . A first step consisted in inferring a network in each set of variables ( bio-clinical , FAs and mRNA level ) using a sparse Graphical Gaussian Model ( GGM , [30] ) . This model is based on the assumption that , in each set of variables , the distribution of the variables , ( Xj ) j = 1 … p’ is Gaussian N ( 0 , ∑ ) and that the observations obtained for all individuals are independent and identically distributed . The method then unravels the conditional dependency structure of the variables , i . e . , defines a network whose edges correspond to positive or negative partial correlations P ( Xj , Xj’| ( Xk ) k≠j , j’ ) Using a maximum likelihood approach , the method performs an edge selection , simultaneously to the estimation of partial correlations . Unlike simple correlation , partial correlation is a mean to assess direct correlations between pairs of variables , independently of the other variables and is thus closer to a causality relation than simple correlation . The number of selected edges was chosen according to the description given in step 3 below . A second step consisted in inferring a network between each pairs of two different sets of variables among bio-clinical , FAs and mRNA level sets . To do so , we used the approach that was proven successful to infer a gene/phenotype network in [31]: regularization canonical correlation analysis ( CCA , [16 , 32] ) . The additional regularization constraint was used to deal with the large number of variables as compared to the number of observations . The number of selected edges was chosen according to the description given in step 3 below . A third step consisted in merging the 3 networks obtained in the first step with the 3 networks obtained in the second step . As the number of variables in the 3 datasets was very different ( from 15 bio-clinical variables up to 221 gene expressions ) , a naive strategy consisting in estimating the selected edges in each set of variables ( or in each pair of two sets ) in a same manner would have led to give too much importance on the largest set of variables , i . e . , to the gene expression dataset . The number of selected edges was thus adjusted to be equal to the number of nodes in each set ( or pair of sets ) of variables , leading to smaller densities for the largest networks . The first step of the analysis was performed using the R package glasso ( cran . r-project . org/web/packages/glasso ) and the second step using the R package mixOmics ( http://perso . math . univ-toulouse . fr/mixomics ) . Global network analysis . To stress out the macro-structure of the network , a spin-glass model and simulated annealing were used to maximize the modularity quality measure [33] and obtain a vertex clustering [34] for all 5 networks . The significance of the clustering was assessed using a permutation test as described in [35]: the clustering was declared significant if the obtained modularity was larger than the maximum modularity found over 100 random graphs with the same degree distribution than the graph under study . Random graphs with identical degree distributions were generated using a permutation of the edges as justified by [36] . Sub-network analysis . Significance of the betweenness within a cluster was assessed using a permutation test to check if the betweenness was significantly high regarding the node’s degree in its cluster . A significant result ( p<0 . 05 ) indicates a node more central than expected in the graph and a non-significant ( p≥0 . 05 ) result indicates a node which centrality is expected for the node’s degree . For nodes with a high degree ( so-called hubs ) , a non-significant result does not however indicate that the node is not important within its cluster: its importance is already acknowledged by its many connections with the other nodes . But , provided its degree , it is not particularly central . Significant betweenness was thus used as a measure of importance of the hubs in the networks ( even though hubs were systematically investigated , it provided an additional information on the node’s critical role ) . The permutation test was performed in a way similar to the modularity test: the highest betweenness over 100 random graphs with the same degree distribution was compared to all observed betweenness . The nodes with an observed betweenness in the top 5% were declared significant . The network analysis ( node clustering and betweenness calculation ) was performed with the R package igraph ( igraph . org; [37] ) . Finally , clusters with identical central nodes in two different networks were tested for the significance of the number of common nodes using a Fisher exact test with the set of all variables as reference: pairs of clusters with a p-value smaller than 5% in the Fisher exact test are those that have a larger number of common nodes than what was expected by random chance only . Sub-graphs ( clusters ) were laid out using force-based algorithms in Gephi 0 . 8 . 2 software ( gephi . org , [38 , 39] ) . Nodes’ sizes indicate degree , i . e . , the number of edges adjacent to the node . Nodes with the largest degrees , called hubs , were systematically extracted . Nodes’ colors and font size indicate betweenness centrality , a measure that counts how often a node appears on shortest paths between two other nodes in the network . Therefore , betweenness centrality indicates nodes that are the most likely to disconnect the network if removed . The variables are connected by an edge only if they have been selected by the sparse estimation . Edge thickness is proportional to the strength of the correlation ( CCA ) or of the partial correlation ( GGM ) but should only be compared for a given set of estimation ( i . e . , partial correlation strength between two pairs of genes can be compared but should not be compared to correlation between a gene and a FA or a bio-clinical parameter ) . The biological functions represented by mRNAs from each cluster were searched using Ingenuity Pathways Analysis ( IPA ) software version 7 . 5 ( Ingenuity Systems , Redwood City , CA ) . The significance of canonical pathways was tested using the Fisher Exact test with the set of 221 genes as reference . Data were controlled for multiple testing by using Benjamini-Hochberg P value correction .
Baseline anthropometric and clinical characteristics of the 135 women are displayed in Table 1 . After the end of LCD , individuals were a posteriori classified into 3 groups according to weight changes during WMD . To ensure that there was no striking between group difference at baseline and after LCD , bio-clinical variables , gene expression and FA profiles were also analyzed a posteriori according to weight control classification . At baseline , women from WL group had higher weight and BMI than those from WS groups ( Table 2 ) . Weight loss induced by LCD was similar in the 3 groups even though mean weight in WL group remained higher than in WS group after the LCD . Plasma adiponectin was higher at baseline in WL group compared to WR and WS groups . During LCD , there was no intergroup difference in bio-clinical changes . All parameters improved except plasma fructosamine and adiponectin . S3 Table displays the anthropometric and clinical characteristics at the end of the weight maintenance phase according to weight control group and by randomization arm . During WMD , women from WL group lost 7 . 0 ± 0 . 4 kg compared to the end of LCD and those from WR group regained 5 . 0 ± 0 . 4 kg . Adiponectin improved during WMD only in WL group . There was no difference regarding age , center ( data not shown ) or distribution of the 5 WMD dietary arms between groups ( S3 Table ) . There was no intergroup difference in changes in dietary intake along DI ( S1 Fig . ) . A bunch of 221 mRNA ( S2 Table ) selected from previous AT investigations using microarrays was quantitatively assessed using RT-qPCR . Among these genes , 155 genes were down-regulated during LCD . The most representative pattern was a down-regulation during LCD and up-regulation during WMD . The most regulated genes in the 3 groups , SCD and FASN , encoded enzymes for different steps of FA synthesis , stearoyl CoA desaturase and fatty acid synthase , respectively ( S2 Fig . ) . S3 Table displays the AT changes in FA composition . At baseline , in WL group , AT had higher percentages of polyunsaturated FAs ( PUFAs ) and lower saturated FAs ( SFAs ) and mono unsaturated FAs ( MUFAs ) compared with other groups . SFAs and MUFAs exhibited the most representative changing course during DI . During LCD , in WL group , 2 SFAs ( 12:0 and 14:0 ) and 2 MUFAs ( 14:1 ( cis-9 ) and 16:1 ( cis-9 ) ) AT content decreased . Three other MUFAs ( 18:1 ( cis-9 ) , 20:1 ( cis-11 ) , 16:1 ( cis-7 ) ) , and 4 PUFAs , including 20:4 ( cis-5 , 8 , 11 , 14 ) , increased . Altogether , after WMD , the AT from WL and WS groups had similar FA profile than after LCD . In the WR group , the FA content returned to baseline values . The greatest changes were an increase in 12:0 , and 14:1 ( cis-9 ) and a decrease of 18:1 ( cis-9 ) percentages . S3 Fig . displays SCD activities assessed using 14:1 ( cis-9 ) /14:0 , 16:1 ( cis-9 ) /16:0 and 18:1 ( cis-9 ) /18:0 ratios and showed no between group difference at baseline and after LCD . At the end of WMD , 14:1 ( cis-9 ) /14:0 and 16:1 ( cis-9 ) /16:0 , but not 18:1 ( cis-9 ) /18:0 , were higher in WR compared to WL group . Network inference was performed using the 3 step inference method ( see Materials and Methods ) at baseline , at the end of LCD , and in the 3 groups at the end of WMD , resulting in 5 global networks . Then , to stress out the macro structure of the network , a vertex clustering was performed . All 5 clustering performed on the 5 global networks were found to have a significantly high modularity , proving the relevance of the sub-graphs ( clusters ) . At baseline . Before LCD , among 14 clusters detected , 9 displayed more than 6 vertices . Insulin , waist circumference and 18:1 ( cis-9 ) were central nodes of 3 of the clusters containing at least 2 types or variables ( bio-clinical , FAs or mRNAs ) ( Fig . 3 ) . Insulin was the variable with most significant betweenness centrality ( p-value = 0 . 03 ) among the most central nodes of the 3 clusters . The insulin-centered cluster contained plasma glucose and mRNA encoding proteins involved in “Adhesion and Diapedesis” as major canonical pathway according to IPA analysis . This included various cytokines ( CCL2 , CCL18 ) and metalloproteases ( MMP9 , MMP19 ) with positive correlation to fasting insulin . Most of these genes were negatively linked to 18:0 and positively linked to 16:1 ( cis-9 ) . The module whose hubs were waist circumference ( degree: 27; p-value of waist circumference betweenness centrality = 0 . 39—not significant ) and HDL ( betweenness centrality p-value = 0 . 36—not significant ) showed respectively positive and negative correlations with genes involved in an “Immune Response” gene expression IPA signature ( CD163 , CCL3 , CCL19 , C1QC , C2 , IL10 and FCGBP ) . Adiponectin was negatively connected to part of these immune response genes and 18:1 ( cis-11 ) . Among genes negatively connected to waist circumference were AZGP1 and GPD1L , whose lower expression in AT from metabolic syndrome ( MetS ) individuals was previously described [24] . The most significant mRNA signature of the module organized around 18:1 ( cis-9 ) ( degree: 38; betweenness centrality p-value = 0 . 85—not significant ) was “Fatty Acid Biosynthesis” . Transcripts of this path included all desaturases ( SCD , FADS1 and FADS2 ) , ALDH6A1 and ACSL1 . Like AACS and LPIN1 , two other transcripts involved in lipid metabolism , all transcripts but ALDH6A1 were positively and negatively connected to 14:1 ( cis-9 ) and 18:1 ( cis-9 ) , respectively . Effect of an 8-week active weight loss . After LCD , vertex clustering detected 10 modules of which 7 had more than 6 nodes . Fig . 4 displays the 4 modules with at least 2 types of variables . Hubs were 14:0 ( degree: 49; betweenness centrality p-value < 0 . 01 ) , waist circumference ( degree: 38; betweenness centrality p-value: 0 . 98—not significant ) , 14:1 ( cis-9 ) ) ( degree: 10; betweenness centrality p-value < 0 . 01 ) , and 18:2 ( cis-9 , 12 ) ( degree: 34; betweenness centrality p-value: 0 . 38—not significant ) . The 14:0 centered module also contained adiponectin as central node . The most significant mRNA signature was “Growth Hormone Signaling” . The transcripts from this signature ( GHR , IGF1 , IRS1 , and MAPK3 ) were all positively connected to 3 saturated FAs , i . e . 12:0 , 14:0 or 18:0 as well as to adiponectin . The module with waist circumference as hub also included BMI as high degree ( 35 ) and high centrality node . The most significant mRNA signature was “Adhesion and Diapedesis” . Transcripts from this signature ( CCL2 , CCL3 , CCL18 , CCL19 , FN1 and MMP19 ) were all positively connected to waist circumference , except MMP19 . CCL3 was positively connected to waist circumference whereas GPD1L and AZGP1 were negatively connected to this abdominal adiposity marker . In this module , 16:1 ( cis-9 ) was negatively connected to GPD1L and positively to anthropometric parameters , plasma triglycerides and insulin . The module with highest degree node 14:1 ( cis-9 ) encompassed genes involved in “Fatty Acid Biosynthesis” ( SCD , FADS1 and FADS2 ) as well as SLC2A4 , FASN , SREBP1 , PNPLA2 and PNPLA3 in a positive manner . Of note , all of these genes were significantly down-regulated during LCD . The 18:2 ( cis-9 , 12 ) with highest degree node mostly contained transcripts with negative relationship to this FA . These transcripts included those encoding proteins involved in triglyceride metabolism ( LIPE , DGAT1 , DGAT2 and AGPAT1 ) . After 6 months weight maintenance diet . Vertexes classification was performed and the most important heterogeneous clusters with more than 6 nodes are presented in Figs . 5 and 6 . Since WS group showed intermediary phenotype , we focused on WR and WL groups . Individuals regaining weight . Of 12 modules , classification detected 5 heterogeneous clusters of interest . As displayed in Fig . 4 , the systolic blood pressure ( degree: 34; betweenness centrality p-value = 0 . 05 ) and waist circumference ( degree: 32; betweenness centrality p-value = 0 . 33—not significant ) hubs showed negative relationship of these nodes with a “Sucrose , Serotonin and Adrenalin Degradation” IPA signature made of ADHFE1 , ALDOB , ALDOC , C2 and MAOA . These central nodes were also negatively connected to AZGP1 and GPD1L and positively to IL10 . The module converging on fructosamine ( degree: 10; betweenness centrality p-value = 0 . 12—not significant ) showed no FA but a “Growth Hormone Signaling” mRNA signature that included IRS1 , FGF2 , IGF1 and GHR , the 2 former transcripts being significantly up-regulated during WMD in the WR group and the latter positively connected to fructosamine via FGF2 . In the module organized around 14:1 ( cis-9 ) ( degree: 16; betweenness centrality p-value = 0 . 62—not significant ) the most significant mRNA signature was “Cancer Signal” mainly represented by transcripts up-regulated during WMD , i . e . CCND1 , CYCS , E2F4 , ITGB2 , and MAPK3 . 16:1 ( cis-9 ) was another hub ( degree: 12 ) positively connected to 14:1 ( cis-9 ) . Two modules were with saturated FAs as hubs . The first one focused on 18:0 ( degree: 20; betweenness centrality p-value = 0 . 09—not significant ) and contained a large array of poly-unsaturated FAs plus 18:1 ( cis-9 ) which amount significantly decreased during WMD , exclusively in WR group . The most significant mRNA signature was “Adhesion and Diapedesis” ( CCL18 , CCL19 , CCL3 and IL1RN ) . The second cluster was organized around 14:0 ( degree: 42; betweenness centrality p-value = 0 . 66—not significant ) which was positively connected to 16:0 and 12:0 . The most significant mRNA signature was “Angiogenesis Inhibition by TSP1” , especially VEGFA , an mRNA up-regulated during WMD and positively connected to 14:0 , and MMP9 with negative relationship to 14:0 . The SCD , FADS2 , ELOVL5 and SREBP1 transcripts involved in DNL were positively connected to 12:0 , 14:0 or 16:0 . Individuals with continued weight loss . Of 11 modules , the 3 heterogeneous clusters are presented in Fig . 5 . The 14:1 ( cis-9 ) centered module ( degree: 21; betweenness centrality p-value = 0 . 01 ) encompassed genes involved in DNL , i . e . AACS , FASN , SCD , FADS1 , FADS2 and ELOVL5 . All were positively correlated to 14:1 ( cis-9 ) . The AACS , SCD , FADS1 and ELOVL5 mRNA levels increased during WMD . The most complex path was based on waist circumference ( degree: 38; betweenness centrality p-value = 0 . 23—not significant ) and incorporates BMI , weight , C reactive protein ( CRP ) and 20:4 ( cis-5 , 8 , 11 , 14 ) as nodes with high centrality . AZGP1 and GPD1L were negatively connected to waist circumference . The most significant mRNA signature was “Complement Adhesion and Diapedesis” . This included CCL3 , CCL18 and CCL19 , C1QA , C1QB and C1QC that displayed significantly decreased mRNA levels during WMD and positive correlation with waist circumference . All FAs were n-6 with positive relationship to waist circumference , weight and BMI . Especially , the 20:4 ( cis-5 , 8 , 11 , 14 ) had positive correlation to CRP . The module organized around 18:1 ( cis-11 ) ( degree: 17; betweenness centrality p-value = 0 . 11—not significant ) contained low density lipoproteins , cholesterol and adiponectin but showed no enriched mRNA signature .
Both lipids and transcripts ( as frames for protein synthesis ) are important components of AT biology . To identify interactions between these molecular species we investigated the networks of AT esterified FAs and mRNAs together with bio-clinical data in obese women according to weight changes along a longitudinal DI . The present study is the first to jointly investigate gene expression and lipidome from the same biopsy of AT in such a large number of obese individuals . Networks are useful models to investigate a set of relations between variables . In particular , network clusters in gene networks are more robust , i . e . , less influenced by measurement noise , than each individual relation [15 , 40] . In the present case , the strength of the relations between the different sets of variables ( e . g . , the strength of the relation between two transcripts or the strength of the relation between a transcript and a FA level ) have very different scales . This caveat is controlled using a non-global inference approach , in order to have a global model of the interactions between all sets of variables . The regularized CCA has previously been used in combination with sparse partial least squares regression to investigate AT transcriptionally coordinated paths correlated with PUFA intake during the LIPGENE study [17] . Here , we used a 3-step inference method to infer a global model using 3 datasets: first , inferring a network in each dataset using a sparse GGM; second , inferring a network between each pairs of two different sets of variables using regularized CCA; third , merging the 3 networks obtained during the first step with the 3 networks resulting from the second step . As the numbers of variables in the 3 datasets were very different ( from 15 bio-clinical variables up to 221 mRNA levels ) , a simple strategy consisting in estimating the selected edges in each set of variables ( or in each pair of two sets ) in a same manner would have led to give too much importance on the largest set of variables , i . e . , to the gene expression dataset . The number of selected edges was thus adjusted to be equal to the number of nodes in each set ( or pair of sets ) of variables , leading to smaller densities for the largest networks . To improve the significance of our findings , systematic statistical tests were performed to test the significance of the betweenness centrality of the nodes compared to their degrees . Significance of nodes indicates that , given their degrees , they have a betweenness larger than expected and are thus significantly central in their clusters . Our study showed both constant and specific biological signatures in response to different weight control phases relevant to distinct metabolic features . We focused on body weight changes and especially according to weight control 6 months after calorie restriction . The present combination of network inference and node clustering enabled to draw a picture of transcript-FA-bioclinical variables interactions at each step of the longitudinal dietary intervention , leading to highlight the unexpected pivotal position of myristoleic acid ( 14:1 ( cis-9 ) ) . This FA was linked to DNL transcripts during active and continued weight loss . It is to be noted that , after WMD , the WR group merely displayed specific AT signatures never found at baseline or during weight loss . The most striking invariable feature was the presence of waist circumference as central node along all steps of the DI . To check similarity between all clusters with waist circumference as hub , paired comparison of the number of common nodes between clusters was performed between baseline cluster and either LCD , or WL group , or WR group cluster . The p-values of these tests were all < 0 . 001 , indicating a high similarity between nodes’ composition of the clusters having waist circumference for hub . Waist circumference is the most prominent clinical risk factor involved in MetS [41] . A persistent positive link with the macrophage inflammatory protein 1α ( CCL3 ) and negative with the adipokine α2-glycoprotein 1 ( AZGP1 ) and the enzyme glycerol-3-phosphate dehydrogenase 1-like ( GPD1L ) mRNA levels was found at baseline , after active weight loss and at 6 months of the weight control follow-up in WL and WR groups . Variants in GPD1L are associated with risk of sudden death in patients with coronary artery disease [42] . AZGP1 is a lipid mobilizing factor with putative role in insulin resistance as mRNA and protein were low in AT of type 2 diabetes patients and circulating AZGP1 protein inversely correlated with BMI and waist-to-hip ratio [43] . The chemokine CCL3 is up-regulated with insulin resistance in AT [44] . These genes are at top rank of the MetS signature described in AT from obese individuals [24] . The relationship between these transcripts and the major component of MetS suggests that they could be used as biomarkers for risk stratification of type 2 diabetes or cardiovascular disease in obese individuals , alone or combined to bio-clinical related factors . At baseline , fasting plasma insulin was the most significant central vertex among all modules . This cluster exhibited an immune signature , all transcripts of the Adhesion and Diapedesis pathway being positively connected to insulin . On the other hand , insulin was negatively connected to stearic acid and transcripts encoding factors involved in lipid metabolism ( CIDEA ) [45] , especially lipolysis ( GPR109A and ABDH5 ) [46] , and SIRT1 . The SIRT1 gene encodes a histone deacetylase that regulates various metabolic pathways and regulate lipids and glucose metabolism [47] . Besides the positive relationship between immune cells content in AT in the etiology of insulin resistance [48] , this cluster indicates that , in obese women , the higher is the insulin level at fasting , the lower is the lipid metabolism signaling in AT . After LCD induced weight loss , 3 modules focused on FAs . One was organized around linoleic acid , an essential FA that is highly represented ( >30% ) in the commercial hypocaloric meals provided during LCD ( data not shown ) . However , linoleic acid ( 18:2 ( cis-9 , 12 ) ) content of fat pads was unchanged compared to baseline . Indeed , there is minimal deposition of dietary fat into AT during periods of negative energy balance [9] . Myristic acid ( 14:0 ) was the most central vertex of a module along with lauric ( 12:0 ) and stearic acids ( 18:0 ) . Adiponectin , which is an adipocytokine with anti-inflammatory and insulin sensitive properties [49] was another central vertex . Myristic acid and adiponectin were both positively connected between each other and to insulin signaling or insulin-like transcripts ( IRS1 and IGF1 ) . The biological role of myristic acid remains poorly explored . Fatty acylation of signaling proteins play key roles in regulating cellular structure and function . Among the various myristoylated proteins are numerous signal transducing proteins [50] . In the present study , there was a statistically significant decrease in myristic acid triglycerides AT content during LCD , indicating a mobilization from lipid droplet that might provide non esterified myristic acid within the adipose cell . Whether such available myristic acid indeed does acylate signal transduction proteins is a question of particular interest . Six months after the end of LCD , the AT from women that continued to lose weight ( WL group ) also displayed two modules organized around FAs , myristoleic acid and vaccenic acid . Vaccenic acid amount is low in AT ( <2% ) . It comes from palmitoleic acid elongation . There was no significant change in AT vaccenic acid content during the dietary intervention . Its steadiness in AT from individuals continuing to lose weight indicates that this FA was poorly mobilized during weight loss . A positive correlation between vaccenic plasma TG content and insulin resistance has been shown in men [51] . Whether there is a similar link with AT triglycerides deserves attention even though no direct relationship with glucose homeostasis parameters appears in the present module . When considering active weight loss and continued weight loss after calorie restriction , a remarkable feature was the presence of myristoleic acid connected to an array of genes involved in FA synthesis , especially DNL enzymes and desaturases ( FASN , SCD , FADS1 and FADS2 ) . Like palmitoleic ( 16:1 ( cis-9 ) ) and oleic acids ( 18:1 ( cis-9 ) ) , myristoleic acid is a product of desaturation by SCD ( from myristic acid ) . It is a minor AT FA ( <0 . 5% of total FA content ) that is not provided by food . Surprisingly , in the present study it is an important focal node , which AT content decreased during LCD and remained stable at the end of WMD , except in WR group . Moreover , at the end of WMD , in WR group and in relation to SCD gene expression in AT , an increased SCD activity ( assessed by 14:1 ( cis-9 ) /14:0 ratio ) was observed that could be due to a positive regulation of SCD transcription by saturated FAs [52] . In this group , 14:0 and 16:0 were focal nodes and positively connected to SCD . The SCD activity is known to be associated with triglyceride accumulation [53] but its beneficial effect on insulin sensitivity remains controversial [52] . Control of SCD expression and DNL are coordinated . SCD is tightly regulated by saturated FAs and poly-unsaturated FAs as linoleic acid , SREBP-1c and carbohydrate response element binding protein ( ChREBP ) α and β [52] . ChREBP isoforms were not included in the series of mRNA quantified here but SREBP1 was positively connected to myristoleic acid after LCD . This is in agreement with the transcriptional activation of SCD by SREBP1c [52] . In contrast to liver where DNL is considered deleterious , DNL occurring in fat depots may provide beneficial health effects since it produces lipid species with bioactivities distinct from those of lipids predominantly derived from diet [2 , 54] . Strategies to enhance DNL specifically in AT may provide new therapies for metabolic and cardiovascular diseases [55–57] . The presence of a DNL signature with acute ( LCD ) and continuing ( WMD ) weight loss is in line with the enhanced differentiation potential of preadipocytes observed after calorie restriction [58] . In the present study , myristoleic acid might be an interesting marker of DNL and SCD activity in AT . Its persistence in AT triglycerides despite fat mass loss may constitute a hallmark of beneficial adipogenesis after weight loss . Last , AT from WR group showed a salient hyperplastic attribute with 3 modules encompassing genes involved in cell proliferation , angiogenesis , or growth factor signal transduction . Of note , the former cluster exhibited two mono-unsaturated FAs as central nodes with no link to genes involved in FA metabolism . The angiogenic signature was mainly due to VEGFA ( mean fold change during WMD = 1 . 9±0 . 4 ) that encodes an essential proangiogenic factor in AT [59] . The latter was organized around fructosamine , which is a serum marker of poor long-term glycemic control , as a hallmark of the deleterious effect of energy store repletion . The positive link of fructosamine to a series of transcripts- TWIST1 that encodes a transcription factor abundantly expressed in adipocytes [60] , which is positively correlated to insulin sensitivity [61] and SPTAN1 , a transcript encoding an insulin responsive α-fodrin involved in the glucose transporter GLUT4 translocation in adipocytes [62]-related to glucose homeostasis and beyond insulin signaling ( IRS1 , IGF1 , FGF2 and GHR ) may seem counterintuitive . Growth hormone shares protein anabolic properties with insulin . On the other hand , fasting insulin and glucose are part of another module which displays an immune signature ( Adhesion and Diapedesis ) , emphasizing the link between adipose tissue inflammatory status and insulin resistance [48] . The link between weight regain and proliferative patterns was previously shown using transcriptomic in a small subset of individuals from the same trial [28] . The present study indicates AT hyperplasia in individuals failing weight maintenance despite continued energy restriction . Altogether , no cluster showed a lipid metabolism signature in this group . Stearic acid was the hub of a module with the immune response signature . This FA was negatively connected to unfavorable bio-clinical parameters ( body fat mass , fasting plasma insulin , triglycerides and CRP ) and positively to beneficial ones ( adiponectin and HDL ) . This suggests that the highest is lipid droplet stearic acid content , the better is metabolic status . The cluster with systolic blood pressure and waist circumference as hubs displayed an amine degradation signature . Levels of noradrenaline associate with obesity and cardiovascular risk [63] . Systolic blood pressure was negatively correlated to most variables , including AZGP1 and GPD1L described above except diastolic blood pressure . This parameter was negatively correlated to waist circumference as well as BMI . This feature was different from the one observed in the weight loss group where waist circumference was positively correlated to weight and BMI . This emphasizes the predominant role of waist circumference , compared to blood pressure , in metabolic syndrome compared to blood pressure . The present investigation shows several limitations . Only women were investigated; as a preeminent effect of sex on AT gene expression was previously shown [24 , 64] . Also , we studied fat from the subcutaneous abdominal region and we cannot extrapolate our findings to other subcutaneous , gluteo-femoral or visceral fat depots . Last , we performed unsupervised learning using GGM . This approach uses partial correlations and differs from relevance networks that use direct correlations and thus provide a strong but sometimes biased measure of the dependence between variables . Bayesian networks that lead to directed acyclic graphs ( DAG ) could provide a clue on causal relationships but some knowledge information has to be provided a priori . In the present networks , edges do not represent simple correlations but between variables dependencies . Using GGM , interpretation is not causality but only a matter of strong and direct statistical association . Nodes with highest betweenness centrality represent variables whose fine tuning might greatly impact the level of the other connected variables . To conclude , this approach has linked a characteristic structure of AT network to a slimmed phenotype thereby suggesting myristoleic acid as main lipidic biomarker for DNL and SCD activity . The anabolic signature unique to individuals with unsuccessful weight control suggests detrimental tissue hyperplasia . This initial analysis provides a valuable starting point for more in-depth investigation of the implication of myristoleic acid in weight loss .
|
Obesity is an excess fat mass leading to metabolic diseases . Dietary management is a conventional strategy to promote weight loss . As energy buffering , in the form of esterified fatty acids , and secretory organ , the adipose tissue has a pivotal role in obesity and its related complications . A comprehensive insight of adipose tissue response during and after calorie restriction might improve obesity management . Modern nutrition research study the impact of diet on health by combining multiple datasets to provide an holistic view of tissue physiopathology . To identify significant clusters of fatty acids , transcripts or bio-clinical parameters related to weight change along calorie restriction and subsequent weight follow-up in obese individuals , the issue of different datasets integration must be resolved . Here , we implemented an innovative multistep approach to infer multi-data networks and compare clusters of network components . This original strategy highlighted an unexpected central role of a minor adipose tissue fatty acid , myristoleic acid , which is not provided by food . Its link to transcripts encoding enzymes from a pathway converting glucose into fat that mediates favorable metabolic effects makes myristoleic acid a key factor of the positive impact of fat mass reduction .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
System Model Network for Adipose Tissue Signatures Related to Weight Changes in Response to Calorie Restriction and Subsequent Weight Maintenance
|
Cystic echinococcosis ( CE ) is a globally occurring zoonosis , whereas alveolar echinococcosis ( AE ) is endemic only in certain parts of the Northern Hemisphere . The socioeconomic impact of human echinococcosis has been shown to be considerable in highly endemic regions . However , detailed data on direct healthcare-related costs associated with CE and AE are scarce for high income countries . The aim of this study was to evaluate direct costs of human disease caused by CE and AE in Austria . Clinical data from a registry maintained at a national reference center for echinococcosis at the Medical University of Vienna were obtained for the years 2012–2014 . These data were used in conjunction with epidemiological data from Austria’s national disease reporting system and diagnostic reference laboratory for echinococcosis to assess nationwide costs attributable to CE and AE . In Austria , total modelled direct costs were 486 , 598€ ( 95%CI 341 , 825€ – 631 , 372€ ) per year for CE , and 683 , 824€ ( 95%CI 469 , 161€ - 898 , 486€ ) for AE . Median costs per patient with AE from diagnosis until the end of a 10-year follow-up period were 30 , 832€ ( 25th– 75th percentile: 23 , 197€ - 31 , 220€ ) and 62 , 777€ ( 25th– 75th percentile: 60 , 806€ - 67 , 867€ ) for inoperable and operable patients , respectively . Median costs per patients with CE from diagnosis until end of follow-up after 10 years were 16 , 253€ ( 25th– 75th percentile: 8 , 555€ - 24 , 832€ ) and 1 , 786€ ( 25th– 75th percentile: 736€ - 2 , 146€ ) for patients with active and inactive cyst stages , respectively . The first year after inclusion was the most cost-intense year in the observed period , with hospitalizations and albendazole therapy the main contributors to direct costs . This study provides detailed information on direct healthcare-related costs associated with CE and AE in Austria , which may reflect trends for other high-income countries . Surgery and albendazole therapy , due to surprisingly high drug prices , were identified as important cost-drivers . These data will be important for cost-effectiveness analyses of possible prevention programs .
Cystic echinococcosis ( CE ) is a zoonosis caused by Echinococcous granulosus and is endemic on all continents . In contrast , Echinocococcus multilocularis , the pathogen responsible for alveolar echincoccosis ( AE ) , occurs only in certain areas of the Northern Hemisphere [1] . Both infections occur in humans in Austria . The western provinces of Austria are traditional hotspots of E . multilocularis transmission , with an increase in cases seen recently [2] . The geographic distribution of AE has expanded over the past several decades and now includes the entire country . Autochthonous E . granulosus transmission is rare in Austria , with only occasional locally-acquired CE cases reported in the past 20 years [3] . The majority of CE cases treated in Austrian hospitals are migrants from South-Eastern Europe and the Middle East , residing foremost in Austria’s Eastern provinces . AE is associated with significant morbidity and mortality if left untreated . Treatment varies with disease stage , but therapeutic options are typically limited to hepatic surgery and albendazole therapy . If surgery is not feasible , palliative management with long-term albendazole treatment is indicated . Ideally , AE cases should receive long-term follow-up with advanced radiological imaging modalities , including positron-emission-tomography/computed tomography ( PET/CT ) [4] . CE is treated using a cyst stage-specific scheme consisting of surgery , percutaneous interventions , pharmacological treatment , or a watch-and-wait approach [5][6] . Although the socioeconomic impact of echinococcosis has previously been assessed for endemic countries [7] , detailed studies on direct costs linked to echinococcosis , and in particular AE , are scarce and even more so for high-income regions . Consequently , there is a dearth of information about the economic impact of AE and CE in high income regions like the Central European country of Austria , which may guide decision makers about the cost-effectiveness of control measures and of clinical management . The aim of this study was , therefore , to quantify costs associated with the treatment of echinococcosis in Austria from a societal perspective .
Clinical and epidemiological data were collected from a clinical registry of CE and AE patients managed at a reference center for the clinical management of echinococcosis at the General Hospital ( AKH ) of the Medical University of Vienna in Austria from 2012 to 2014 . Patients seeking care for the first time in the given time period and subsequently managed at the reference center were eligible to be included . However , not all echinococcosis patients are registered , referred or managed at this center with other large centers treating patients on an individual basis . Thus , additional epidemiological data on the incidence of echinococcosis in Austria were collected from the Austrian Ministry of Health’s report on zoonoses [8] and the national reference laboratory for the diagnosis of echinococcosis , which is part of the Department of Parasitology at the Medical University of Vienna , and the only reference laboratory for echinococcosis in Austria . This laboratory processes samples from all suspected cases of echinococcosis in Austria . Ethics approval was obtained from the ethics committee of the Medical University of Vienna ( #2031/2012 ) . Frequency data on hospitalizations and interventions were extracted from the clinical registry and patient records . Costs associated with these hospitalizations were obtained from the hospital’s Office for Medical Economics [5] . Data were also collected from patient records on the duration of albendazole drug therapy , including deviations from standard dosing . When complete dosing information was not available , these data were imputed based on published guidelines [5][6] . For outpatient routine laboratory testing ( complete blood count and standard chemical parameters ) , specific diagnostic tests for echinococcosis ( ELISAs , Western Blots , PCRs and histological examinations ) , clinical follow-up visits , diagnostic imaging ( CT-scans , magnetic resonance imaging ( MRI ) scans , PET/CT and PET/MRI ) , and albendazole treatment , cost estimates were obtained from the Department of Parasitology , the hospital’s outpatient self-payer guide ( not freely available ) , and the Austrian Reimbursement Code ( “Erstattungskodex” ) for registered drugs [9] . Overall direct costs for incident cases presenting to the AKH in the years 2012 , 2013 , and 2014 were summed and the proportional contribution of respective items ( hospitalizations , imaging , etc . ) analyzed per year of follow-up . Patients were classified as suffering from AE or CE , and according to CE-specific disease stage at inclusion into the study . Cost data were assessed for normality and the Wilcoxon matched-pairs signed-ranks test used to compare costs for the year of diagnosis and subsequent two years of follow-up . In order to model nationwide costs , estimates of incidence were based on data from Austria’s diagnostic reference laboratory for echinococcosis in addition to national data provided by the Ministry of Health . Data from these institutions represent the best available estimates of AE and CE incidence in Austria . A spreadsheet model was developed in Microsoft Excel 2010 for Windows to evaluate national direct costs associated with AE and CE . Parameters were sampled across their distributions using a Monte Carlo approach with 1 , 000 bootstrap replicates . Annual incidence was modelled using a Poisson distribution with λ = 12 for AE and λ = 40 for CE [2] . Mean patient age was estimated at 60 years for AE and 44 years for CE based on clinical registry data . A year 2013 Austrian life table was used to model yearly survival probability for patients aged 60 and 44 years , respectively [10] . The proportion of patients receiving an intervention ( surgery or interventional radiology ) was modeled using a binomial distribution based on clinical registry data , with probability = 0 . 65 for AE and probability = 0 . 66 for CE . The seemingly comparable proportion of patients requiring an intervention for both AE and CE is explained by the fact that minimally invasive procedures such as PAIR ( puncture , aspiration , injection , reaspiration ) are part of the estimate for CE . A follow-up time of 10 years was assumed according to the local standard operating procedure . Direct costs for the first 3 years of treatment were obtained based on registry data . For year 4 through 10 , yearly costs were estimated to be 90% of the previous year until the end of follow-up , for both AE and CE . In a previous Swiss study on AE [11] , a discount factor of 3% was applied . However , this factor is believed to overestimate costs for the population under care in Austria . Therefore , in order to account for monitoring and the long-term use of albendazole , the current study includes a slightly steeper decrease in costs over time . In order to evaluate the impact of this discounting factor , a sensitivity analysis was performed by comparing the results to a model without discounting ( i . e . , assuming year 3 costs would be applicable for years 4–10 ) and to a model with 3% discounting per year .
Based on time of follow-up , overall direct , healthcare-related cost associated with CE for the first year after diagnosis at our center was 336 , 419€ ( n = 38 patients ) , 70 , 619€ for the second year ( n = 25 ) and 5 , 335€ ( n = 8 ) for the third year ( see Table 2 ) . The most cost-intensive components in the first year of treatment were hospitalizations ( 66% ) , followed by albendazole therapy ( 23% ) , whereas laboratory procedures ( 7% ) , diagnostic imaging ( 4% ) , and outpatient visits ( 1% ) only contributed to a small proportion of direct healthcare-related costs . Proportional costs per year of follow-up are depicted in Fig 1 . Per patient costs were highest in the year of diagnosis ( 8 , 853€ ) and significantly decreased in the second year of treatment ( 2 , 824€ ) ( p = <0 . 001; n = 25 ) . Cost per patient continued to decline into the third year of treatment ( 667€ ) . However , there was not a statistically significant difference between year 2 and year 3 ( p = 0 . 161; n = 8 ) . For year 1 , a stage-based analysis showed that per-patient costs were higher for active cysts ( CE1 to CE3b and extrahepatic active cysts combined ) compared to inactive cysts ( CE4 and CE5 combined ) ( see Table 3 ) ( p = 0 . 001 ) . Assuming 10% discounting in years 4–10 , modelled median costs per patients with CE from diagnosis until end of follow-up after 10 years were 16 , 253€ ( 25th– 75th percentile: 8 , 555€ - 24 , 832€ ) and 1 , 786€ ( 25th– 75th percentile: 736€ - 2 , 146€ ) for patients with active and inactive cyst stages , respectively . Based on an estimated incidence of 40 cases per year , a mean age of 44 years , and a 66% probability of requiring an invasive intervention ( surgery or interventional radiology ) , the modelled yearly direct costs for CE in Austria were 486 , 598€ ( 95%CI 341 , 825€ – 631 , 372€ ) . For the results of the sensitivity analysis with ( a ) no discounting factor for years 4–10 and ( b ) a discounting factor of 3% per year , see Table 4 . Overall direct costs for the 7 AE patients included in this cohort amounted to 132 , 739€ . The largest contributors to first-year costs were hospitalizations , including surgical interventions ( 79% ) , and drug therapy with albendazole ( 12% ) . Diagnostic imaging ( 4% ) , laboratory procedures ( 4% ) , and outpatient visits ( <1% ) contributed only marginally to the direct costs of AE in year one ( see Table 5 ) . Most costs occurred in the year of diagnosis and decreased in subsequent years ( see Table 6 ) . However , no formal statistical testing on cost differences between years was performed due to the low number of patients in follow-up in years two and three . Assuming 10% discounting in years 4–10 , median modelled direct costs per patients with AE from diagnosis until end of follow-up after 10 years were 30 , 832€ ( 25th– 75th percentile: 23 , 197€ - 31 , 220€ ) and 62 , 777€ ( 25th– 75th percentile: 60 , 806€ - 67 , 867€ ) for inoperable and operable patients , respectively . Based on an estimated incidence of 12 cases per year , a mean age of 60 years at diagnosis , and a 65% probability of receiving a surgical intervention , the modelled yearly direct costs for AE in Austria were 683 , 824€ ( 95%CI 469 , 161€ - 898 , 486€ ) . The results of the sensitivity analysis are presented in Table 4 . In a sensitivity analysis with ( a ) no discounting factor for years 4–10 and ( b ) a discounting factor of 3% per year , the modelled overall costs per year were 838 , 465€ ( 95% CI 597 , 826€ – 1 , 079 , 106€ ) and 781 , 077€ ( 95% CI 548 , 099€ – 1 , 014 , 055€ ) , respectively .
This study presents detailed direct costs associated with the diagnosis and treatment of echinococcosis in Austria , which is considered a country with very high human development by the Human Development Index . In contrast to most other economic analyses of echinococcosis , this study was based on a real cohort of patients . Nationwide costs for CE were estimated to cumulate to 486 , 598€ ( 95%CI 341 , 825€ – 631 , 372€ ) per year . Surgical interventions with accompanying hospitalizations and therapy with albendazole accounted for the vast majority of costs . Similar studies from high income countries are scarce . Differences in healthcare systems and billing systems , distinct methods and parameters used for the cost models of each publication , economic disparities , inflation and diverging price-levels of each country make direct comparisons almost impossible . However , the high costs of surgical interventions were also noted in a previous study from Italy [12] . Based on an Austrian population of 8 . 7 million inhabitants , and estimated incidence of 0 . 46 cases /100 , 000 persons / year , costs for CE accumulated to an estimated 5 , 660€ per 100 , 000 inhabitants per year ( 95%CI 4 , 006€ - 7 , 315€ ) . A study from Italy estimated direct CE-associated costs of 6 , 398€ per 100 , 000 inhabitants per year with an estimated incidence between 1 . 06 and 2 . 78 cases per 100 , 000 inhabitants per year [13] . In one study from Spain published in 2005 , the overall direct cost of CE in humans was estimated at 603 , 671€ ( 95% CI 499 , 200€ – 662 , 638€ ) per year [14] . Assuming a population of 43 million inhabitants , as stated in the paper , this corresponds to 1 , 404€ per 100 , 000 inhabitants per year , based on 159 diagnosed cases in 2005 ( i . e . 0 . 36 cases per 100 , 000 inhabitants ) . CE is a rare condition in Austria , with few endemic cases . Disability Adjusted Life Years ( DALYS ) lost due to CE accounted for only 0 . 0078% of DALYs lost to infectious and non-infection conditions in Austria as estimated by the Global Burden of Disease Study 2016 data [15] . The costs attributed to CE appear negligible when compared to major drivers of healthcare costs . For example , it was estimated that approximately 1 . 2 billion € are spent on healthcare costs related to cancer per year in Austria [16] . Nevertheless , morbidity and direct costs per case may be substantial , especially when considering that CE is largely preventable by appropriate livestock management , deworming of dogs , not feeding raw offal to dogs , vaccination of sheep , and good food hygiene . As expected , costs associated with CE were significantly higher for active stages of the disease compared to inactive ones , and costs were highest in the year of diagnosis . At the center , for evaluation of interventions , advanced and relatively expensive imaging modalities , including MRI and CT scans , were performed in the management of CE to assess potential surgical operability or feasibility of percutaneous interventions [5] . Ultrasound examinations were performed , usually during an outpatient visit and were not billed separately . However , from a perspective of cost-effectiveness , replacing CT- or MRI-scans by ultrasound would only marginally reduce the direct costs of echinococcosis due to the low proportion of costs contributed by imaging in an affluent healthcare system . On the other hand , in low to middle income countries , management of CE is most probably overwhelmingly based on ultrasound , and may constitute a relevant cost-factor . Interestingly , albendazole consumption was high for both AE and CE in the first year of follow-up when peri-interventional albendazole-therapy or first line pharmacologic therapy ( depending on the cyst stage ) occur . In the second and third year , however , albendazole consumption was very low in CE patients , but still high in AE patients , some of which need long term pharmacologic therapy , which is unusual in CE . Thus , overall we think that our data on albendazole consumption seem plausible and reflect treatment recommendations . Interestingly , albendazole , a drug listed by the World Health Organization as an “essential medicine”[17] , of which millions of doses are used at very low cost or through donation programs each year in developing countries , was a major driver of healthcare costs in the management of both AE and CE . Costs for albendazole are prohibitively high in some countries of Western Europe and other developed regions [18][19] , and could be a primary target to decrease costs associated with the management of echinococcosis . Costs for albendazole are volatile and differ massively even between neighboring countries . In a recent study from Italy , the problem of albendazole drug shortages was also highlighted [20] . This has , to our knowledge , not yet occurred in Austria , but may potentially endanger treatment success . Multiplication of costs for old , unpatented drugs such as albendazole or daraprim following the acquisition of the main supplier by another company has possibly contributed to the high costs [21][22] . However , this is probably a problem requiring a political solution and not a medical or scientific one . Consequently , the contributors of direct costs associated with CE may be different in resource-limited regions of the world , where drug costs are lower and resources are often not available for surgical interventions . AE is endemic in several medium to high-income countries in the Northern Hemisphere , including Austria . Median costs per patients with AE from diagnosis until the end of a 10-year follow-up period were 30 , 832€ ( 25th– 75th percentile: 23 , 197€ - 31 , 220€ ) and 62 , 777€ ( 25th– 75th percentile: 60 , 806€ - 67 , 867€ ) for inoperable and operable patients , respectively . This is lower than direct costs of 108 , 762€ per patient estimated in a Swiss study [11] . One reason for this observation is the different discount factor used ( 10% in our model vs . 3% in the Swiss study ) , which partially explains the difference . Secondly , according to the Organization for Economic Co-operation and Development ( OECD ) , the price level index in Switzerland is 1 . 46 times that of Austria [23] , which may account for another part of the observed difference in direct costs between these two neighboring countries . As the treatment of AE consists of major surgery or long-term albendazole therapy , with no percutaneous interventions available , cost per case was higher than for CE . This is highly plausible as CE usually does not require major surgery or long-term suppressive therapy with albendazole , which were found to be the two main drivers of costs . Although cost per case will differ between countries , the main cost drivers for Western and Central European counties will most likely be similar , with disparities mostly attributable to difference in albendazole drug prices . This cost estimate can also be used to determine cost-effectiveness of control programs , which may include use of praziquantel-baits for E . multilocularis in foxes or systematic screening of at-risk populations [24] . Bait-based programs often involve the distribution of baits via air , and are thus expensive . Although cost estimations for such programs are not available for Austria , it seems unlikely that such interventions would be cost-effective with respect to the results of this study . Cost-effectiveness , as modelled by Hegglin et al . , could only be achieved in an area with high population density after decades of campaigning [25] . There are several limitations of the current study . First , the sample size was small particularly for differentiating CE disease-stage specific costs per year in the third year of follow-up , and for AE in the second and third year of follow-up . Thus , modelled costs may be affected by individual outliers . Secondly , patient data were abstracted for a maximum of three years . Therefore , healthcare-related costs linked to echinococcosis occurring later than 3 years after diagnosis were not recorded and had to be modelled . We chose to introduce a discounting factor ( 10% per year ) to approach this problem . This may still be relatively conservative , but is substantially higher than the previously used 3% [11] . In fact , the first year after diagnosis is the main driver of costs with later years of follow-up only contributing a relatively small proportion of costs . No deaths were observed in the study population , but excessive deaths due to echinococcosis that occurred beyond the time period based on real data ( i . e . in the modelled period ) were not included in the model . Excessive or earlier deaths would result in lower overall costs than modelled . Likewise , costs from patients that needed more than one intervention were included if these occurred in 2012–2014 . However , relapses that may occur during the modelled period were not accounted for leading to a possible underestimation of overall costs . Measurement of albendazole-sulphoxide in blood is recommend by some guidelines for AE or in the event of adverse drug effects [5] , but this was not performed during the respective period as it was unavailable at our center . Frequent measurements would slightly increase overall costs . Finally , albendazole treatment duration had to be estimated for a few cases based on current treatment guidelines [5][6] , which are in line with institutional treatment guidelines . In summary , this study presents a detailed analysis of direct healthcare-related costs linked to AE and CE in a developed country . This analysis demonstrates that hospitalizations ( including surgical interventions ) and albendazole treatment are the main drivers of costs for both CE and AE and , therefore , constitute potential targets for cost reduction strategies . Future studies from healthcare systems in countries with other socioeconomic backgrounds could further improve our understanding of drivers of the financial burden of echinococcosis in order to develop future and improve current prevention , treatment , and long-term care strategies .
|
Cystic and alveolar echinococcosis , caused by E . granulosus and E . multilocularis , both occur in humans in Austria . Lesions may develop at any site , with the liver being most frequently affected . Morbidity–especially for alveolar echinococcosis–can be significant and treatment may include major surgery or long-term suppressive medical therapy . The present study was performed to investigate direct healthcare-related costs of cystic and alveolar echinococcosis in Austria based on the data from a clinical registry maintained at the reference center for echinococcosis , the reference laboratory for echinococcosis and data from the national disease reporting system . Annual incidences of AE and CE were estimated at λ = 12 and λ = 40 newly diagnosed cases per year , respectively . Estimated costs due to cystic echinococcosis were 486 , 598€ ( 95%CI 341 , 825€ – 631 , 372€ ) per year , and 683 , 824€ ( 95%CI 469 , 161€ - 898 , 486€ ) for AE . Major cost drivers were surgical interventions with hospitalizations and drug costs . These data will provide a basis for cost-effectiveness analyses of prevention programs , and highlight possible targets for cost reduction strategies .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
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"diagnostic",
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] |
2019
|
Evaluation of direct costs associated with alveolar and cystic echinococcosis in Austria
|
Yersinia pestis , the causative agent of plague , is typically transmitted by the bite of an infected flea . Many aspects of mammalian innate immune response early after Y . pestis infection remain poorly understood . A previous study by our lab showed that neutrophils are the most prominent cell type recruited to the injection site after intradermal needle inoculation of Y . pestis , suggesting that neutrophil interactions with Y . pestis may be important in bubonic plague pathogenesis . In the present study , we developed new tools allowing for intravital microscopy of Y . pestis in the dermis of an infected mouse after transmission by its natural route of infection , the bite of an infected flea . We found that uninfected flea bites typically induced minimal neutrophil recruitment . The magnitude of neutrophil response to flea-transmitted Y . pestis varied considerably and appeared to correspond to the number of bacteria deposited at the bite site . Macrophages migrated towards flea bite sites and interacted with small numbers of flea-transmitted bacteria . Consistent with a previous study , we observed minimal interaction between Y . pestis and dendritic cells; however , dendritic cells did consistently migrate towards flea bite sites containing Y . pestis . Interestingly , we often recovered viable Y . pestis from the draining lymph node ( dLN ) 1 h after flea feeding , indicating that the migration of bacteria from the dermis to the dLN may be more rapid than previously reported . Overall , the innate cellular host responses to flea-transmitted Y . pestis differed from and were more variable than responses to needle-inoculated bacteria . This work highlights the importance of studying the interactions between fleas , Y . pestis and the mammalian host to gain a better understanding of the early events in plague pathogenesis .
Bubonic plague is the most common form of plague in humans and is the result of transmission of Yersinia pestis into the dermis via the bite of an infected flea . The bacteria survive in the skin and eventually disseminate to the dLN where they replicate to high numbers forming an enlarged lymph node termed a bubo . The cellular architecture of this bubo eventually becomes compromised resulting in hematogenous spread of the bacteria followed rapidly by death of the host . Fleas can also deposit bacteria directly into the bloodstream of a mammalian host resulting in primary septicemic plague that may constitute as many as one third of human cases [1 , 2] . Y . pestis evolved from its closest relative Y . pseudotuberculosis , approximately 1500 to 6400 years ago [3] . An essential step in evolution from an orally acquired pathogen that causes mild gastroenteritis to a highly pathogenic , flea-transmitted pathogen was aquistion of the ability to form a biofilm in the flea [4] . This biofilm blocks the proventriculus , a valve structure between the esophagus and midgut of the flea , and interferes with the flea’s ability to take a blood meal [5] . Blocked fleas make repeated attempts to feed until they eventually succumb to starvation or dehydration . Lorange et al . studied the vector efficiency of blocked rat fleas and found that less than half of the fleas transmitted Y . pestis while attempting to feed . For the fleas that did transmit , as many as 4000 CFU were detected , but the median number transmitted was 82 CFU [6] . The exact events that occur in the dermis immediately after deposition of Y . pestis by a flea remain enigmatic . Macrophages are considered permissive for Y . pestis survival whereas neutrophils are much more bactericidal toward the organism [7]; however , up to 10% of Y . pestis may survive after phagocytosis by neutrophils [8 , 9] . Y . pestis has been observed within macrophages and neutrophils early after intraperitoneal infection [10] , but it is unclear if an intracellular phase is important in bubonic plague pathogenesis . The most important virulence factor of Y . pestis is the pCD1 plasmid-encoded type III secretion system ( T3SS ) . The T3SS effector proteins are preferentially translocated into phagocytes in vivo [11] where they disrupt multiple signaling pathways in phagocytes resulting in cellular paralysis , necrosis or apoptosis [12] . Genes encoding the T3SS are induced by growth at 37°C , but minimally expressed in the flea midgut [13 , 14] . Y . pestis also produces a proteinaceous antiphagocytic capsule called F1 . Similar to the T3SS , the F1 capsule is poorly expressed in the flea and induced by growth at 37°C [14 , 15] . Thus , there is likely a period immediately after deposition of the bacteria in the dermis until the T3SS apparatus , its secreted effectors and F1 capsule can be expressed when the Y . pestis is vulnerable to phagocytes . Neutrophils are highly phagocytic innate immune cells that ingest and destroy invading bacteria . We have previously used intravital microscopy to examine Y . pestis-host cell interactions in vivo [16] . We found that large numbers of neutrophils are recruited to the infection site within 2–3h after i . d . injection of Y . pestis . Interestingly , recruited neutrophils rapidly associated with bacteria and many trafficked Y . pestis away from the injection site . In contrast , dendritic cells ( DC ) , potent antigen presenting cells , were not recruited to the injection site and showed minimal interaction with bacteria [16] . Many previous studies of the early events following Y . pestis infection , including our own , have used intradermal needle inoculation to model bubonic plague transmission . However , needle inoculation differs from the natural route of transmission , the bite of an infected flea , in a number of ways . The flea mouthparts that are inserted into the skin during feeding are at least an order of magnitude smaller in diameter than the 30 gauge needle used for i . d . injections . Flea saliva also contains a number of molecules whose homologs in other blood feeding arthropods affect innate immunity [17] . Additionally , Y . pestis isolated from flea midguts display a markedly different phenotype when compared to in vitro cultured bacteria , including increased expression of biofilm extracellular matrix components and antiphagocytic factors [14] . Thus , we hypothesized that transmission by the natural plague vector might alter the numbers or composition of innate immune cells recruited to the site of infection and their interactions with bacteria . The low transmission efficiency of the flea vector makes quantitative assessment of cellular interactions difficult and led us to develop intravital microscopy methods to study these interactions in vivo . The goal of the present study was to characterize the Y . pestis-host cell interactions that occur in the dermis early after transmission of bacteria by the rat flea Xenopsylla cheopis .
Before we could characterize the responses to flea-transmitted Y . pestis in vivo , we needed to develop methods for reliably identifying flea bite sites on the mouse ear and characterize the neutrophil response to uninfected flea bites . To examine the gross effects of flea feeding on mouse skin , we constructed a simple clamp-on feeding chamber that would allow fleas to feed on a mouse ear ( S1 Fig ) . The chamber containing fleas was placed on the ear for 50 min and dissecting microscope images of the ear were captured before and after feeding . Overall , the most noticeable effect of uninfected flea feeding was marked vasodilation in the ear ( Fig . 1A ) . Occasionally , a flea bite would result in a small , discrete erythematous spot at the bite site , but more often there was no obvious visible indicator of where the fleas had fed . Similar results were seen after feeding of uninfected and infected fleas ( S2 Fig ) . The absence of any consistent localized gross pathology after flea feeding made it difficult to reliably identify flea bite sites . Fortunately , intraperitoneal injection of mice with the non-membrane permeant fluorescent DNA stain Sytox Blue prior to flea feeding resulted in the staining of cells damaged as the fleas inserted their mouthparts into the skin . Foci of Sytox Blue stained nuclei of damaged cells can be seen in areas where fleas have fed ( Fig . 1B ) . To confirm that these areas are flea bites , we injected mice i . v . with the vascular dye Q655 prior to flea exposure . Damage to blood vessels during flea feeding caused localized vascular dye leakage resulting in bright Q655 staining surrounding the vessel . These bright Q655 areas corresponded to areas of Sytox Blue staining ( Fig . 1C , D left panel ) . As further confirmation of our ability to identify flea bite sites , we anesthetized fleas while their mouthparts were embedded in the mouse skin and used microscissors to cut the mouthparts off above the skin . The highly autofluorescent nature of the flea exoskeleton allowed for confocal imaging of the mouthparts , which were found embedded in an area containing both Sytox Blue and Q655 staining ( Fig . 1C ) . Thus , the Sytox Blue method is a reliable way of identifying flea bite sites on mouse skin . To characterize the neutrophil response to individual uninfected flea bites , we fed fleas on LysM-eGFP transgenic mice that express high levels of eGFP in neutrophils and lower levels in macrophages in the skin [18] . The total neutrophil recruitment to the flea bite sites was evaluated and assigned a numerical score from 0 ( no recruitment of neutrophils ) to 4 ( influx of large numbers of neutrophil resulting in a aggregated mass of eGFPbright cells at the bite site ) . We found that uninfected flea feeding recruited remarkably few eGFPbright neutrophils to the flea bite , despite the cellular damage and vascular leakage present at the bite site ( identified by Sytox Blue or Q655 staining , respectively ) ( Fig . 1D , S1 Video ) . Neutrophil recruitment scores for four independent experiments ranged from 0 to 2 with a mean of 0 . 9 +/-0 . 4 ( Fig . 2 ) . In Fig . 1D multiple flea bites can be seen in the micrograph by Sytox Blue and Q655 staining . Neutrophils appear to be much more heavily recruited to the flea bite near the center of the field . Interestingly , we observed the mobilization and migration of eGFPdim cells towards the bite site over the course of the experiment ( S1 Video ) . Similar eGFPdim cell movement towards uninfected flea bites was observed in four independent experiments . These eGFPdim cells have been characterized as F4/80+ , CD11b+ macrophages in the dermis of the LysM-eGFP mouse [18] . Thus , uninfected flea bites result in very little neutrophil recruitment and resident macrophages appear to migrate towards flea bite sites . Y . pestis is typically transmitted by fleas in which the bacteria have established a biofilm that blocks the proventriculus . These blocked fleas are unable to take a normal blood meal and make repeated unsuccessful attempts to feed , partially withdrawing their mouthparts and reprobing . We hypothesized that this might result in more damage to the skin and consequently increased neutrophil recruitment in comparison to uninfected flea bites . To test this , blocked fleas infected with a T3SS deficient strain of Y . pestis expressing the fluorescent protein mCherry were allowed to feed on LysM-eGFP mice for approximately 50 min . Again , the Sytox Blue reagent was used to identify flea bite sites . We found obvious flea bites where bacteria could not be detected in >50% of the experiments involving feeding of blocked fleas , which is in agreement with what is known about flea transmission efficiency [6] . These bites were imaged to determine the neutrophil response to blocked flea bites without the influence of bacteria at the bite site . We observed a highly variable neutrophil response to blocked flea bites ( Fig . 3 ) . The responses ranged from recruitment of very few neutrophils ( Fig . 3A , S2 Video ) , similar to what is seen with uninfected fleas , to an influx of large numbers of neutrophils to the flea bite site ( Fig . 3B , S3 Video ) . When the neutrophil recruitment in nine independent experiments was scored , the results ranged from scores of 0 to 4 with a average score of 2 +/- 0 . 4 ( Fig . 2 ) . The skin of mice fed on by blocked fleas had more foci of Sytox Blue staining than skin fed on by uninfected fleas and these foci often appeared in clusters , presumably due to a flea making repeated attempts to feed in the same location . The numbers of neutrophils recruited did not appear to correlate with the amount of Sytox Blue staining at the bite site ( Fig . 3 ) . Thus , the neutrophil response to blocked flea bites is much more variable than the response to uninfected flea bites . Similar to observations of uninfected flea bites , movement and migration of eGFPdim macrophages towards blocked flea bites was common , occurring in seven out of nine independent experiments . In the two experiments where macrophage migration was not seen , large numbers of eGFPbright neutrophils were recruited to the bite site , which may have obscured observation of the macrophage movement . To characterize the neutrophil response to Y . pestis transmitted via the bite of an infected flea , blocked fleas infected with Y . pestis pMcherry were fed on a LysM-eGFP mice , flea bites were identified by Sytox Blue staining , and bite sites that contained mCherry+ bacteria were imaged . Consistent with previous studies [6] , fleas transmitted a highly variable number of bacteria into the skin ( determined qualitatively by image analysis ) . We show three example experiments representing responses to low ( roughly ten or fewer ) , moderate ( roughly hundreds ) and high ( roughly thousands ) numbers of transmitted bacteria as determined by visual inspection of the bite site ( Figs . 4 , S4 , S5 and S6 Video ) . For the purpose of comparison , images from the 0 h and 4 h timepoints after needle inoculation of Y . pestis ( ~1000 CFU ) or a sterile 30 gauge needle stick alone are also shown in Fig . 4 . In seven independent experiments where bacteria could be seen at flea bites , the neutrophil recruitment scores ranged from 0 to 4 with an average of 2 . 8 +/- 0 . 4 ( Fig . 2 ) . Overall , the number of neutrophils recruited to bite sites containing bacteria was higher than for uninfected flea bites or blocked flea bites without transmission . Furthermore , neutrophil recruitment appeared to correlate with the number of bacteria present at the bite site ( Fig . 2 ) . Interestingly , even when large numbers of neutrophils were recruited to the bite site and associated with bacteria , very little translocation of the bacteria was observed . When bacteria were observed moving , they were frequently ( observed in four out of seven experiments where bacteria were present at the flea bite site ) associated with eGFPdim cells , which are likely macrophages ( Fig . 5 , S7 Video ) . Movement of bacteria in association with eGFPbright neutrophils was a rare event , observed in only 1 of the 7 experiments . This is in contrast to what is observed after needle inoculation of bacteria into the dermis , where many bacteria are trafficked away from the injection site in association with neutrophils [16] . Because the neutrophil recruitment to needle-inoculated Y . pestis is so robust , the presence of large numbers of eGFPbright cells may obscure bacteria-eGFPdim macrophage interactions . To address this possibility , we treated Lys-eGFP mice with anti-GR1 , an antibody that efficiently depletes neutrophils and , to a lesser extent , inflammatory monocytes , thus permitting the visualization of the macrophage response to Y . pestis in the near absence of neutrophils . We observed movement of eGFPdim macrophages towards the injection site ( S8 Video ) , similar to what is seen in response to flea bites ( S1–S5 Video ) . However , in contrast to what was observed after flea-transmission ( S7 Video ) , in four independent experiments with needle-inoculated Y . pestis we did not observe movement of bacteria in association with eGFPdim cells , suggesting that flea-transmitted Y . pestis may be more likely than needle-inoculated bacteria to interact with macrophages in vivo . Dendritic cells are antigen presenting cells that reside in peripheral tissues and migrate into the lymphatics after contact with pathogens [19] . To determine whether or not DC interact with Y . pestis after flea-borne transmission , we used a transgenic mouse expressing yellow fluorescent protein ( YFP ) under control of the itgax promoter [20] . Itgax encodes a component of CD11c , a molecule widely used to identify DCs . The bite sites of uninfected fleas , blocked fleas that did not transmit bacteria , and blocked fleas that had deposited Y . pestis in the dermis were imaged for at least 4 hours post-feeding ( Fig . 6A ) . In response to uninfected flea bites DCs appear to randomly move through the dermis ( Fig . 6A , S9 Video ) , similar to what is observed in a naïve mouse ear ( Fig . 6A , S10 Video ) . Consistent with what was observed after needle inoculation of these mice [16] , we did not observe any notable interaction between DCs and flea-transmitted bacteria ( Fig . 6A , S11 Video ) . Interestingly , while there was no net influx of a large number of DCs like that seen with neutrophils , the cells that were present appeared to migrate towards the flea bite sites that contained Y . pestis . A similar phenomenon was seen at some blocked flea bites where no bacteria were deposited in the dermis , but was much more variable ( Fig . 6A , S12 Video ) . To quantify this cellular movement , image analysis software was used to track the migration of these cells over the course of the experiment . Cell tracking and displacement are shown in the bottom panels of Fig . 6A . The direction of displacement of each cell track was scored as being “toward” , “away” from or “neutral” relative to the flea bite site ( Fig . 6B ) . Additionally , the average number of cell tracks with displacement >30 μm was determined for each experiment ( Fig . 6C ) . We conclude that DCs migrate towards flea-transmitted Y . pestis or blocked flea bites , but not to uninfected flea bites in the dermis and that there was overall more displacement of DCs when bacteria were present at the bite site . Fleas are considered to be primarily capillary feeders; they probe the skin with their mouthparts until a blood vessel is cannulated and a blood meal is siphoned directly from the vessel [21 , 22] . Blocked fleas can deposit Y . pestis in the extravascular dermal tissue or , less frequently , directly into the lumen of a blood vessel [2] . To determine the numbers of bacteria transmitted by fleas in our experiments and the tissue localization of flea-transmitted Y . pestis early after infection , we collected ear dermis , dLN and spleen tissue samples from mice after completion of the intravital microscopy experiments described above ( approximately 5 h after termination of flea feeding ) . Tissues were triturated and plated to determine the number of colony forming units ( CFU ) present . The results of each independent experiment consisting of an individual mouse are shown in S1 Table . The number of blocked fleas that fed on each mouse varied from one to seven . We recovered no CFUs from 22% of the mice tested despite many of these mice being fed upon by as many as four blocked fleas . Among mice that had detectable bacteria in the dermis after flea exposure , the number of dermal CFUs ranged from 5 to 3660 with a median of 237 . 5 CFUs . The number of CFUs cultured from the spleen served as an indicator of transmission of bacteria directly into the bloodstream . Among the 28 mice that had detectable CFUs in any of the tissues tested , 23 ( 82% ) had anywhere from 1 to 4000 CFUs/spleen . Two mice had bacteria in their spleens , but no bacteria were detected in their dermis or dLN , indicating that fleas had deposited bacteria directly into the lumen of blood vessel during the feeding attempt . Despite harvesting the tissues at the early time point of ~5 h post-feeding and the use of a highly attenuated strain of Y . pestis , we found a surprisingly high number of bacteria present in the dLN . The numbers ranged from 15 to 1000 CFUs with a median of 270 CFUs/LN . Thus , dissemination of bacteria from the dermis to draining lymph node can occur within 5 h of flea feeding . The above experiments were done in conjunction with the intravital microscopy studies , thus the mice were exposed to variable numbers of blocked fleas , received variable numbers of flea bites , and were assayed ~5 h after flea feeding . To quantify transmission by individual fleas , we performed experiments where mice were exposed to 1 to 3 blocked fleas placed on one or both ears in an effort to consistently obtain mice that had been fed on by only 1 blocked flea . Mice were euthanized 1 h after termination of flea exposure and their ear , dLN and spleen tissues assayed for CFU . Each time a mouse ear had been fed on by at least one blocked flea it was recorded as a feeding event . In total , we exposed a total of 25 mice and recorded 31 feeding events . Of these events , 14 ( 45 . 2% ) resulted in transmission of bacteria into at least one of the tissues assayed and these are depicted in Fig . 7 . Nine ( 29% ) of the feeding events resulted in deposition of Y . pestis into the dermis . We detected bacteria in the spleen of 9 ( 29% ) mice 1 h after removal of fleas , suggesting that bacteria were introduced directly into the bloodstream during flea feeding . Bacteria were detected in the dLN after 6 ( 19 . 4% ) individual feeding events . The presence of bacteria in the dLN at this early time point indicates that some bacteria disseminate to the dLN within 2 h after introduction into the dermis .
The degree to which flea transmission influences the pathogenesis of bubonic plague or the innate immune response to infection is unknown . Here we characterize the very early neutrophil , macrophage and dendritic cell recruitment to flea bites and flea-transmitted Y . pestis . We developed a method for reliably and accurately identifying flea bite sites in mice using the DNA stain Sytox Blue . Using this method we imaged the host cellular response to uninfected flea bites and found minimal recruitment of neutrophils to the bite site . This was surprising in light of previous work showing a rapid neutrophil response to tissue damage [18 , 23] . Specifically , a study by Peters et al . showed robust neutrophil recruitment to uninfected sand fly bites on the same LysM-eGFP mouse strain used in our study [18] . Sand flies are “pool feeders” in that they feed by wounding the dermal microvasculature with serrated mouthparts and siphoning blood from a hemorrhagic pool formed within the wound . In contrast , fleas are considered “capillary feeders” that use their small mouthparts to cannulate a dermal blood vessel , with apparently little damage to the cells at the bite site . This may result in less inflammation and neutrophil recruitment than is seen at sand fly bite sites . Additionally , flea saliva contains a variety of components homologous or analogous to salivary proteins in other blood feeding arthropods that are known to be anti-inflammatory [17] . Both of these factors may be responsible for the low numbers of neutrophils recruited to uninfected flea bites . Interestingly , we observed the mobilization and migration of eGFPdim cells towards the flea bite site . In the LysM-eGFP transgenic mouse used in this study , these eGFPdim cells in the dermis are largely F4/80+ tissue resident macrophages [18] . We did not observe movement of these cells towards needle inoculation sites in our previous study [16] , but the large numbers of eGFPbright neutrophils recruited to tissue damage done by the needle made it difficult to see the dim macrophages . When we examined blocked flea bites in LysM-eGFP mice where bacteria had been deposited at the bite site , we observed more neutrophil recruitment relative to uninfected flea bites and the neutrophil numbers appeared to correlate with the amount of bacteria at the site . This suggests that the neutrophils were recruited to the bacteria and not the bite itself . It also shows that any suppressive effect that flea saliva may have on neutrophil recruitment could not override the response to bacteria in the dermis . Despite the presence of a large number of neutrophils , we observed very little movement of Y . pestis at the bite site . This is in contrast to our previous study showing many injected bacteria being trafficked away from the injection site in association with eGFPbright neutrophils [16] . Y . pestis isolated from flea midguts are more resistant to phagocytosis by macrophages and neutrophils than broth-cultured bacteria due to upregulation of a family of insecticidal-like toxin complex proteins in the flea [14 , 24] . The two-component regulatory system PhoP-PhoQ , important for the resistance of Y . pestis to stressors experienced in the mammalian host such as low pH , osmotic or oxidative stress , or antimicrobial peptides is also upregulated in the flea relative to in vitro broth-cultured bacteria [14 , 25] . Additionally , Y . pestis forms a biofilm in the flea midgut as result of increased production of a polysaccharide extracellular matrix ( ECM ) in this environment . The effects of this ECM on mammalian host response are unknown , but structurally similar ECM produced by Staphylococci protects against innate immune effectors [26] . Thus , the phenotype of flea-derived Y . pestis differs considerably from broth-grown bacteria in ways that may influence pathogenesis and innate host response . Further work will be needed to evaluate the interactions of flea-grown Y . pestis with innate immune cells in vivo . Interestingly , when bacterial movement at the flea bite site was observed , many of these bacteria were associated with eGFPdim macrophages . In each case the macrophages did not transport bacteria completely away from the injection site , but remained in the field of view for the duration of the experiment ( Fig . 5 , S7 Video ) . Again , this is in contrast to experiments with needle-inoculated bacteria where most of the Y . pestis movement was in association with neutrophils that transported bacteria completely out of the field of view [16] . However , the large number of eGFPbright neutrophils present may have obscured the rare eGFPdim events in these experiments . To address this , we needle-inoculated PMN-depleted LysM-eGFP mice with Y . pestis expressing dsRed and imaged them by confocal . While eGFPdim macrophages were recruited to the injection site , we observed minimal movement of bacteria in association with these cells . These results suggest that flea-transmitted bacteria may preferentially interact with macrophages over neutrophils . This would have implications for Y . pestis pathogenesis , as macrophages are much more permissive for Y . pestis survival and growth than neutrophils [7] . Imaging of blocked flea bites in CD11c-YFP mice revealed minimal interactions between YFP+ cells and flea-transmitted Y . pestis at the bite site . It is important to note that YFP expression in these mice is not limited exclusively to DCs , nor does every subset of DC in the dermis express YFP . It is more accurate to classify the YFP+ dermal cells in our experiments as antigen-presenting mononuclear phagocytes [27]; however , for simplicity , we refer to these YFP+ cells as DCs . While we did not observe a massive influx of DCs , they did appear to mobilize and migrate specifically towards blocked flea bites containing bacteria . The consequences of this migration of DCs towards flea-transmitted bacteria are unknown . DCs do not appear to associate with bacteria at the bite site in the timeframe studied , but it remains possible that they would show more association with bacteria later after infection . These results are consistent with our previous study showing minimal interaction between needle inoculated Y . pestis and DC early after infection [16] . Uninfected flea bites did not recruit DCs and migration towards blocked flea bites where bacteria were not detected was variable . It is plausible that some bacterial components , such as lipopolysaccharide , could have been introduced into the bite site by blocked fleas even if no whole bacteria were transmitted . These bacterial components could act as pathogen associated molecular patterns ( PAMPs ) that directly or indirectly stimulate recruitment of innate immune cells [28] . It is also possible that a very small number of bacteria might have been transmitted , but were undetectable by microscopy . These factors may explain the variability in cellular response we observed . Additionally , blocked fleas are unable to take a blood meal and tend to probe the skin in repeated unsuccessful feeding attempts . The additional tissue damage from this probing could explain the increased cellular recruitment to blocked compared to uninfected flea bites , although the amount of Sytox Blue staining at the bite site did not appear to correlate with neutrophil recruitment . The CFU assays of the dermis , dLN , and spleen early after flea feeding yielded highly variable results , consistent with previous studies on the regurgitative transmission mechanism of X . cheopis [6] . It was not uncommon to find several hundred or even thousands of bacteria in the spleen after flea feeding . This is most likely due to regurgitation of bacteria directly into the bloodstream during the blocked flea’s attempt to feed as has been previously described [2] . Interestingly , several animals had hundreds or more CFU in the dLN at ~5 h post-feeding . This prompted us to look 1 h post-feeding where we found some animals with a small number of CFU in the dLN . Overall , these data suggest that very rapid dissemination to the spleen and dLN is a common occurrence after flea transmission of Y . pestis . The data also suggest that a small number of flea-transmitted bacteria may move so rapidly into the lymphatics that they bypass any significant interactions with phagocytes at the bite site . The ultimate fate of these early LN disseminators is unknown . Despite the historical significance of Y . pestis and the importance of fleas in the plague transmission cycle , the early events in the skin after deposition of bacteria via blocked flea bite are poorly understood . Here we have characterized the innate cellular recruitment to uninfected and infected flea bites in vivo . We also gathered quantitative data on the numbers and tissue distribution of Y . pestis transmitted by fleas . Our results show a much greater neutrophil response to flea-transmitted Y . pestis than to uninfected flea bites . We also observed migration of resident tissue macrophages towards uninfected and blocked flea bite sites and their association with flea-transmitted Y . pestis . Similar migration of dendritic cells towards infected , but not uninfected , flea bites was observed . Interestingly , we found Y . pestis in the dLN by 1 h after flea exposure , suggesting that initial dissemination of bacteria to the LN occurs more quickly than was previously appreciated [29 , 30] . In support of this , Gonzalez et al . recently reported that needle-injected Y . pestis could be found in the dLNs of some mice as early as 10 min post-infection [31] . Future work will be aimed at determining the fate of these early disseminators and their importance in bubonic plague pathogenesis .
Xenopsylla cheopis fleas were infected with Y . pestis pMcherry ( strain KIM6+ [virulence plasmid negative , pigmentation locus positive] transformed with a pMcherry plasmid [Clontech] ) using a previously described artificial feeding system [5] . Fleas were monitored for blockage by microscopic examination for up to six weeks post-infection . Blockage was diagnosed by the presence of fresh blood in the flea esophagus , but not the midgut , immediately after feeding . C57BL/6J LysM-eGFP knock-in mice were originally created by T . Graf [32] ( Albert Einstein University , Bronx , NY ) and were bred by Taconic Laboratories under a contract with NIAID . C57BL/6J ( stock number 000664 ) and CD11c-YFP ( stock number 008829 , originally described Lindquist et al . [20] ) mice were purchased from The Jackson Laboratory ( Bar Harbor , ME ) . Ten- To 20-week-old female mice were used in all experiments . All mice were maintained at the Rocky Mountain Laboratories animal care facility under specific-pathogen-free conditions . For experiments involving PMN-depletion , mice were injected i . p . with 250 μg anti-GR1 antibody ( clone RB6–8C5 , BioXCell , West Lebanon , NH ) 24 h and 4 h prior to infection with ~1000 CFU of dsRed-expressing Y . pestis , as described in [16] . This treatment results in >95% depletion of Ly6G+ neutrophils . The Y . pestis strain expressing dsRed was used instead of the mCherry-expressing strain in this experiment to be consistent with a previous study of the response to needle-inoculated Y . pestis , and due to a higher level of fluorescent protein expression in broth culture . Mice were anesthetized by subcutaneous injection of a ketamine/xylazine mixture and secured on a heating pad to maintain body temperature . Where indicated , mice were injected with 250 μM Sytox Blue ( Life Technologies ) in 150 μL PBS i . p . and , in some cases , 60 μl of Qtracker655 ( 2 μM stock , Life Technologies ) in 150 μl PBS i . v . 10 to15 min prior to flea exposure . Fleas were immobilized by incubation on ice and placed in a custom-made feeding chamber consisting of a 200 μl PCR tube and a foam padded plastic clamp ( S1 Fig ) . This chamber was then clamped onto the ear of the mouse and the fleas allowed to warm to room temperature . The fleas were in contact with the mouse for 10 min for uninfected fleas or 50 min for blocked fleas . The mouse and feeding chamber were then placed in a jar containing isoflurane for approximately 30 sec to anesthetize the fleas . The chamber was then removed from the ear and the fleas collected and microscopically examined to determine if fresh blood was present in their digestive tract indicating feeding . In some cases , a model SMZ1500 dissecting microscope ( Nikon , Tokyo , Japan ) equipped with a model DP72 color camera ( Olympus , Center Valley , PA ) was used to capture images of mouse ears before and after exposure to fleas . The ears of LysM-eGFP or CD11c-YFP mice were imaged by confocal microscopy as previously described [16] . Briefly , mice were anesthetized with an isoflurane-O2 mixture provide by nose cone and their ears mounted to a coverslip on the stage of a Zeiss LSM 510 Meta confocal microscope ( Zeiss , Oberkochen , Germany ) equipped with an incubated chamber set to 30°C . Z stacks were acquired with a 20x objective at 2 min intervals for the indicated duration and the images obtained were processed using Imaris 6 . 3 . 1 software ( Bitplane , South Windsor , CT ) . All supplemental video files are shown at the same magnification with the exception of S7 Video which has been digitally zoomed using the Imaris software . Neutrophil recruitment was scored by assessment of total neutrophil accumulation observed over the duration of videos of Lys-eGFP mice fed upon by uninfected or blocked fleas . Each video was scored by 3 lab members on a scale from 0 to 4 in whole number increments , with 0 representing essentially no net recruitment of neutrophils and 4 representing massive accumulation of neutrophils forming a large aggregate at the bite site . The results are shown as the mean and standard error of the mean ( SEM ) . Tracking of YFP+ cells in time series of CD11c-YFP mice was accomplished using the tracking function within the Imaris 6 . 3 . 1 software package . Once the cellular movement had been tracked , we used the software to determine the direction of net displacement of each cell . Limiting further analysis to YFP+ cells with a net displacement of at least 30 μm over the course of the experiment , we scored each cell displacement event as being towards ( displacement within a 45° angle in the direction of the bite site ) , away ( displacement within a 45° angle in the opposite direction of the bite site ) , or neutral ( all remaining displacement events ) relative to the flea bite site . For mice that had not been fed on by fleas , a spot at the center of field of view was arbitrarily chosen to represent the flea bite site . Ear , draining cervical lymph node and spleen tissues were collected from mice after euthanasia . Ears were separated with forceps into ventral and dorsal halves . Tissues were placed in Lysing Matrix H bead tubes ( MP Biomedicals ) containing 500 μl of PBS and disrupted with a Fastprep 120 ( Thermo Savant ) . The numbers of Y . pestis pMcherry CFU in the tissue samples were determined by dilution and plating on blood agar plates containing 100 μg/ml carbenicillin . All animal studies were performed under protocols adhering to guidelines established by the Public Health Service Policy on Humane Care and Use of Laboratory Animals . The protocols were reviewed and approved by the Rocky Mountain Laboratories Animal Care and Use Committee ( AALAS unit number 000462 , PHS-OLAW number A-4149–01 ) . For experiments measuring neutrophil recruitment scores , data were analyzed using a Kruskal-Wallis nonparametric test followed by a Dunn’s multiple comparison test . For experiments determining the direction of DC migration , data were analyzed using two-way ANOVA with Tukey’s multiple comparison post-test . For experiments measuring total DC displacement , data were analyzed using one-way ANOVA with Holm-Sidak’s multiple comparisons post-test .
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Flea-borne transmission is central to the natural history of the plague bacillus Yersinia pestis , and infection within the context of flea feeding may affect the pathogenesis of bubonic plague . We analyzed the mammalian host response to Y . pestis in the skin immediately after transmission by its natural vector , the rat flea Xenopsylla cheopis , to observe differences relative to the response to needle-inoculated bacteria . Our results show that uninfected flea bites induce minimal inflammation , but flea-transmitted Y . pestis cause the recruitment of neutrophils roughly in proportion to the number of bacteria deposited in the skin . We observed interactions of flea-transmitted bacteria with macrophages , a cell type much more permissive than neutrophils for survival and growth of Y . pestis . We found that dendritic cells , important sentinel antigen presenting cells , were recruited to , but had minimal interaction with , flea-transmitted bacteria . Additionally , we found that Y . pestis could disseminate from the flea bite site to the draining lymph node and spleen as early as 1 h after flea feeding , significantly earlier than has been previously reported . This study reveals important differences between needle-inoculated and flea-transmitted Y . pestis in the immediate host response to infection and improves our understanding of the early host-bacterium interactions in plague pathogenesis .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
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Dermal Neutrophil, Macrophage and Dendritic Cell Responses to Yersinia pestis Transmitted by Fleas
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A wide spectrum of disease severity has been described for Human African Trypanosomiasis ( HAT ) due to Trypanosoma brucei rhodesiense ( T . b . rhodesiense ) , ranging from chronic disease patterns in southern countries of East Africa to an increase in virulence towards the north . However , only limited data on the clinical presentation of T . b . rhodesiense HAT is available . From 2006-2009 we conducted the first clinical trial program ( Impamel III ) in T . b . rhodesiense endemic areas of Tanzania and Uganda in accordance with international standards ( ICH-GCP ) . The primary and secondary outcome measures were safety and efficacy of an abridged melarsoprol schedule for treatment of second stage disease . Based on diagnostic findings and clinical examinations at baseline we describe the clinical presentation of T . b . rhodesiense HAT in second stage patients from two distinct geographical settings in East Africa . 138 second stage patients from Tanzania and Uganda were enrolled . Blood samples were collected for diagnosis and molecular identification of the infective trypanosomes , and T . b . rhodesiense infection was confirmed in all trial subjects . Significant differences in diagnostic parameters and clinical signs and symptoms were observed: the median white blood cell ( WBC ) count in the cerebrospinal fluid ( CSF ) was significantly higher in Tanzania ( 134cells/mm3 ) than in Uganda ( 20cells/mm3; p<0 . 0001 ) . Unspecific signs of infection were more commonly seen in Uganda , whereas neurological signs and symptoms specific for HAT dominated the clinical presentation of the disease in Tanzania . Co-infections with malaria and HIV did not influence the clinical presentation nor treatment outcomes in the Tanzanian study population . We describe a different clinical presentation of second stage T . b . rhodesiense HAT in two distinct geographical settings in East Africa . In the ongoing absence of sensitive diagnostic tools and safe drugs to diagnose and treat second stage T . b . rhodesiense HAT an early identification of the disease is essential . A detailed understanding of the clinical presentation of T . b . rhodesiense HAT among health personnel and affected communities is vital , and awareness of regional characteristics , as well as implications of co-infections , can support decision making and differential diagnosis .
Human African Trypanosomiasis ( HAT ) , also known as sleeping sickness , is caused by the protozoan parasites T . b . gambiense ( West and Central Africa ) and T . b . rhodesiense ( East and South Africa ) . The disease is transmitted by tsetse flies ( Glossina ssp . ) predominantly in the rural areas of most of sub Saharan Africa . 60 Million people live at risk of infection , but less than 10% are under adequate surveillance [1] , reflecting its neglected status . Sleeping sickness caused by either subspecies presents in two disease stages defined as the first , or haemo-lymphatic stage and the second , meningo-encephalitic stage . Diagnosis of HAT is made in blood , lymph and the cerebrospinal fluid ( CSF ) . The second stage of the disease is indicated by the presence of trypanosomes and/or an elevated white blood cell ( WBC ) count ( ≥5WBC/mm3 ) in the CSF . The disease stage and the causative species of infection direct the choice of treatment . T . b . gambiense infections are treated with pentamidine in the first stage and eflornithine , a combination of eflornithine and nifurtimox , or melarsoprol in the second stage [2]–[4] . T . b . rhodesiense first and second stage infections are treated with suramin and melarsoprol respectively [2] . In the field , the trypanosome subspecies is entirely determined by the geographical location of the patient as the distinction of T . b . gambiense and T . b . rhodesiense is only possible in well equipped laboratories through PCR analysis . The detection of the human serum resistance-associated ( SRA ) gene unequivocally identifies T . b . rhodesiense trypanosomes [5] , [6] . In Uganda , the only country where both forms of the disease are present , a potential geographical overlap of the two endemic areas has become likely [7] . This would hamper determination of infective trypanosomes under field conditions and therefore also the identification of the correct treatment . For first stage infections there are no specific clinical signs and symptoms in both forms of the disease; fever , headache and loss of appetite are common . In T . b . rhodesiense the presence of a chancre at the site of the infective bite may be indicative for a trypanosome infection [8] . Second stage infections show disease-characteristic neuro-psychiatric signs and symptoms: severe endocrinological and mental disturbances and severe motor problems are the main signs [9] . While often considered together , Gambiense and Rhodesiense HAT are clinically and epidemiologically different diseases [10] . T . b . gambiense HAT is a chronic disease , whereas T . b . rhodesiense is characterized by an acute disease progression . If left untreated , both forms of HAT are fatal . The mean time to reach the second stage has been estimated at over one year for T . b . gambiense [11] but only 3 weeks for T . b . rhodesiense HAT [12] . Correspondingly , average times from infection to death are almost 3 years and 6 to 12 months , respectively [11] , [12] . A diversity of forms of clinical progression from asymptomatic to acute have been reported for T . b . gambiense infections [13]–[15] . This seems to be even more pronounced for T . b . rhodesiense infections; a wide spectrum of disease severity ranging from a chronic disease pattern in southern countries of East Africa with existing reports of asymptomatic carriers [16] to an increase in virulence towards the north had been described [17] . Even though those differences were already described more than 60 years ago [18] the first comparative study was carried out in 2004: on the basis of the SRA gene polymorphism , trypanosomes isolates from Uganda ( acute profile ) and Malawi ( chronic profile ) confirmed to be of different genotypes . However , clinical characteristics of the study groups were limited the presence of a chancre and the self-reported duration of illness [19] . Another hypothesis postulates that the differences in disease severity could be attributed to differences in genetic resistance to trypanosomiasis among host populations [18] . From the estimated 50′000 to 70′00 cases per year [20] , over 97% are T . b . gambiense cases and only a few thousand are due to T . b . rhodesiense [1] . Therefore , most literature concentrates on T . b . gambiense HAT . Its clinical picture and related cardiac and endocrinological disorders have been extensively described [21]–[28] . On the other hand , literature on the clinical aspects of T . b . rhodesiense HAT is scarce . We identified four studies ( see table 1 ) describing its clinical presentation . Only one study in 60 patients infected with T . b . rhodesiense was designed prospectively and used a standardized questionnaire [29] . In this paper we describe the clinical presentation of second stage T . b . rhodesiense HAT in 138 patients from two distinct geographical settings in East Africa . We compare our findings to the existing literature and discuss factors that could explain the differences observed .
The Kaliua Health Centre ( KHC ) , a 50-bed missionary hospital in Tanzania ( Urambo District ) and the Lwala Hospital , a designated 100- bed district hospital in Uganda ( Kaberamaido District ) participated in the Impamel III program ( improved application of melarsoprol ) . A proof-of-concept trial ( n = 60 ) followed by a utilization study ( n = 78 ) to assess the safety and efficacy of the abridged , 10-day melarsoprol schedule for the treatment of second stage HAT [30] , [31] in T . b . rhodesiense patients . Eligible for enrolment were second stage patients with a minimum age of 6 years and confirmed second stage HAT . Patients with first stage infections , pregnant women and moribund or unconscious patients were excluded . Patients were passively enrolled at the study sites . Diagnosis of HAT was made in blood and in CSF . Blood was examined using microscopy and/or the haematocrit centrifugation technique [32] . If trypanosomes were present , a lumbar puncture was performed for disease staging . Analysis of the CSF was done by direct microscopy and/or single modified centrifugation technique and white blood cell ( WBC ) count using counting chambers . Second stage infections were confirmed by the presence of trypanosomes and/or ≥5 WBC/mm3 in the CSF . The standard assessment of co-infections included malaria , filariasis and voluntary testing for HIV/AIDS . The local Principal Investigators filled individual case report forms ( CRFs ) . Data used for describing the clinical presentation of the disease were patient demographics , diagnostic findings , self reported duration of illness and clinical signs and symptoms on admission graded by scale of severity ( grade 0 , 1 , 2 ) . Each participant gave written informed consent . For the participation of children and adolescents ( below 18 years ) the parents , the legal representative or the guardian gave written informed consent . Ethical clearances were obtained from the Ethics Committees in Tanzania ( National Institute for Medical Research ) , Uganda ( Ministry of Health ) and Switzerland ( Ethics Committee of both cantons of Basel ) . Before first patient enrolment , the Impamel III program was registered in the database of Current Controlled Trials ( ISRCTN40537886 ) . All data were double entered and verified using Epi Data 3 . 1 software ( www . epidata . dk ) and analysis was accomplished with the statistical software package STATA Version IC10 . 0 ( STATA , StataCorp , USA ) . The statistical analysis was performed comparing proportions with the Pearson Chi Square and means with the Student's t test . Logistic regression was used to test differences between groups of patients with different co-infections .
The use of the abridged 10-day melarsoprol schedule for the treatment of second stage T . b . rhodesiense HAT was highly satisfactory ( detailed safety and efficacy data to be published separately ) . In this paper we describe the clinical presentation of the disease in 138 second stage patients from Tanzania and Uganda . The majority of patients were passively detected . Nine ( 9 ) patients from Uganda ( 13% ) were actively identified during a survey of the National Agricultural Research Organisation ( NARO ) in the HAT endemic region of the country . There was no significant difference between actively and passively recruited patients for the median WBC count in the CSF ( actively detected: median WBC = 27 , IQR = 24; passively detected: median WBC = 19 , IQR = 43 , p = 0 . 067 ) and the median self-reported duration of illness ( actively detected: median = 3 months , IQR = 2 , passively detected: median = 2 months , IQR = 4 , p = 0 . 141 ) . 14 patients ( 11 in Uganda and 3 in Tanzania ) could not be examined per protocol as they died or were in a comatose state upon arrival at the study sites which led to an exclusion of those patients from the Impamel III trials . By molecular analysis of blood samples , the presence of the SRA gene [33] was demonstrated and confirmed T . b . rhodesiense infection in all trial subjects [34] . Data on the demographic and diagnostic baseline characteristics of the study population are shown in table 2 . The proportion of male ( 57 . 2% ) and female ( 42 . 8% ) patients was comparable . 18 . 8% ( 26/138 ) trial participants were younger than 16 years whereof 88 . 5% ( 23/26 ) were enrolled in Uganda . There were no county-specific differences for the presence of trypanosomes in blood and CSF: 99% ( 68/69 ) of patients from Tanzania and 91% ( 63/69 ) from Uganda had trypanosomes in blood ( p = 0 . 0524 ) and 70% ( 55/69 ) and 86% ( 59/69 ) respectively had trypanosomes in the CSF ( p = 0 . 3690 ) . However , there was a significant difference for the median WBC count in the CSF in Tanzania and Uganda ( 134 vs . 20 WBC/mm3 , p<0 . 0001 ) . Also , a body mass index ( BMI ) below 16 . 5 was more frequent in patients from Uganda ( p<0 . 0001 ) . Clinical signs and symptoms reported at baseline and the level of significance ( 95% ) are summarized in table 3 . Headache , fever , general body pain and joint pains were common in both study populations . Clinical suspicion for cardiac insufficiency was found in both countries: 5 . 1% ( 7/138 ) of the patients had indication for left heart insufficiency ( combination of cough and dyspnoe ) and 5 . 8% ( 8/138 ) for right heart insufficiency ( combination of oedema and hepatomegaly ) . Patients in Uganda had a more unspecific presentation of the disease whereas specific signs and symptoms for second stage HAT , namely sleeping disorders and aggressiveness were more common in patients from Tanzania . To look at changes of diagnostic markers and clinical signs and symptoms over time we compared them in patients grouped by self-reported duration of illness ( see figure 1 ) . In Tanzania and Uganda 21 . 7% ( 15/69 ) and 36 . 2% ( 25/69 ) respectively were diagnosed with HAT having signs and symptoms for one month or less . 47 . 8% ( 33/69 ) of patients from Tanzania and 31 . 9% ( 22/69 ) of patients from Uganda were diagnosed having signs and symptoms of the disease between 1 and 3 months . Respective percentages for diagnosis of HAT after feeling ill for more than 3 months were 30 . 4% ( 21/69 ) in Tanzania and 31 . 9% ( 22/69 ) in Uganda . In both countries , the presence of trypanosomes in blood and/or CSF and the WBC count in the CSF did not significantly change over time . Also , there was no change over time for most of the clinical signs and symptoms . However , we observed that tremor ( p = 0 . 01 ) , walking difficulties ( p = 0 . 040 ) , sleeping disorders at night ( p = 0 . 029 ) , disturbed appetite ( p = 0 . 044 ) and aggressiveness ( p<0 . 001 ) aggravated over time in all patients . Per protocol , standard assessment of co-infections at baseline included malaria and filariasis . 79 . 7% ( 55/69 ) of the patients from Tanzania and 2 . 9% ( 2/69 ) from Uganda were malaria positive on admission . None were found positive for filariasis . The HIV status was determined on voluntary basis . In Tanzania , 94 . 2% ( 65/69 ) of the patients tested their status and 24 . 6% ( 16/65 ) were found positive . In Uganda , 31 . 9% ( 22/69 ) tested their status and 9 . 1% ( 2/22 ) were found positive . We used the data from Tanzania to study implications of malaria and HIV co-infections on the clinical presentation and treatment outcomes of T . b . rhodesiense HAT . No significant difference either in the clinical appearance or in treatment outcomes for those patients was found . Details are shown in table 4 and 5 .
Based on data from the Impamel III trials we describe the clinical presentation of second stage T . b . rhodesiense HAT in Tanzania and Uganda and confirm a wide spectrum of clinical presentation in these two geographically distinct areas in East Africa . In both settings T . b . rhodesiense HAT followed the classical disease pattern , but interestingly the neurological signs and symptoms typical for HAT were seen in a relatively small percentage of patients from Uganda . In patients from Tanzania , however , they were the dominate clinical manifestation . This correlated with the significantly higher reported CSF WBC counts in patients from Tanzania . Unspecific signs of the disease such as fever , headache , general body pain and joint pains were reported in similar proportions in both study populations . We observed fever ( ≥37 . 5 ) in 29 . 7% ( 41/138 ) of the trial subjects . In the literature , fever was reported in the range of 31–71% in second stage patients from Zambia [18] , [29] , [35] . In the two study populations we saw high fever ( >38 . 5 ) on admission only in Uganda ( 5 . 8% , 4/69 ) whereof 50% were children . Fever seems to be more common in T . b . rhodesiense than T . b . gambiense second stage patients in which fever was only occasionally reported ( 16% ) and high fever was mostly seen in children [25] . In the two study populations , oedema was reported in Uganda and Tanzania in 20 . 3% and in 37 . 7% of the patients , respectively ( p = 0 . 0244 ) . This was comparable to the reported range of oedema in the literature ( 21 . 7–43 . 3% ) [18] , [29] , [35] , [36] . The clinical aspects of T . b . gambiense HAT [21] , [25] , [26] , [37] have been systematically studied and show that the hallmark of second stage disease are neurological signs and symptoms [21] , [25] . Unfortunately , this has never been done for T . b . rhodesiense HAT and hampers comparisons . However , published data report sleeping disorders during daytime hours with 63 . 3–70 . 5% of patients being affected [29] , [35] . We observed sleeping disorders during daytime hours in Uganda and Tanzania in 56 . 5% and in 95 . 7% of the patients , respectively ( p<0 . 0001 ) . Similarly , sleeping disorders at night time are reported in the literature in 28 . 3% of patients [29] . We observed it in 34 . 8% of the patients from Uganda and in 92 . 8% of the patients from Tanzania ( p<0 . 0001 ) . Also other neurological signs and symptoms were significantly more frequent in patients from Tanzania; tremor ( p = 0 . 0001 ) , abnormal movements ( p<0 . 0001 ) , inactivity ( p = 0 . 0076 ) and aggressiveness ( p<0 . 0001 ) . Clearly , the neurological signs and symptoms are more pronounced in Tanzania than in Uganda , and when compared to the literature . In Uganda , almost 50% of patients were in a poor nutritional status ( 48% had BMI<16 . 5 ) as food security is very poor in this part of the country . This most likely contributes to weakness and , therefore , walking difficulties in the absence of neurological symptoms . Malnutrition is associated with immunodeficiency and higher susceptibility for a wide range of infections such as tuberculosis [38] , [39] and pneumonia [40] , as well as a poorer response to treatment . Another potential consequence of malnutrition in Uganda is an increased number of patients admitted with severe coma indicating a more rapid progression of the disease . Yet , we assume that many HAT cases from T . b . rhodesiense endemic areas in Tanzania die without ever having had contact with the health system due to geographical isolation . With regards to treatment outcomes , we did not see any differences in the two study populations . In both countries all patients were free of parasites at end of treatment . Also , there was no apparent difference in parasite clearance rates . Time- and treatment-dependant dynamics of CSF WBC counts in the two study populations will be published separately . Cardiovascular involvement is typical , but rarely of clinical relevance in T . b . gambiense HAT [41] , [42] . We have limited knowledge of the effects of cardiac involvement in T . b . rhodesiense patients , but there is evidence that perimyocarditis seems to play an important role in the clinical course and fatal outcomes [43] , [44] . We observed symptoms of cardiac failure such as oedema ( swelling of legs ) in 29% of the patients . Hepatomegaly occurred in 18% , dyspnoea in 7% and cough in 20% of the patients . However , echocardiography or laboratory testing ( i . e . brain natrium peptide ) could not be performed to confirm heart failure . Co-infections with malaria and HIV were studied in detail in the patient population from Tanzania as the majority of the patients were malaria-positive on admission ( 80% ) and agreed to voluntary testing of their HIV status ( 94 . 2% ) . Patients that were malaria-positive on admission more often had pruritus ( p = 0 . 025 ) , sleeping disorders during day time hours ( p = 0 . 026 ) and disturbed appetite ( p = 0 . 01 ) . Also , they exhibited strange behaviour more often ( p = 0 . 001 ) . However , there is insufficient evidence for profound differences in malaria-positive and malaria-negative subjects , possibly due to asymptomatic carriers . We identified one study that looked at T . b . rhodesiense and HIV co-infections in 25 patients from Kenya . In terms of treatment outcomes no conclusive results were obtained [45] . Our results indicate that the HIV status of the patient does not change the clinical presentation and/or the treatment outcomes of T . b . rhodesiense HAT . For T . b . gambiense HAT , there seems to be no association between HIV and HAT infection rates [46] , [47] but evidence exists for a negative association with treatment outcomes [47] , [48] . More research efforts are needed to better understand the complex interactions of co- infections , especially for neglected tropical diseases [49] . Our findings on the different clinical presentation of T . b . rhodesiense HAT in the two study populations could be due to an observation bias , bias in patient selection , or in comparing patients at incongruous time points after infection . Bias due to co-infections or differences in host and/or parasite genetics is also possible . An observation bias can not be ruled out but is however less likely as the Impamel III program was conducted with a structured case report form ( CRF ) and one monitoring person . We have seen variability in signs and symptoms with clear definitions ( e . g . lymphadenopathy , abnormal movements or tremor ) as well as subjective definitions ( e . g . insomnia , headache or inactivity ) . We can not completely rule out a selection bias due to the exclusion of moribund and unconscious patients in which baseline examination per protocol was not possible . However , the number of excluded patients was relatively small ( <10% ) and the two study populations were similar in regards to self-reported duration of illness . Even though unsuccessful , active case searches were conducted in both countries which reduced a potential selection bias . Central nervous system involvement in T . b . rhodesiense HAT was previously reported within 3 weeks to 2 months of infection [12] . One third of the study population already had clear neurological signs and symptoms within one month of infection which reflects the acuteness of T . b . rhodesiense infections . The WBC count in the CSF as well as most of the clinical signs and symptoms also developed quickly and did not significantly change over time . Disease progression was noticeable by aggravation of tremor , walking difficulties , sleeping disorders at night time , disturbance of appetite and aggressiveness over time , in both study populations . Based on the results shown we rule out a bias of our findings due to co-infections . Previous infections with trypanosomes and/or host genetics might be determinants for the different clinical presentation of the disease in Tanzania and Uganda . There are speculations that apathogenic forms of the disease could influence immune responses to pathogenic infections [50] , [51] supported by the fact that HAT is more acute in white than in the black populations [52] , [53] . But we also see a high variability in disease severity among African populations [17] , [18] , a fact that has been related to the descent of people: people of Nilotic descent , who migrated into the East African region from tsetse-free areas during the past 2 , 000 years may have less tolerance than people of Bantu descent , whose ancestors have been exposed to human trypanosomes for several thousand years [18] . Our findings do not align with this theory as in Tanzania , the majority of the population is of Bantu origin and in Uganda the majority of the population is of Nilotic origin . Different parasite genotypes could be responsible for the observed spectrum of disease severity , a hypothesis has already been raised 60 years ago [16] , [17] , [54] . Recent findings on the phylogenetic relationship between different T . b . rhodesiense strains showed that the high variability of the T . b . rhodesiense genome is attributed to multiple and independent evolutions from T . b . brucei [55] . Our data show a clear difference in the clinical presentation of T . b . rhodesiense HAT in Tanzania and Uganda but a detailed assessment of host and parasite genotypes was beyond the scope of this paper . T . b . rhodesiense HAT is a highly neglected disease and tools for disease control are very limited . There are no sensitive diagnostics at hand and melarsoprol , the only available drug to treat second stage disease , is toxic . An early identification of the disease is vital to prevent late onset of treatment . However , most of the patients are first treated for other conditions such as malaria and pneumonia . A low degree of disease awareness among health personnel is common and aggravated by the low prevalence and the focal distribution of HAT . A detailed understanding of the clinical presentation and regional characteristics of T . b . rhodesiense HAT is important and can support decision making and differential diagnosis at health facility level .
|
Sleeping sickness , or Human African Trypanosomiasis ( HAT ) , caused by Trypanosoma brucei rhodesiense is one of the most neglected tropical diseases . It affects mainly rural , poor East African populations and has very high socio-economic impacts . T . b . rhodesiense HAT is an acute disease; patients quickly progress from the first stage , where trypanosomes are detectable in blood and lymph , to the second stage , where parasites penetrate the central nervous system . If left untreated , T . b . rhodesiense HAT is fatal . Disease control is hampered by the absence of sensitive diagnostic tools and safe drugs . Second stage patients can only be treated with melarsoprol , a highly toxic , arsenical drug . It is more difficult to treat patients successfully at advanced stages of the disease , and late onset of treatment should be avoided . Yet , most patients are treated for other conditions prior to HAT diagnosis . Therefore , it is important that health personnel in T . b . rhodesiense endemic regions have a detailed understanding of the clinical presentation of the disease and consider regional characteristics of T . b . rhodesiense HAT for decision making and differential diagnosis .
|
[
"Abstract",
"Introduction",
"Materials",
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"Methods",
"Results",
"Discussion"
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"infectious",
"diseases/neglected",
"tropical",
"diseases",
"virology/immunodeficiency",
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2011
|
Clinical Presentation of T.b. rhodesiense Sleeping Sickness in Second Stage Patients from Tanzania and Uganda
|
Dengue includes a broad range of symptoms , ranging from fever to hemorrhagic fever and may occasionally have alternative clinical presentations . Many possible viral genetic determinants of the intrinsic virulence of dengue virus ( DENV ) in the host have been identified , but no conclusive evidence of a correlation between viral genotype and virus transmissibility and pathogenicity has been obtained . We used reverse genetics techniques to engineer DENV-1 viruses with subsets of mutations found in two different neuroadapted derivatives . The mutations were inserted into an infectious clone of DENV-1 not adapted to mice . The replication and viral production capacity of the recombinant viruses were assessed in vitro and in vivo . The results demonstrated that paired mutations in the envelope protein ( E ) and in the helicase domain of the NS3 ( NS3hel ) protein had a synergistic effect enhancing viral fitness in human and mosquito derived cell lines . E mutations alone generated no detectable virulence in the mouse model; however , the combination of these mutations with NS3hel mutations , which were mildly virulent on their own , resulted in a highly neurovirulent phenotype . The generation of recombinant viruses carrying specific E and NS3hel proteins mutations increased viral fitness both in vitro and in vivo by increasing RNA synthesis and viral load ( these changes being positively correlated with central nervous system damage ) , the strength of the immune response and animal mortality . The introduction of only pairs of amino acid substitutions into the genome of a non-mouse adapted DENV-1 strain was sufficient to alter viral fitness substantially . Given current limitations to our understanding of the molecular basis of dengue neuropathogenesis , these results could contribute to the development of attenuated strains for use in vaccinations and provide insights into virus/host interactions and new information about the mechanisms of basic dengue biology .
Dengue virus ( DENV ) is an arthropod-borne flavivirus that belongs to the family Flaviviridae . The DENV genome is a 11 kb single-stranded RNA molecule of positive polarity that encodes a single open read frame ( ORF ) , which is flanked by two untranslated regions ( 5′ and 3′UTR ) [1]–[2] , which are involved in viral RNA replication and translation [3]–[6] . ORF translation generates a single polyprotein that is cleaved by host and virus-derived proteases to produce three structural ( C , prM and E ) and seven non-structural proteins ( NS1 , NS2A , NS2B , NS3 , NS4A , NS4B and NS5 ) [1] . The four serotypes of DENV ( DENV-1 to DENV-4 ) are transmitted to humans by the mosquito vector Aedes aegypti . Dengue disease is endemic to subtropical and tropical countries , and the World Health Organization ( WHO ) estimates that 50 to 100 million individuals become infected annually . DENV infection results in a spectrum of illnesses , ranging from a flu-like disease ( dengue fever , DF ) to more severe and potentially fatal , dengue hemorrhagic fever ( DHF ) and dengue shock syndrome ( DSS ) [7]–[8] . Epidemics with high frequencies of DHF/DSS are spreading throughout South America and unusual clinical presentations such as encephalitis , hepatitis and other visceral signs are becoming more frequent [9]–[12] . There are currently no vaccines or specific licensed antiviral drugs for prevention or treatment of dengue [13]–[15] . Despite major advances in DENV biology , many aspects of dengue pathogenesis remain largely unknown . Animal models reproducing some of the salient features of dengue disease have been used to investigate the underlying pathogenesis mechanisms . Multiple lines of evidence indicate that immunopathological mechanisms play an important role in the development of DHF/DSS [7] , [16] . The prevalence of DHF is higher in patients experiencing secondary infection with a heterotypic dengue virus serotype , leading to the suggestion that severe disease may result from antibody dependent enhancement ( ADE ) [17]–[21] . However , severe disease is often observed after primary infections , indicating a role for individual strains of DENV , in addition to host factors related to previous infection in the development of severe dengue disease [22]–[25] . Disease severity is thus probably determined by the interplay of viral and host factors . Several mouse models of dengue disease have been described , but even those that faithfully reproduce some features of human disease , present limitations because they are based on the use of mouse-adapted viruses or genetically modified animals . Nevertheless , these models have provided insights into DENV pathogenesis . Many studies have shown that mutations affecting the E protein , which covers the flavivirus surface , can alter flavivirus virulence . The E protein , a glycosylated dimeric membrane protein [26] , interacts with receptors on the host cell surface [27]–[28] , mediating virus binding and fusion to the host cell membrane [29]–[31] and conferring protective immune responses by eliciting antibody production [32]–[33] . Prestwood and coworkers [34] described a DENV-2 isolate that had been obtained by passing a clinical isolate in mosquitoes and mice , and that caused severe disease in AG129 mice . By reverse genetic techniques , they identified two mutations affecting the E protein ( E124 and E128 ) as responsible for an increase in virulence . The recombinant virus had a low affinity for heparin sulfate , reducing its binding to cells and increasing its half-life in the serum . This would potentially allow a larger number of viral particles to infect the visceral tissues thereby increasing disease severity in this mouse model . NS3 protein is one of the most highly conserved proteins in flaviviruses . This multifunctional protein has at least three different activities [35] . It has a serine protease domain that catalyzes the cleavage of several viral proteins , an RNA helicase domain , and an RNA triphosphatase domain , which promotes dephosphorylation of the 5′UTR region during capping activities [36]–[46] . In the course of human dengue infection , NS3 is a common target of T cells [47] . The helicase domain of NS3 ( NS3hel ) , together with NS5 , an RNA-dependent RNA polymerase , participates in viral RNA replication and it is essential for genome propagation . It has been demonstrated that the interaction between DENV NS3hel and NS4B triggers the dissociation of the helicase from single-stranded RNA thereby modulating viral replication . The enzymatic activities and role of NS3 proteins in viral replication and polyprotein processing have been studied for several members of the Flaviviridae family [48]–[50] , but only a few studies have identified point mutations in NS3 modulating viral pathogenesis . We previously described neurovirulent variants of DENV-1 that were generated by adapting viruses to cause lethal neurological disease in mice [51]–[52] . Comparisons of the sequences of parental and mouse-adapted strains identified mutations affecting positions 402 and 405 of E protein , and in the helicase domain of the non-structural protein NS3 ( positions 209 , 435 and 480 ) , as potentially responsible for this neurovirulent phenotype [52]–[53] . We evaluated the viral molecular determinants putatively identified as contributing to DENV pathogenesis in a mouse model , by introducing each mutation , individually or in combination , into a non-neurovirulent infectious cDNA clone of DENV-1 and recovering genetically defined DENV-1 strains which were then used to determine the effect of these mutations in vitro and in vivo . These results build on previous demonstrations that multiple mutations in different regions of the genomes of dengue and other flaviviruses cooperate in the modulation of pathogenesis [54]–[56] .
Animal experiments were approved by the ethics committee for animal experimentation of the Federal University of Parana ( CEP/UFPR 23075-0429663/2007-97 ) . The procedures using animals in this research project are specified in accordance with the ethical principles established by the Brazilian College of Animal Experimentation ( COBEA ) and requirements established in “Guide for the Care and Use of Experimental Animals ( Canadian Council on Animal Care ) ” . Aedes albopictus cells ( C6/36 ) were grown at 28°C in Leibovitz L-15 medium ( Gibco/Invitrogen , Grand Island , NY , USA ) supplemented with 0 . 26% Tryptose ( Sigma-Aldrich , St . Louis , MO , USA ) , 25 µg/mL gentamicin ( Gibco/Invitrogen , Grand Island , NY , USA ) and 5% fetal bovine serum ( FBS ) ( Gibco/Invitrogen , Grand Island , NY , USA ) . Human hepatoma cells ( Huh7 . 5 ) were grown in 37°C , under an atmosphere containing 5% CO2 , in Dulbecco's Modified Eagle Medium: Nutrient Mixture F-12 ( DMEM/F12 ) ( Gibco/Invitrogen , Grand Island , NY , USA ) supplemented with 25 µg/mL gentamicin and 10% FBS . Neuroblastoma cells ( Neuro-2a ) were grown in 37°C , under an atmosphere containing 5% CO2 , in Dulbecco's Modified Eagle Medium ( DMEM ) ( Gibco/Invitrogen , Grand Island , NY , USA ) supplemented with 1x non essential amino acids ( Gibco/Invitrogen , Grand Island , NY , USA ) , 25 µg/mL gentamicin and 5% FBS . All clones were constructed using the backbone of the infectious genome-encoding plasmid pBACDV1 [57] ( a bacterial artificial chromosome plasmid – pBAC ) . The pBACDV1 consists of the full-length cDNA of strain BR/90 ( differing from the sequence deposited in GenBank ( AF226685 . 2 ) by only 11 nucleotides , and none of which results in an amino-acid substitution ) , a T7 RNA polymerase promoter sequence with a single-non-genomic G residue introduced immediately upstream from the first nucleotide of the 5′UTR ( to ensure high levels of synthetic transcript production ) , and a hepatitis delta virus ribozyme sequence ( HDV-RZ ) followed by a unique restriction endonuclease site just after the last nucleotide of the 3′UTR , ( to facilitate the production of templates for RNA synthesis ) [57] . To construct the recombinant cDNA clones containing the mutations identified in the neurovirulent DENV-1 strains , overlapping polymerase chain reaction ( PCR ) amplifications to generate cDNA molecules containing specific mutations , except for the NS3435 mutation , which was located very close to a naturally occurring restriction endonuclease site , making it possible to incorporate this mutation into the DENV-1 cDNA through the use of a single mutated oligonucleotide . All amplifications were carried out with the high fidelity enzymes of the TripleMaster System ( Eppendorf , Westbury , NY , USA ) or LongRange PCR ( Qiagen , Valencia , CA , USA ) , following the manufacturer's protocols . In some cases , the fragments containing the desired mutations were initially inserted into the pGEM-T Easy Vector System ( Promega , Madison , WI , USA ) , in accordance with manufacturer's instructions . The desired infectious cDNAs were reconstructed by using the corresponding fragments obtained either directly from the PCR amplicon , or from the pGEM-T clone to replace the parental fragments in the DENV-1 infectious genome in pBACDV1 . The fragments replaced for each mutation were: a NotI/MluI fragment for the E mutations ( E402 and E405 ) , a BsiWI/RsrII fragment for the NS3435 mutation , a MluI/BsiWI fragment for the NS3209 mutation , and a BsiWI/NheI fragment for the NS3480 mutation ( Figure 1 ) . The clones with individual mutations were named: pBAC-E402 , pBAC-E405 , pBAC-NS3209 , pBAC-NS3435 and pBAC-NS3480 , respectively . Finally , for the construction of the double and triple mutants , we combined the E-mutation with the NS3-mutation found in two independent neuroadapted strains ( Table 1 ) , generating the clones pBAC-E405/NS3435 , pBAC-E402/NS3209 , pBAC-E402/NS3480 and pBAC-E402/NS3209/NS3480 . Each construct was confirmed by sub mitting the replaced fragment for sequencing , at the Macrogen Sequencing Service ( Seoul , South Korea ) . Infectious DENV RNAs were generated by linearizing the recombinant pBAC DNAs in an overnight digestion at 25°C with SwaI ( New England Biolabs , Ipswich , MA , USA ) , purifying the products by phenol extraction and ethanol precipitation and transcribing them in vitro with T7 RNA polymerase in the presence of an 7 mG ( ppp ) G RNA cap analog ( Biolabs , Ipswich , MA , USA ) with the T7 MEGAScript Transcription System ( Ambion , Austin , TX , USA ) . Eight individual wells of C6/36 cells cultured at 28°C were transfected with RNA transcripts in the presence of Lipofectin ( Invitrogen , Carlsbad , CA , USA ) . Supernatant samples were harvested in duplicate at 48 , 72 , 96 and 120 hours after transfection , and used for viral titration . The time points with the highest titers were used for subsequent viral amplification . Viral titers were determined by the focus-forming unit technique in C6/36 cells ( ffuC6/36 ) , as previously described [58] . Foci were immunostained with purified supernatants of the Flavivirus group-specific mouse monoclonal antibody 4G2 , and the bound antibodies were then decorated with goat anti-mouse immunoglobulin conjugated to alkaline phosphatase ( Promega , Madison , WI , USA ) , which was detected by adding a solution of NBT ( nitro-blue tetrazolium chloride ) and BCIP ( 5-bromo-4-chloro-3′-indolyphosphate p-toluidine salt ) ( Promega , Madison , WI , USA ) as a substrate . To increase viral titers and generate working stocks , two rounds of infection were performed with each of the recovered virus , using the time point with highest titer in RNA transfection experiments in vitro . The first round of amplification was performed in T25 flasks ( TPP , Trasadingen , Switzerland ) of C6/36 cells ( 5×105 cells/flask ) at a multiplicity of infection ( MOI ) of 0 . 01 . The cell cultures were incubated at 28°C until cytopathogenic effects were observed or , in some cases , infection was confirmed by routine indirect immunofluorescence assays , six days after infection ( data not shown ) . Virus yields for each sample were determined by titration , as described above . The second round of amplification was performed in T300 flasks ( TPP , Trasadingen , Switzerland ) ( 2×107 C6/36 cells/flask ) , under the same conditions as described above . Recombinant viruses were purified from the products of this second amplification by centrifugation on a sucrose gradient , as previously described [59] . A mock-infected control preparation was prepared from non-infected C6/36 cells by the same protocol . Viral RNA was purified from sucrose gradient stocks , using the QIAamp Viral RNA Mini Kit ( Qiagen , Valencia , CA , USA ) . The resulting RNA was reverse transcribed with the Improm-II Reverse Transcriptase ( Promega , Madison , WI , USA ) in the presence of random primers ( 100 pmol/µL – Invitrogen , Carlsbad , CA , USA ) and the entire genome was amplified by PCR for nucleotide sequencing , which was carried out by the Macrogen Sequencing Service ( Seoul , South Korea ) . Huh7 . 5 ( 4×105 cells/well ) and C6/36 ( 2×105 cells/well ) cells were infected in 24-multiwell plates ( TPP , Trasadingen , Switzerland ) with mock and recombinant viruses vBACDV1 , vBAC-E402 , vBAC-E405 , vBAC-NS3209 , vBAC-NS3435 , vBAC-NS3480 , vBAC-E405/NS3435 , vBAC-E402/NS3209 , vBAC-E402/NS3480 and vBAC-E402/NS3209/NS3480 . A MOI of 5 was used to infect Huh7 . 5 cells by incubation for 1 h at 37°C under an atmosphere containing 5% CO2 , and a MOI of 1 was used to infect C6/36 cells by incubation for 1 h at 28°C . Cells were recovered at 24 , 48 , and 72 hours post infection ( hpi ) . The number of cells infected was determined by flow cytometry , according to previously described protocols [61] . Cells were analyzed with a FACS Canto II system ( Becton & Dickinson , San Jose , CA ) . FACS data were analyzed with FlowJo 2 . 2 . 8 software . To determine the binding affinity of the recombinant viruses for Neuro-2a cells , Amicon ( Millipore , Billerica , MA , USA ) concentrated recombinant vBACDV1 , vBAC-E402 , vBAC-E405 viruses and a mock-infected control were incubated with 2×105 Neuro-2a cells at MOI of 100 for 1 h at 4C° . The cells were then washed three times with ice-cold PBS to remove unbound virus . They were lysed and viral RNA was extracted with the QIAamp Viral RNA mini Kit ( Qiagen , Valencia , CA , USA ) , according to the manufacturer's protocols . The number of bound genome-containing particles per cell was then determined by RT/qPCR in three independent experiments , as previously described [60] . The murine gene encoding GAPDH was also included as a housekeeping gene in all analysis , for data normalization [62] . A 50% lethal dose ( LD50 ) assay was performed with virus recovered from the pBACDV1 clone ( vBACDV1 ) , to determine the optimum dose of recombinant viruses for the inoculation of mice . Individual litters of two-day-old Swiss mice were inoculated via intracerebral ( i . c . ) route with four ten-fold dilutions ( corresponding to 100 , 000 ffuC6/36 to 100 ffuC6/36 ) of purified vBACDV1 virus or one dilution of purified mock-infected C6/36 culture fluid ( equivalent to the highest tested concentration of vBACDV1 ) . Animals were monitored for 21 days . We found that the LD50 was equivalent to 56 , 234 ffuC6/36 of vBACDV1 . For comparative studies , 562 ffuC6/36 ( corresponding to 10−2 LD50 ) aliquots of each of the recombinant viruses were compared side-by-side through the i . c . inoculation of three individual litters of two-day-old mice , replicating the methods originally described for DENV-1 neurovirulence in Swiss mice [51] . The animals were observed for 21 days to evaluate the morbidity and mortality . Eight days post infection ( dpi ) , three animals were randomly selected from each litter , euthanized and their brains were harvested and pooled for the quantification of virus replication and gene induction . Ten dpi , one animal per group was euthanized , its brain was collected and fixed in a 10% buffered formalin solution for histological analysis . In addition , mouse brain and spine cord tissues were individually collected at 6 , 8 and 10 dpi of animals infected with mock , vBACDV1 and vBAC-E402/NS3209/NS3480 for RT/qPCR and virus titration analysis . Total RNA was isolated from 30 mg of pooled 8 dpi mouse brain tissues infected with each DENV or the mock , with the RNEasy Mini kit ( Qiagen , Valencia , CA , USA ) , according to the manufacturer's protocol . For the quantification of viral RNA in the brain tissues by RT/qPCR , we subjected 2 µg of each RNA sample to amplification with 400 nM specific DENV-1 oligonucleotides and 300 nM specific DENV-1 probe , with the MultiScribe Enzyme Plus RNase Inhibitor and TaqMan Universal RT-PCR Master Mix ( Applied Biosystems , Foster City , IA , USA ) in an ABI PRISM 7500 Detection System ( Applied Biosystems , Foster City , IA , USA ) as previously described [60] . The mouse GAPDH housekeeping gene was included in all analysis for data normalization as previously described [52] . The RNA isolated from DENV- and mock-infected mouse brain tissues ( pooled from three individuals from each group , as described above ) was used for the quantification of mRNA levels for seven genes ( Irf1 , Psmb8 , Usp18 , C1r , IFNα , IFNβ and CCL5 ) selected on the basis of a previous study by Bordignon and coworkers [63] . For this purpose , 4 µg of each RNA sample were reverse transcribed with ImProm-II Reverse Transcriptase ( Promega , Madison , WI , USA ) and oligo-dT primers ( 10 µM ) according to the manufacturer's protocol . The resulting cDNAs were then diluted to a concentration of 2 ng/µl and used for amplification by qPCR , as previously described [63] . Melting curves were used to check product specificity . Levels of mRNA for each selected gene were recorded as gene mRNA/murGAPDH mRNA induced by dengue virus infection in the central nervous system ( CNS ) of mice . The qPCR data are reported as means ± standard deviation ( SD ) and were analyzed by one-way ANOVA with Bonferroni's or Dunn's correction for multiple comparisons . In vitro growth kinetics data are reported as means ± standard deviation ( SD ) and were analyzed using two-way ANOVA followed by a Bonferroni's test . The level of significance for the analyses was set at p≤0 . 05 . Mortality data were analyzed by plotting Kaplan-Meier survival curves and carrying out Log-rank ( Mantel-Cox ) multiple comparison test . The analyses were performed with GraphPad Software ( Prism 5 for Mac OS X – version 5 . 0c , San Diego , CA , USA ) .
For identification of putative viral determinants on the phenotype of neuroadapted DENV-1 strains , we constructed a panel of DENV cDNA infectious clones containing a subset of mutations affecting the E and NS3 proteins selected in two separate studies of the neuroadaptation of the FGA/89 strain to newborn mice [51]–[52] . The mutations were introduced into a DENV-1 infectious clone not adapted to mice ( pBACDV1 , derived from the DENV-1 prototype strain ( BR/90 ) – [57] ) . Comparisons of the sequences of the infectious clone , the neuroadapted isolates and the parental strain used to generate the neuroadapted strains ( FGA/89 ) ( Table S1 ) , led us to focus on mutations at positions 402 and 405 in E and 209 , 435 , and 480 in NS3 for the studies described here ( Table 1 ) . The mutations affecting E ( E402 and E405 ) acquired during adaptation were found to be located outside the parts of the protein used for structural determinations by X-ray crystallography . Both these mutations lie within the first of two predicted α-helical structures H1pred in the stem region of E just after the ectodomain [64] ( Figure 2 ) . This stem region seems to be involved in the formation of the E homotrimer , the interactions between E and prM , particle formation and intracellular retention [64]–[68] . The NS3 mutations acquired during neuroadaptation are located in the helicase domain , with the NS3209 mutation in subdomain I , and mutations NS3435 and NS3480 in subdomain II [45] ( Figure 3 ) . The helicase domain of the NS3 protein appears to be responsible for supporting the initiation of ( − ) ssRNA synthesis , through the unfolding of RNA secondary structures , providing access to the replication machinery [36] , [69]–[70] . We investigated the effect of these mutations both individually and in combination on the in vitro and in vivo properties of DENV-1 , by using infectious cDNAs harboring the mutations ( Figure 1 and Table 1 ) as a source for in vitro RNA synthesis . The RNAs generated were then introduced into C6/36 cells for the recovery of viruses , which were amplified and purified as described in the Methods section . Analyses of the complete sequences of the genomes of all of the amplified viruses confirmed their identity with the pBACs used to generate them and showed that no adventitious mutations had been produced in the cloning steps or arisen during virus recovery and propagation . To evaluate the role of each mutation in the neurovirulent phenotype in a mouse model , purified recombinant viruses were inoculated i . c . in newborn Swiss mice . Three litters of mice , each containing 5 to 11 animals , were used . All inoculations were performed with a single dose of virus ( 562 ffuC6/36 ) , corresponding to 1/100 LD50 for the parental cDNA clone-derived virus , vBACDV1 ( see Methods , all viral genomes were resequenced before inoculation ) . The equivalent viral genomic RNA ( GE ) to FFU ratio ( 562 ffuC6/36 ) for each virus inocula was determined by RT/qPCR as previously described [61] to assure the comparability of viral infection doses ( data not shown ) . Figure 4 shows the combined mortality data for three experiments . The animals inoculated with mock , FGA/89 , vBACDV1 , vBAC-E402 , vBAC-E405 , vBAC-NS3209 and vBAC-E402/NS3209 viruses survived forthe entire 21-day observation period . Mice in the groups inoculated with FGA/89 , vBACDV1 , vBAC-E402 and vBAC-E402/NS3209 behave normally throughout the observation period , whereas animals from the groups infected with vBAC-E405 and vBAC-NS3209 displayed mild signs of disease ( Figure S1 ) . However , all the animals inoculated with vBAC-NS3435 , vBAC-NS3480 , vBAC-E405/NS3435 , vBAC-E402/NS3480 or vBAC-E402/NS3209/NS3480 displayed more severe signs of disease . Almost all of the animals in these groups displayed encephalitis and partial paralysis of the hind limbs ( Figure S1 ) . In the groups for which deaths were recorded , 29% of the animals inoculated with vBAC-NS3435 died and the mortality rate was even higher ( 61% ) for mice inoculated with vBAC-NS3480 . These results highlight the importance of the NS3435 and NS3480 mutations for the acquisition of the viral neurovirulent phenotype . Furthermore , mortality reached 73% in the group of animals inoculated with the double-mutant virus , vBAC-E405/NS3435 , and inoculation with vBAC-E402/NS3480 and vBAC-E402/NS3209/NS3480 viruses killed 100% of the animals ( Figure 4 ) . Thus , viruses containing the E402 and E405 mutations alone were no more virulent than vBACDV1 . However , when these mutations were combined with the NS3480 and NS3435 mutations respectively , the resulting viruses , each of which carried two of the mutations found in the neuroadapted derivatives ( FGA/NA d1d and FGA/NA P6; Table 1 ) , were neurovirulent . To assess the ability of the recombinant viruses ( vBACDV1 , vBAC-E402 and vBAC-E405 ) to interact with Neuro 2A cell receptors , binding assays were carried out ( Figure S2 ) . No significant difference in binding capacity was observed between these viruses . We previously showed that viral replication in the brains of mice inoculated with the FGA/89 and FGA/NA P6 strains of DENV-1 peaked nine days after inoculation [52] . To evaluate the replication properties of the recombinant viruses , brains of three animals were collected from each group on the eight day after inoculation , before the onset of signs of disease and death . RT-qPCR analyses and viral titration performed on the brain tissues of animals inoculated with the panel of viruses showed that vBAC-E405/NS3435 , vBAC-E402/NS3480 and vBAC-E402/NS3209/NS3480 produced the largest numbers of viral progeny and the highest levels of RNA synthesis ( Figure 5 ) in the brain tissues of infected animals , consistent with the high frequency of encephalitis in these animals later in the incubation period ( see Figure 4 ) . We investigated whether the neurovirulent phenotype resulted from an increase in viral fitness by carrying out in vitro growth kinetics studies on human and insect derived cells and quantifying protein synthesis . Levels of protein synthesis were significantly higher in Huh7 . 5 and C6/36 cells infected with vBAC-E405/NS3435 , vBAC-E402/NS3480 and vBAC-E402/NS3209/NS3480 than in cells infected with vBACDV1 ( Figure 6 ) . Results from a previous study ( [63] and unpublished results] ) revealed that a number of innate immune response genes were differentially expressed in the brains of mice infected with avirulent and neurovirulent strains of DENV-1 . Therefore , to analyze the influence of individual mutations on the ability of recombinant viruses to induce innate immunity genes , a subset of genes representing several major pathways [interferon signaling ( Irf1 - interferon regulatory factor 1 ) , interferon alpha and beta , antigen presentation ( Psmb8 - proteosome subunit beta type 8 ) , protein ubiquitination pathway ( Usp18 - ubiquitin specific protease 18 ) , complement system ( C1r - component 1 , r subcomponent ) and chemokine ( CCL5 - chemokine ligand 5-C-C motif ) ] were selected for analyses . RNAs extracted from brain tissues obtained 8 days after infection , were subjected to amplification with specific primers for these genes , and the RT-qPCR signals obtained were normalized with respect to the signal for murGAPDH ( Figure 7 ) . Consistent with the virulence ( Figure 4 ) and viral load studies ( Figure 5 ) , levels of expression for all of the host genes shown in Figure 7 were significantly higher in animals infected with FGA/NA d1d , FGA/NA P6 ( data not shown ) or any of the recombinant viruses containing double and triple mutations ( vBAC-E402/NS3480 , vBAC-E405/NS3435 and vBAC-E402/NS3209/NS3480 ) than in mock-infected or vBACDV1-infected animals . To discard an eventual mouse to mouse variation due to the outbred nature of the mice used in this study , and confirm the role of the critical residues responsible for increased viral load and pathogenesis , single animals were euthanized at various time points during infection ( 6 , 8 and 10 dpi ) and individual mouse CNS and spinal cord tissues were analyzed . Viral RNA synthesis , viral load curves and modulation of innate immune response genes were correlated with disease and death of the animals infected with vBAC-E402/NS3209/NS3480 compared to mock-infected or vBACDV1-infected animals ( Figure S3 ) . We also evaluate the target cells and the damage caused by virus infection in the CNS of these mice , by carrying out histological analyses of brain tissues . Brain tissue collected ( 10th dpi ) , from animals infected with neuroadapted ( FGA/NA d1d and FGA/NA P6 ) or recombinant viruses , displayed moderate to severe meningitis . The degree of tissue injury observed ( data not shown ) was consistent with viral RNA replication , viral load ( Figure 5 ) and the severity of infection as determined by mortality rate ( Figure 4 ) .
Several studies have provided support for the hypothesis that viral virulence determinants play a role in dengue pathogenesis and vector transmissibility [71]–[73] . In this study we focused on determining how point mutations , acquired during the adaptation of DENV to mice increase viral fitness in vitro and in vivo , and exert their effects on mice neuropathogenesis . We used reverse genetics techniques to sample individual mutations found in two independently obtained newborn mouse-adapted isolates of DENV-1 [51]–[52] . Comparisons of the genomes of the parental ( FGA/89 ) and neuroadapted variants of DENV-1 ( FGA/NA d1d and FGA/NA P6 ) suggested that acquired mutations in the genes encoding E and NS3 might be responsible for the neurovirulence of these mouse-adapted strains . To test the role of these mutations in viral fitness and virulence , we created a panel of non-mouse adapted infectious clone-derived viruses with the E mutations ( E402 Phe to Leu and E405 Thr to Ile ) and NS3 mutations ( NS3209 Val to Ile , NS3435 Leu to Ser and NS3480 Leu to Ser ) present separately , in paired or in group of three mutations , as in the empirically adapted isolates . Both of the E mutations studied mapped to the region outside the ectodomain and the three NS3 mutations studied here are located in the helicase region of NS3 . The positions of the mutations detected in the neuroadapted isolates ( E402 and E405 – Figure 2 ) were not consistent with a change in affinity for the receptor . Indeed , the recombinant viruses carrying these mutations had the same binding affinity for Neuro2A cells as the infectious clone-derived virus . We therefore conclude that the mechanism by which E protein mutations increases virulence involves critical steps occurring after viral attachment ( fusion/assembly/release ) . Chen and coworkers [74] reported similar results concerning the effect of mutations affecting this domain of E protein on neurovirulence in mice . They used chimeric DENV-4 carrying the C-prM-E genes of DENV-3 to show that a mutation at E406 ( substitution of a Lys for the WT Glu ) increased the neurovirulence of a DENV-4/DENV-3 chimera . Lin and coworkers [68] , using site-directed mutagenesis and functional assays , demonstrate the involvement of the EH1 and EH2 domains of the E protein in DENV assembly and cell entry . Substitutions at positions E401 ( Met to Pro ) , E405 ( Thr to Pro ) , E408 ( Gly to Pro ) and E412 ( Met to Pro ) in the EH1 domain affected the assembly of DENV VLPs , probably due to interference with prM-E heterodimerization . The authors hypothesized that mutations mapping to the N-terminal EH1 domain affected the association of the stem region with the viral membrane altering curving and bending during the assembly in the ER . The NS3209 , mutation , which was co-selected with the NS3480 in FGA/NA P6 , had no apparent effect on virulence in our studies . The triple mutant recombinant virus ( E402/NS3209/NS3480 ) gave higher viral RNA levels and virus titers in the mouse CNS 8 dpi than the double mutant ( E402/NS3480 ) , but both viruses killed 100% of the animals by days 15 and 16 post infection , respectively . The recombinant viruses harboring mutations at residues NS3435 and NS3480 , located in the helicase subdomain 2 , after motifs V and VI ( Figure 3 ) , respectively , displayed an alteration of replicative capacity ( in vitro and in vivo ) and were neurovirulent in mice . It has been reported that a substitution at position 249 ( Thr to Pro ) of the NS3hel in West Nile virus confers a highly virulent phenotype on strains usually only weakly virulent in American crows [75] . This region is involved in RNA binding and ATP hydrolysis and is required to drive the helicase along its nucleic acid substrate [76] . The presence of mutations in these regions may affect the activity of the helicase , increasing replication efficiency , through either a direct effect on helicase activity itself or through interaction with other viral or cellular proteins . Sampath and colleagues [46] carried out a structure-based mutational analysis and proposed an “inchworm” model of DENV NS3 translocation and unwinding activity . They suggested that the pocket next to DENV-2 NS3 Ile365 ( tip of domain II ) would acts as a “helix opener” disrupting hydrogen bonds at the fork . The basic concave face between domains II and III would acts as “the translocator” in this model , by binding dsRNA ahead of the fork . The NS3480 mutation maps to this concave face , the NS3435 mutation maps to domain III , and both may therefore enhance dsRNA binding and modulate helicase activity . Grant and coworkers [54] recently described a DENV-2 strain causing lethal infections in immunocompromised AG129 mice . One critical virulence determinant at the NS4B52 protein had been identified . By reverse genetics , these authors demonstrated that the replacement of a Leu residue by a Phe residue , at this position , converted a non-virulent strain into a strain causing 80% lethality and increased viremia independently of the host type I interferon response . Physical interaction between NS4B ( located in the ER lumen ) and NS3 ( located on the cytoplasmic face of the ER ) is unlikely , but the authors hypothesized that a transient interaction could occur before polyprotein processing , thereby modulating DENV replication and implicating NS3 in this process . They also demonstrated that the NS4B52 substitution enhances viral RNA synthesis in mammalian cells but not in C6/36 insect cells . The non-mouse adapted infectious clone-derived viruses with only the E mutations identified in this study ( E402 and E405 ) had no higher binding affinity to Neuro2A cells receptor ( s ) or higher levels of viral RNA synthesis , viral load ( in vitro and in vivo ) and neurovirulence in mice than vBACDV1 . However , the combination of these mutations with NS3hel mutations ( E405/NS3435 , E402/NS3480 and E402/NS3209/NS3480 ) , altered viral replicative capacity across other tissue ( spinal cord ) and cell types ( Huh7 . 5 and C6/36 cells ) and resulted in a highly neurovirulent phenotype in mice . The pathological outcome of an infection is determined by the balance between the host response to infection and the ability of the infectious agent to escape from this response and multiply in the host . As part of this dynamic interaction , the host responses to some infections , including DENV infections , may contribute to the pathophysiology of disease . We have shown that high levels of replication of genetically defined DENV result in the upregulation of genes induced by type I IFN ( IFN-α/β ) , consistent with previous data from non human primates [77] and primary cultures of human cells [78] . In a previous study , we investigated the effect of DENV-1 infection on the transcription profile of CNS of mice . The Ube2l6 gene , which encodes an ubiquitin conjugate enzyme , was found to be up regulated in animals infected with the FGA/89 and with a neuroadapted derived strain FGA/NA a5c , with fold changes of 2 . 59 and 4 . 73 , respectively , eight dpi ( [63] and unpublished results ) . In a recent study based on the use of a high-throughput two hybrid assay , a human cellular protein , with a similar function , UBE2l ( an ubiquitine conjugate enzyme ) , was found to interact with the DENV NS2B , NS4B and NS5 proteins , and siRNA targeting of this gene inhibited DENV replication [79] . As FGA/NA d1d and FGA/NA a5c differ by only three amino-acid substitutions in the E protein , we will investigate further the modulation of the Ube2l6 protein and its interaction with the replication complex during infection with the recombinant viruses generated in this study . Transcript levels for Usp18 , which functions as an ubiquitin cycle enzyme , were positively correlated with higher levels of replication in animals infected with the strains vBAC-E405/NS3435 , vBAC-E402/NS3480 and vBAC-E402/NS3209/NS3480 . We demonstrated here that single mutations in the DENV-1 E protein and NS3hel domain increase viral fitness , both in vitro ( human and mosquito-derived cells ) and in vivo , facilitating early virus emergence during mouse infection consistent with a major role in DENV pathogenesis . In a context of limited knowledge of the molecular basis of dengue pathogenesis , our results could contribute to the establishment of attenuation strains for vaccine development , and provide insights into virus/host interactions and new information about the mechanisms of dengue pathogenesis .
|
Dengue virus constitutes a significant public health problem in tropical regions of the world . Despite the high morbidity and mortality of this infection , no effective antiviral drugs or vaccines are available for the treatment or prevention of dengue infections . The profile of clinical signs associated with dengue infection has changed in recent years with an increase in the number of episodes displaying unusual signs . We use reverse genetics technology to engineer DENV-1 viruses with subsets of mutations previously identified in highly neurovirulent strains to provide insights into the molecular mechanisms underlying dengue neuropathogenesis . We found that single mutations affecting the E and NS3hel proteins , introduced in a different genetic context , had a synergistic effect increasing DENV replication capacity in human and mosquito derived cells in vitro . We also demonstrated correlations between the presence of these mutations and viral replication efficiency , viral loads , the induction of innate immune response genes and pathogenesis in a mouse model . These results should improve our understanding of the DENV-host cell interaction and contribute to the development of effective antiviral strategies .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"virulence",
"factors",
"and",
"mechanisms",
"virology",
"emerging",
"viral",
"diseases",
"biology",
"microbiology"
] |
2012
|
Synergistic Interactions between the NS3hel and E Proteins Contribute to the Virulence of Dengue Virus Type 1
|
Rickettsia conorii conorii is the etiological agent of Mediterranean spotted fever , which is transmitted by the brown dog tick , Rhipicephalus sanguineus . The relationship between the Rickettsia and its tick vector are still poorly understood one century after the first description of this disease . An entomological survey was organized in Algeria to collect ticks from the houses of patients with spotted fever signs . Colonies of R . conorii conorii-infected and non-infected ticks were established under laboratory conditions . Gimenez staining and electron microscopy on the ovaries of infected ticks indicated heavy rickettsial infection . The transovarial transmission of R . conorii conorii in naturally infected Rh . sanguineus ticks was 100% at eleven generations , and the filial infection rate was up to 99% according to molecular analyses . No differences in life cycle duration were observed between infected and non-infected ticks held at 25°C , but the average weight of engorged females and eggs was significantly lower in infected ticks than in non-infected ticks . The eggs , larvae and unfed nymphs of infected and non-infected ticks could not tolerate low ( 4°C ) or high ( 37°C ) temperatures or long starvation periods . R . conorii conorii-infected engorged nymphs that were exposed to a low or high temperature for one month experienced higher mortality when they were transferred to 25°C than non-infected ticks after similar exposure . High mortality was observed in infected adults that were maintained for one month at a low or high temperature after tick-feeding on rabbits . These preliminary results suggest that infected quiescent ticks may not survive the winter and may help explain the low prevalence of infected Rh . sanguineus in nature . Further investigations on the influence of extrinsic factors on diapaused R . conorii-infected and non-infected ticks are required .
Rickettsia conorii conorii is the etiological agent of Mediterranean spotted fever ( MSF ) , one of the oldest recognized vector-borne infectious diseases [1] . In the 1930s , the brown dog tick , Rhipicephalus sanguineus , was suspected to be the vector of MSF . Ticks were crushed and used to inoculate humans who consequently contracted MSF [1] , [2] . Crushed eggs , larvae , nymphs , unfed adults collected in the winter and adults obtained from infected Rh . sanguineus females were able to infect humans . These data suggested that transstadial transmission ( transfer of bacteria from stage to stage ) but also transovarial transmission ( TOT , the transfer of bacteria from adult female ticks to the subsequent generation of ticks via the eggs ) of R . conorii conorii occurs in ticks and consequently that Rh . sanguineus ( Figure 1 ) could act not only as a vector but also as a reservoir of R . conorii conorii [1] . Rh . sanguineus has become one of the most globally widespread ticks because of its specialized feeding and association with domestic dogs [3] . Although Rh . sanguineus rarely feeds on humans , particularly in temperate countries , it seems to have a greater human affinity in warmer temperatures [4] . This tick is highly adapted to warm climates but also thrives in dog kennels and human homes . It can be imported with dogs to the cooler regions and survive in peridomestic environments , provided that it encounters acceptable conditions . Thus , Rh . sanguineus has spread globally between 50°N and 35°S [3] . However , MSF due to R . conorii conorii is known to be endemic in North Africa and southern Europe . MSF has also been described in a few countries in sub-Saharan Africa , and a few cases have also been sporadically reported in northern and central Europe [5] , [6] , sometimes followed by the installation of a local focus of the disease [1] . In contrast , R . conorii conorii infection has never been described in the Americas [7] . Although Rh . sanguineus-R . conorii conorii relationships were a focus of interest of pioneering rickettsiologists , they are still poorly understood even one century later . Interestingly , it has been suggested that R . conorii conorii has a negative effect on the survival of its tick vector when Rh . sanguineus ticks are experimentally infected [8]–[12] . However , preliminary data have recently demonstrated that naturally infected colonies of Rh . sanguineus can be maintained in laboratory conditions over several generations [13] . Therefore , a significant population of infected ticks should exist in the wild . However , the prevalence in the wild of ticks infected by R . conorii is low ( usually less than 1% ) . For example , none of the 2 , 229 Rh . sanguineus ticks collected from Spain were positive for R . conorii [14] . Rarely , a high prevalence of infected ticks has been found in small foci . For example , when a spotted fever focus was investigated in France in May 2007 , 18% ( 24/133 ) of the Rh . sanguineus ticks collected from the walls of one house and from a garden were found to be infected with R . conorii conorii [4] . The vectorial capacity of ticks depends on several characteristics of tick biology , including longevity , host-seeking behavior and mobility , all of which are influenced by extrinsic factors , including climatic conditions [15] . Temperature is known to influence tick-microorganism relationships and consequently , the vectorial capacity of ticks . For example , the maintenance and multiplication of parasites ( e . g . , Theileria spp . and Babesia spp . ) in ticks has been shown to be influenced mostly by temperature [15] . Moreover , it has been shown that ambient temperatures in excess of 27°C are not permissive for the transmission of Borrelia burgdorferi , the agent of Lyme disease in Ixodes dammini ticks [16] . In 1972 , Injeyan et al . [17] inoculated guinea pigs with infected crushed Rh . sanguineus nymphs that were previously held at different temperatures . These ticks were experimentally infected with the so-called “R . conorii Simko isolate” isolated from Rh . simus collected from cattle in Ethiopia . The clinical reactions were most evident in the guinea pigs injected with nymphs held at 35°C , and the reactions were milder in those held at 5°C , 15°C , 20°C , or 25°C [17] . However , the relevant literature offers epidemiological analyses of the influence of climatic factors on tick-borne rickettsial diseases , rather that laboratory results [4] , [17] , [18] . For example , during the 1970s , an increase in the number of observed MSF cases was correlated with a decrease in the number of frost days during the preceding year in France [19]–[21] . The aim of this study was to assess some of the life cycle parameters of infected and non-infected Rh . sanguineus , the transstadial and transovarial transmission of R . conorii conorii and the influence of high and low temperature on the survival of Rh . sanguineus infected with R . conorii conorii .
To collect Rh . sanguineus ticks naturally infected by R . conorii conorii , an entomological survey was organized in Algeria . The houses of patients who had contracted MSF between July and August of 2006 were visited . The owners were interviewed about the presence of ticks on their dogs and in their house . When available , engorged females were removed from dogs . The ticks were transported to Marseille , France and stored in environmental incubators at 25°C and 80% relative humidity ( RH ) with a day/night photoperiod of 16∶8 ( L∶D ) h [9] . After the ticks laid eggs , DNA was extracted from each tick , and all samples were tested by PCR for the rickettsial gltA and rompA genes , as previously described [9] . For all PCR procedures , the negative controls consisted of distilled water or DNA extracted from non-infected ticks from laboratory colonies that were added to the PCR master mix . The amplified products were sequenced , analyzed by BLAST ( www . ncbi . nlm . nih . gov/blast/Blast . cgi ) , and compared to those in the GenBank database . A single specimen tested positive for both rickettsial genes , and the analyzed sequences indicated R . conorii conorii fragments ( result section ) . The larvae and all subsequent stages of the infected tick were placed on New Zealand white rabbits ( Oryctolagus cuniculus ) that were used as the host for the blood meal [13] . Ticks were placed in each of two cloth ear bags , which were secured with Elastoplast® to the ears of rabbit [9] . Unfed adults from the 2nd generation were used for definitive morphological identification by a researcher ( PP ) using standard taxonomic keys for adult ticks [22] . To confirm species identification , amplification of the mitochondrial 12S rRNA gene was achieved by conventional PCR [23] . Specimens ( larvae , nymphs and adults ) of the 3rd , 4th and 10th subsequent generations ( Figure 2 ) were tested by real-time ( RT ) -PCR in a Lightcycler ( Roche ) instrument for the presence of Rickettsia spp . DNA using primers and Taqman probes targeting a partial sequence of the citrate synthase gltA gene , as previously described [24] . Gimenez staining , as previously described , was used to highlight morphological structures compatible with R . conorii conorii in the salivary glands ( Figure 3A ) , ovaries ( Figure 4A ) and eggs ( Figure 5A ) of infected Rh . sanguineus [25] . Engorged , infected ticks were dissected under a binocular microscope . The ovaries were washed with PBS and fixed overnight in 2% glutaraldehyde in a 0 . 1 M cacodylate buffer . After being washed in a 0 . 1 M cacodylate buffer , the specimens were post-fixed in 1% osmium tetroxide in 0 . 1 M potassium ferricyanide for 1 h and dehydrated in an ascending series of ethanol concentrations ranging from 30% to 100% . After the absolute ethanol dehydration step , the dehydration was finished in propylene oxide . The samples were embedded in Epon 812 resin . Sections ( 70-nm thick ) were stained with 5% uranyl acetate and lead citrate before examination using a transmission electron microscope ( Philips Morgagni 268D ) . For better visualization of the carbohydrate layer , another series was completed using ruthenium red . We used colonies of ticks free of Rickettsia , Ehrlichia , Anaplasma , Bartonella and Coxiella burnetii originating from Algeria that were morphologically and molecularly [23] identified as Rh . sanguineus and had been maintained in our laboratory in an incubator at 25°C with 80% relative humidity since 2006 [26] . When obtained , the 12S RNA mitochondrial sequence data presented 100% similarity to Rh . sanguineus from the USA ( HM014443 ) , Portugal ( FJ536554 ) , and Switzerland ( AF483241 ) . Individual New Zealand white rabbits ( Oryctolagus cuniculus ) were used for the attachment of non-infected ticks , as described above . Periodically , new ticks from the wild that tested negative by PCR were included in our non-infected Rh . sanguineus colony , as previously described [27] . The life cycle or developmental period of non-infected and R . conorii conorii-infected Rh . sanguineus ticks was studied through several generations . The duration of the larval , nymphal , and adult feeding ( the number of days from placement of the rabbit until drop-off ) were studied . In addition , the molting period covering the transition from larvae to nymphs and from nymphs to adults ( the number of days from drop-off to ecdysis ) , the pre-oviposition period ( the period from female drop-off to the beginning of oviposition ) , and incubation periods ( from the beginning of oviposition until hatching of larvae ) , as previously described [27] , were studied . The sum of the days of all of these parameters represents the total life cycle of R . conorii conorii-infected ticks . The weight of engorged females and the eggs of these females of non-infected and R . conorii conorii-infected Rh . sanguineus ticks was measured with an analytical balance ( XB 620M , Micromega groupSoframe ) . The weight data of engorged females and eggs were analyzed with GraphPad Prism™ v 2 . 0 software ( La Jolla , USA , www . graphpad . com/prism/Prism . htm ) . Batches of randomly selected eggs , larval and nymphal stage unfed or engorged ticks ( N = 100 ) and adult stage ticks ( N = 40 ) , either infected or non-infected , were used for each of three experiments from the 8th , 9th , and 10th generations . Each of the treatment groups of engorged ticks were held at a particular temperature ( 4° , 25° , 37°C ) for one month and then all of the ticks were held at the same temperature ( 25°C ) for an additional month . The non-engorged ticks held at 4°C , 25°C and 37°C for one month were attached to New Zealand white rabbits for feeding . The experiment with infected and non-infected ticks had been conducted in the same time . The relative humidity ( 80% RH ) was the same for all groups with a light/dark photoperiod of 16∶8 h . The following biological parameters were recorded after one month for infected and non-infected engorged nymphs held at 25°C: the number that were dead without molting , the number that had molted but were dead , and the total number of dead nymphs . For adult ticks , the following biological parameters were noted: the number of ticks dead before attachment on the rabbit , the number dead after attachment , and the total number of dead ticks . Each experiment was performed in triplicate . The infected and non-infected ticks of the corresponding temperature groups were compared . The numbers of dead ticks of each group were compared using a χ2 test conducted with Epi Info software , version 3 . 4 . 1 ( CDC , Atlanta , USA ) . Statistical significance was defined as p<0 . 05 . The animals were handled according to the rules of French Decree N . 8 87–848 of October 19 , 1987 , Paris . Each colony of non-infected and infected ticks had individual rabbits . For the non-infected ticks of the laboratory tick colony , a rabbit was used a maximum for three times for feeding . However , for the infected laboratory colonies and for the analysis of temperature on R . conorii conorii-infected and non-infected Rh . sanguineus ( experimental analysis ) , an individual rabbit for each batch and for each temperature condition was used only once . All experimental protocols were reviewed and approved by the Institutional Animal Care and Use Committee of the Université de la Méditerranée ( Marseille , France ) .
A total of thirty engorged female Rh . sanguineus ticks were collected from 7 dogs of patients who contracted MSF in Algeria . A single specimen collected in Ghazonet , Algeria tested positive for R . conorii conorii ( GenBank , accession number ompA: DQ518245 and gltA: AE008677 ) . The positive controls tested positive for all PCR reactions , and no signal was obtained from the negative controls for any PCR reaction . Molecular identification of tick species based on partial 12S rRNA mitochondrial sequence data indicated 99 . 7% ( 337/338 ) similarity to Rh . sanguineus from the USA ( HM014443 ) , Portugal ( FJ536554 ) , and Switzerland ( AF483241 ) . Twelve successive generations were obtained , and the infected colony is still growing in our laboratory as of this writing ( Figure 2 ) . The PCR assay was positive for specimens of all stages of these generations for rickettsial detection . For the 10th generation , all engorged nymphs ( 20/20 ) , all adults randomly chosen ( 16/16 ) , 4/4 females after laying eggs and the pools of eggs from these females tested positive by RT-PCR for rickettsial DNA . These data suggest 100% transstadial and transovarial transmission of R . conorii conorii in Rh . sanguineus ticks . The filial transmission rate ( FIR , proportion of infected eggs or larvae obtained from an infected female ) of R . conorii conorii was 99 . 07% ( 107/108 ) , 94 . 3% ( 66/70 ) and 95 . 5% ( 21/22 ) in larvae from several infected females of the 3rd , 4th , and 11th generations , respectively . Gimenez staining revealed many morphological structures compatible with R . conorii conorii in the salivary glands ( Figure 3B ) , ovaries ( Figure 4B ) and eggs ( Figure 5B ) . Electron microscopy of the ovarian tissue revealed heavy infection compatible with R . conorii conorii ( Figure 4C , 4D ) . The Rickettsiae exhibited typical rickettsial morphology and size , as previously described [10] . No difference was observed between the duration of life cycle of R . conorii conorii-infected and non-infected ticks held at 25°C , but the average weight of engorged females and eggs was found to be significantly lower in infected ticks . The number of infected females began to drop off on day 8 after placement on the rabbit , and their blood meal was completed by day 15 ( Figure 6 ) . The average weight of 31 infected engorged females was 0 . 38111 g ( range , 0 . 2518–0 . 5389 g ) compared to 0 . 4749 g ( range , 0 . 2333–0 . 6203 g ) for six non-infected engorged females ( p = 0 . 0136 ) . At 25°C , the pre-oviposition period started between one and two weeks after the end of female engorgement . The average weight of eggs from one female infected tick was 0 . 2099 g ( range , 0 . 0998–0 . 2976 g , 31 samples ) compared to 0 . 2919 g ( range , 0 . 1321–0 . 4271 g , 6 samples ) for non-infected eggs ( p = 0 . 0021 ) . The infected eggs began hatching within 2–3 weeks , as did non-infected eggs . Hatched larvae were kept at 25°C for at least 2–3 weeks before feeding . The duration of pre-feeding period was 2 to 4 weeks after the end of eclosion of the last specimen . Infected larvae fed for 3–6 days . The infected engorged larvae molted into the nymphal stage between 9 and 15 days after engorgement . Molted R . conorii conorii-infected nymphs were fed for approximately 3–4 weeks after ecdysis , as were non-infected nymphs . Nymphs were fed for 4–7 days . It took 2–3 weeks for engorged nymphs to molt into adults . Under our standard laboratory conditions , the life cycle of infected Rh . sanguineus ticks lasted 18 to 24 weeks . To avoid different abnormalities in infected ticks and to maintain genetic diversity , new , non-infected male ticks were placed on rabbits during the feeding of infected female ticks . In conclusion , under laboratory conditions we did not find any difference in the duration of developmental stages of the life cycle of R . conorii conorii-infected Rh . sanguineus when compared to non-infected ticks [27] .
This study confirms the vertical transmission of R . conorii conorii in naturally infected Rh . sanguineus ticks over twelve generations with a TOT rate of 100% and an FIR of up to 99% . R . conorii conorii was detected in ovary tissue by electron microscopy and by Gimenez staining , which supports the mechanism of transmission through several generations of infected ticks . The duration of the different steps of the tick life cycle in laboratory conditions were similar between non-infected and R . conorii conorii-infected ticks . These results are in agreement with recent published data about non-infected Rh . sanguineus [27] . The difference in the average weights of engorged females and eggs between the infected and non-infected ticks suggests that the fecundity of infected female ticks is lower than that of uninfected females . This implies that the prevalence of infection in a tick population should gradually decline and disappear without periodical augmentation . The mortality rate of infected and non-infected engorged nymphs and adults maintained in our laboratory at 25°C and 80% RH was approximately 2% . In contrast , a comparatively higher mortality rate was observed when R . conorii conorii-infected engorged nymphs ( 88–10% , 4°C; 17–67% , 37°C ) and adults ( 10–40% , 4°C; 37 . 5–85% , 37°C ) that were exposed to low temperature or high temperature for one month were transferred to 25°C , compared to the control group ( Table 1 , 2 ) . The negative effect of temperature on the viability of Rh . sanguineus infected with Rickettsia conorii conorii could be related to the long-recognized phenomenon known as reactivation , which remains poorly understood [28] . Between 1926 and 1930 , Spencer and Parker demonstrated that triturated and starved Dermacentor andersoni ticks infected with Rickettsia rickettsii , the agent of Rocky Mountain spotted fever , did not cause disease but did result in seroconversion when injected into guinea pigs . However , feeding the ticks for a short time or keeping them at an elevated temperature ( 24 to 48 h at 37°C before trituration and inoculation ) resulted in clinical manifestation of disease . These authors postulated that the virulence of R . rickettsii in the tick vector is linked directly to the physiological state of the tick and defined this phenomenon as “reactivation” [29]–[32] . In 1982 , reversible structural modifications of R . rickettsii were demonstrated to be linked to physiological changes in the tick host and correlated with reactivation , i . e . , the restoration of pathogenicity and virulence infectivity [28] , [33] . More recently , R . rickettsii was shown to be lethal for the majority of experimentally and transovarially infected D . andersoni [18] . However , infected female ticks incubated at 4°C presented a lower mortality rate than those held at 21°C or 27°C . Although temperature is a common environmental signal for the upregulation of virulence gene expression , the information currently available in the literature poorly explains the reactivation phenomenon and its consequences for ticks [34] . In the present experiments , the temperature range ( 4°C , 25°C and 37°C ) approximated the temperature differentials expected to be encountered by Rh . sanguineus in the natural environment in southern France and more generally , in Mediterranean settings . As confirmed in our study , infected and non-infected eggs , larval and nymphal unfed stages are unable to survive at a cold temperature in laboratory conditions , and the temperature exerts considerable influence on the length of their life cycle [27] , [35] . Recently , the effect of low temperature ( 8±2°C ) on non-infected Rh . sanguineus eggs has been shown to be a major limiting factor for the establishment of populations of the tick in colder regions [36] . Non-infected engorged nymphs and adults are less influenced by daily temperature . The maximum survival of nymphs and adult ticks occurs at 20–30°C and 85% relative humidity; the minimal temperature threshold for molting is between 10 and 15°C [17] . Rh . sanguineus overwinter as engorged nymphs or unfed adults [35] , so our preliminary results suggest that infected ticks might not survive the winter . This could help to explain the scarcity of infected ticks found in the wild . Further studies investigating whether the mortality of R . conorii conorii-infected ticks is higher among diapaused ticks would be interest and could have important implications for the ecology of MSF . Such studies could be performed by exposing ticks to natural conditions or simulating natural conditions with proper regimens of photoperiod and temperature , as the diapause is induced where temperature is still warm , but changes in photoperiod induces ticks to enter a state of dormancy in a safe place , in order to survive to adverse conditions that will come the next winter . In regards to the ecology of Rocky Mountain spotted fever , it is generally hypothesized that R . rickettsii is maintained in nature by the regular establishment of new populations of infected ticks . The probability of new populations of ticks becoming infected with Rickettsiae is difficult to precisely calculate , but a rough estimate can be obtained based on the assumed life span of susceptible mammals , the antibody prevalence in mammals , the average number of days of peak rickettsemia in infected animals and the number of days of infectious feeding on rickettsemic animals required to establish generalized infections in ticks [37] . It is likely that vertebrate reservoirs play a more dominant role in the ecology of R . conorii conorii than previously thought . Non-immune dogs , which include puppies in endemic areas or dogs living outside endemic areas of MSF or , at least , Rh . sanguineus , have been suggested as potential reservoirs for R . conorii conorii [1] . Recently , Levin et al . [38] reported that dogs are capable of acquiring R . conorii israelensis from experimentally infected Rh . sanguineus ticks that could also transmit infection to cohorts of uninfected ticks . Other animals have also been found to be experimentally susceptible to R . conorii , such as hedgehogs , Swiss mice , Hartley guinea pigs and Spermophilus citellus ( Citellus citellus ) [1] . Recently , one of 16 Rh . sanguineus collected from hedgehogs tested positive for R . conorii [39] . In addition , the role of the European rabbit Oryctolagus cuniculus in the epidemiology of MSF had been suggested by pioneering rickettsiologists [1] . Rabbit ticks and fleas , as well as that of small rodents such as Pitymys duodecimcostatus living in rabbits burrows , are suspected to be involved in the R . conorii conorii life cycle . Interestingly , the prevalence of infected Rh . sanguineus may vary from one specific setting to another within endemic areas , and the foci of MSF are usually small with a low propensity for diffusion [1] . However , a reservoir role has not been confirmed for any of these animals [1] . More work is needed to thoroughly decipher the relationship between R . conorii conorii and its vector , Rh . sanguineus . Aside from the need to definitively confirm the role of animal reservoirs in perpetuating R . conorii conorii , continued investigations of the Rh . sanguineus-R . conorii conorii interaction are needed to provide a better understanding of the factors influencing the ecology and epidemiology of MSF . In particular , studies on the poorly understood rickettsial inactivation-reactivation phenomenon may provide a better insight into the interaction between Rickettsiae and ticks .
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The bacterium Rickettsia conorii conorii is the etiological agent of Mediterranean spotted fever ( MSF ) , which is a life-threatening infectious disease that is transmitted by Rhipicephalus sanguineus , the brown dog tick . Rh . sanguineus-R . conorii conorii relationships in the wild are still poorly understood one century after the discovery of the disease . In this study , we collected naturally infected ticks from the houses of people afflicted by MSF in Algeria . Colonies of both infected and non-infected ticks were maintained in our laboratory , and we studied the effect of temperature variations on the infected and non-infected ticks . We did not observe any major differences between the biological life cycle of the infected and non-infected ticks held at 25°C . However , a comparatively higher mortality relative to the control group was noticed when R . conorii conorii-infected engorged nymphs and adults were exposed to a low temperature ( 4°C ) or high temperature ( 37°C ) for one month and transferred to 25°C . R . conorii conorii-infected Rh . sanguineus may maintain and serve as reservoirs for the Rickettsia if they are not exposed to cold temperatures . New populations of ticks might become infected with Rickettsiae when feeding on a bacteremic animal reservoir .
|
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"Abstract",
"Introduction",
"Materials",
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"Methods",
"Results",
"Discussion"
] |
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"medicine",
"infectious",
"diseases",
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2012
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Why Are There So Few Rickettsia conorii conorii-Infected Rhipicephalus sanguineus Ticks in the Wild?
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The mechanical unfolding of proteins is a cellular mechanism for force transduction with potentially broad implications in cell fate . Despite this , the mechanism by which protein unfolding elicits differential downstream signalling pathways remains poorly understood . Here , we used protein engineering , atomic force microscopy , and biophysical tools to delineate how protein unfolding controls cell mechanics . Deleted in liver cancer 1 ( DLC1 ) is a negative regulator of Ras homolog family member A ( RhoA ) and cell contractility that regulates cell behaviour when localised to focal adhesions bound to folded talin . Using a talin mutant resistant to force-induced unfolding of R8 domain , we show that talin unfolding determines DLC1 downstream signalling and , consequently , cell mechanics . We propose that this new mechanism of mechanotransduction may have implications for a wide variety of associated cellular processes .
The smAFM experiments were carried out according to protocols described previously [8] . The talin fragment polyprotein constructs , including flanking I27 , were synthesised and cloned into the pFN18a vector . The polyproteins were expressed in Escherichia coli BL21-CodonPlus ( DE3 ) -RILP competent cells , using the T7 promoter present in the plasmid . Protein expression was induced with IPTG when the culture reached an OD600 nm of 0 . 6 . Cells were lysed with lysozyme and sonication before protein purification with Ni-NTA beads in a batch process . The eluted proteins were analysed for purity with SDS-PAGE . Final concentration of protein used for experiments was 1–10 μg/ml . Glass coverslips were functionalised with the chloroalkane ligand to HaloTag as previously described [32] . The glass coverslips were first cleaned using Helmanex III ( 1% in water ) , acetone , and ethanol washes . The surfaces were then prepped with O2 plasma cleaning for 15 min . Surfaces were then silanised using ( 3-aminopropyl ) trimethoxysilane diluted to 1% in ethanol . Surfaces were then washed with ethanol and then dried with N2 . These amine-functionalised surfaces were then incubated with 10 mM succinimidyl-[ ( Nmaleimidopropionamido ) tetracosaethyleneglycol] ester ( SMPEG24 , Thermo ) diluted in 100 mM borax buffer ( pH 8 . 5 ) for 1 h . The final step involved incubating the surfaces overnight with 10 mM HaloTag Thiol O4 ligand in the same buffer . The surfaces were quenched 50 mM 2-mercaptoethanol in water . We used a commercial AFS-1 from Luigs & Neumann , GmbH , based on a device developed at the Fernandez Lab , Columbia University [33] . The cantilevers used were gold-coated OBL-10 levers from Bruker . The spring constants varied between 4–10 pN/nm as measured by equipartition theorem with the appropriate adjustments for cantilever geometry . Around 20 μl of protein solution was incubated on functionalised coverslips for 30 min prior to the experiments to allow for HaloTag binding . The cantilever was pressed into the surface with a force of approximately 300 pN to bind the cantilever to the polyprotein . Force extension experiments were conducted at 400 nm/s retraction rate . Data analysis was carried out using Igor Pro ( Wavemetrics ) , for which the wormlike chain model was applied . The following structures from RCSB Protein Data Bank were used as the protein models for the individual talin rod subdomains: R8 ( id 5FZT residues 1451–1588 ) , R8-DLC1 ( id 5FZT talin residues 1451–1588 and DLC1 residues 467–489 ) , and R7R8 ( id 2X0C ) . The talin structures were modified in PyMOL to produce the desired constructs . MD and SMD simulations were performed using Gromacs ver 2016 . 1 [34 , 35] at the Sisu supercomputer , CSC , Finland . The CHARMM27 force field [36] and explicit TIP3P water model [37] in 0 . 15 M KCl solution were used , and the total charge of the system was adjusted by K+ and Cl− ions . The energy minimisation of the system was performed in 100 , 000 steps , using the steepest descent algorithm . The equilibration was performed with NPT ensemble and was maintained at 310 K using the V-rescale algorithm [38] and 1 atm under Berendsen barostat , as implemented in Gromacs 2016 . 1 . First run of equilibration was performed for R8 and R8-DLC1 with long linkers . These complexes were followed over 30 ns simulation without applied restrictions . Both domains were very stable , with low root-mean-square deviation ( RMSD ) changes . Major structural changes were observed for the long interdomain linker . In the second equilibration run , the fixed and pulled Cα atoms were restrained with harmonic potential during the 10 ns simulation . The temperature coupling was applied separately for the protein and the solution parts . Each system was equilibrated up to 10 ns , with subsequent monitoring of the RMSDs of Cα atoms . Structures after 10 ns were used as the starting pulling conformation . Pulling vector was set between Cα of the first and the last residue of the appropriate domain parallel to the z-axis of the pulling box . The movement of Cα of N-terminal residue was restrained with harmonic potential , while Cα of C-terminal residue was subjected to the constant velocity pulling . After placing the equilibrated protein in the pulling box and orienting it appropriately in the pulling direction , the water solution was equilibrated as external bath during 1 ns bath equilibration [39] . Furthermore , equilibration was performed at isotropic conditions while pulling at semi-isotropic conditions when pressure control was turned off in the pulling direction ( z-axis ) . The constant velocity pulling SMD simulations were performed at 2 nm/ns with the spring constant set to 1 , 000 kJ/mol nm2 . All trajectories were produced in 3 repetitions . The WT MEF cell line was a kind gift from Dr Wolfgang Ziegler and has been previously described by Xu and Baribault in 1998 [40] . The Tln1−/−Tln2−/− MEF cell line has been previously described by Theodosiou and colleagues [17] . Both cell lines were maintained in high-glucose DMEM supplemented with 10% FBS and 1% GlutaMax ( Thermo Fisher Scientific , United States of America ) . A humidified 37°C incubator with 5% CO2 was used for culturing both cell lines . Cells were negative when tested for mycoplasma contamination . In all cell experiments other than the FRAP , a GFP-Talin1 plasmid was used . The GFP-Talin1 plasmid was a gift from Anna Huttenlocher ( Addgene plasmid # 26724 ) [41] . The GFP-DLC1 plasmid was a gift from Irene Ng [42] . The GFP-vinculin plasmid was obtained from Susan Craig [43] . The GFP-paxillin was a gift from Chinten James Lim ( Addgene plasmid # 80023 ) . The talin 1 plasmid ( Addgene # 26724 ) was used as template to generate the R8 , C1 , and C2 constructs by PCR-based mutagenesis starting construct via 5′ FseI and 3′ Scal . R8: For the substitution of amino acid glutamine with cysteine at position 1459 , CAA was replaced with TGC; and for the substitution of serine for cysteine at position 1583 , AGC was replaced with TGC . C1: For the substitution of amino acid glutamine with cysteine at position 1459 , CAA was replaced with TGC . C2: For the substitution of serine for cysteine at position 1583 , AGC was replaced with TGC . The GFP-DLC1 FRAP experiments were conducted on glass-bottom petri dishes ( Mattek ) coated with human plasma FN ( 10 μg ml−1; Sigma ) and incubated at 37°C . For the DTT condition , DTT ( Sigma ) was added at a concentration of 100 μM 30 min prior to the start of the experiment . Confocal photobleaching and TIRF imaging were carried out using an inverted microscope ( Eclipse Ti; Nikon ) . Five TIRF images were taken at 5 s intervals prior to bleaching for reference . Specified regions of the cells were then bleached using the confocal laser at 100% power . TIRF images were taken at 5 s intervals for 100 s to capture fluorescent recovery . Images were analysed with FIJI , with the fluorescent signal normalised between the prebleach intensity and background . Statistical analysis was then carried out using Prism ( GraphPad ) . Data were pooled from repeats . The significance between curves was measured using extra sum-of-squares F test on the best fit lines . Immunofluorescence staining was done on coverslips coated with 10 μg/ml fibronectin ( Sigma , F0895 ) . Following pertinent treatment , cells were fixed with 4% PFA ( Sigma , P6148 ) in PBS for 10 min and then blocked and permeabilised with 2% BSA-0 . 1%Triton ( Sigma , T8787 ) in PBS for 30 min . After blocking , cells were incubated with primary antibodies ( MLC-2 Millipore MABT180 1/200 and pMLC-2 Thr18/Ser19 Cell Signalling 3674 1/200 ) prepared in blocking solution for 1 h at room temperature in a humidified chamber . Then cells were washed in PBS and incubated with Alexa Fluor 488 conjugated secondary antibodies and Phalloidin ( Invitrogen , A22283 , 1/1 , 000 dilution ) prepared in PBS for 30 min at room temperature . Finally , coverslips were washed in PBS and mounted in mounting reagent with DAPI ( Invitrogen , P36931 ) . GSH was purchased from Sigma-Aldrich ( catalogue 78259 ) and used at a concentration of 10 mM . DLC1 antibody ( clone C-12 , catalogue sc-271915 , dilution 1/100 ) . siRNA for DLC1 was from Santa Cruz Biotechnology , catalogue sc-72134 . Primers used to monitor DLC1 gene expression knockdown: DLC1 ( m ) -PR , catalogue sc-72134-PR . The micropillar arrays are based on our protocol as described previously [22] . Pillar arrays were coated with human plasma FN ( 10 μg ml−1; Sigma ) and incubated at 37°C for 1 h before measurements . Cells that had been trypsinised before measurements were suspended in culture media and plated onto the pillar substrates . Time-lapse imaging of the pillars was conducted with an inverted microscope ( Eclipse Ti; Nikon ) operating in bright-field mode , with the samples held at an ambient temperature of 37°C . Image sequences were recorded with a sCMOS camera ( Neo sCMOS Andor ) at 0 . 5 Hz using a ×40 ( 0 . 6 NA , air; Nikon ) objective over 100 min . The position of each pillar in the time-lapse videos was tracked using a custom MATLAB program to track the centre of a point spread function of the intensity of the pillars across all frames . By selecting a location free of cells , tracking of a small set of pillars allowed a measurement of the stage drift to be obtained and corrected for in the data set . The time-dependent displacement of a given pillar was obtained by subtracting the initial position of the pillar ( zero force ) from the position in a given frame . Traction forces were obtained by multiplying the pillar displacements by the pillar stiffness; the maxima for each pillar were found to obtain the peak forces across the cell . To analyse the ECM remodelling ability of MEFs , collagen-I ( BD Biosciences , 354249 , stock concentration 9 . 37 mg/ml ) and Matrigel ( BD Biosciences , 354234 , stock concentration 9 mg/ml ) mixture gels were prepared with 10% 10× DMEM ( Sigma , D2429 ) and 10% FBS ( Gibco , 10500 ) , yielding to a final concentration of 4 . 4 mg/ml collagen-I and 2 . 2 mg/ml Matrigel . The gel mixture was neutralised with 1 M NaOH ( Sigma , S8045 ) ; then , 5 × 105 cells were embedded in gels in culture media . Eighty μl gel volume was added per well of a 96-well plate , which was pretreated with 2% BSA ( Sigma , A8022 ) for 1 h , washed with PBS , and air dried for 10 min . Gels were set 1 h at 37°C and then incubated with culture media for 2 d at 37°C . Polystyrene 6-well plates were coated with 10 μg/ml fibronectin in PBS ( pH 7 . 4 ) at 37°C for 1 h and washed 2 times with PBS . Transfected Tln1−/−Tln2−/− MEF cells were allowed to recover for 24 h , trypsinised , and plated onto fibronectin-coated well plates at a low confluency . Cells were allowed to attach for 90 min , followed by replacement of media ( 2 ml of fresh media ) to remove nontransfected cells . For treating the cells with diamide , diamide powder ( Sigma-Aldrich D3648 ) was dissolved in PBS to a stock concentration of 50 mM ( 8 . 6 mg/ml ) and further diluted with complete cell culture media to 150 μM concentration . One ml of the diamide-containing media was added dropwise to 2 ml of cell culture media in each well 15 min before live cell imaging was started . EVOS FL Auto ( Life Technologies , USA ) equipped with a 20× objective and 37°C and 5% CO2 incubator was used for live cell imaging for 12 h at 120 s intervals . The resulting image stacks were analysed with ImageJ version 1 . 50e with MTrackJ plugin [44 , 45] . For each plasmid construct or diamide treatment , 130–200 individual cells were traced from 3 fully independent experiments . Lysates of diamide-treated WT MEF cells were analysed by 2D SDS-PAGE to confirm the effect of diamide treatment on the level of cellular disulphide bonds . Cells at 80% confluency on 10 cm dishes were washed twice with warm PBS and treated for 120 min with 50 μM diamide dissolved in cell culture media . In negative controls , regular media were used instead . After the diamide treatment , cells were washed with ice-cold PBS and treated with 40 mM iodoacetamide in PBS for 5 min . Cells were lysed with 500 μl of RIPA buffer ( 50 mM Tris-HCl pH 7 . 4 , 1% NP-40 , 0 . 5% Na-deoxycholate , 0 . 1% SDS , 150 mM NaCl , 50 mM NaF ) supplemented with 40 mM iodoacetamide and Roche complete protease inhibitor cocktail . Cell lysates were incubated on ice for 30 min and centrifuged at 14 , 000 g for 20 min at 4°C to pellet cell debris . The concentrations of cleared lysates were determined with BCA assay and matched by diluting the lysates with RIPA buffer . For 2D SDS-PAGE , 120 μg samples of cell lysates were mixed with 2× Laemmli sample buffer without reducing agents , denatured at 95°C for 5 min , and loaded onto a medium-sized 1 mm thick 10%/4% polyacrylamide gel . The polyacrylamide gels were run at 25 mA current for 5 h , followed by vertical slicing of the gel with a clean scalpel to separate each lane . The gel slices were incubated in SDS-PAGE running buffer supplemented with 100 mM DTT for 20 min at room temperature to reduce disulphide bonds in the lysates . After the treatment , the gel slices were briefly washed with running buffer and treated with 100 mM iodoacetamide in SDS-PAGE running buffer for 10 min . The pieces of gel were placed horizontally on top of 1 . 5 mm thick 10% polyacrylamide gels and ran at 10 mA current for 14 h . Proteins on the gel were visualised by using silver staining . All statistical analyses were conducted with the Prism graphical software ( GraphPad , Software ) . Data were generated from multiple repeats of different biological experiments in order to obtain the mean values and standard errors ( sem ) displayed throughout . P-values have been obtained through t tests . Significance for the t tests was set at P < 0 . 05 , for which graphs show significance through symbols ( * , P < 0 . 05; ** , P < 0 . 01; *** , P < 0 . 001; **** , P < 0 . 0001 ) .
|
Mechano-induced conformational changes and the unfolding of protein domains are cornerstones of mechanotransduction and regulate the interaction of proteins with other molecules . Talin is a prominent molecule in focal adhesions and one of the few proteins that simultaneously connects integrin receptors in the cell membrane with the actin cytoskeleton . This bridging position , owing to the cytoskeleton’s contractile nature , exposes talin to forces along its length . In this work , we studied the implications of the R8 domain unfolding in the downstream activity of deleted in liver cancer 1 ( DLC1 ) , which binds the talin R8 domain and negatively regulates Ras homolog family member A ( RhoA ) . We created a talin mutant with the R8 domain resistant to mechanical unfolding and observed that cells expressing these talin mutants have altered patterns of focal adhesion dynamics and lower levels of actomyosin contraction . This leads to decreased traction forces and diminished cell migration . We propose a novel force-controlled molecular switch that refines the mechanism of talin-mediated focal adhesion activation , providing negative feedback during focal adhesion maturation . The broader effects of this talin-mediated mechanism need to be elucidated , as it might regulate multiple cellular events .
|
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2018
|
Mechanotransduction in talin through the interaction of the R8 domain with DLC1
|
Behavior is among the most dynamic animal phenotypes , modulated by a variety of internal and external stimuli . Behavioral differences are associated with large-scale changes in gene expression , but little is known about how these changes are regulated . Here we show how a transcription factor ( TF ) , ultraspiracle ( usp; the insect homolog of the Retinoid X Receptor ) , working in complex transcriptional networks , can regulate behavioral plasticity and associated changes in gene expression . We first show that RNAi knockdown of USP in honey bee abdominal fat bodies delayed the transition from working in the hive ( primarily “nursing” brood ) to foraging outside . We then demonstrate through transcriptomics experiments that USP induced many maturation-related transcriptional changes in the fat bodies by mediating transcriptional responses to juvenile hormone . These maturation-related transcriptional responses to USP occurred without changes in USP's genomic binding sites , as revealed by ChIP–chip . Instead , behaviorally related gene expression is likely determined by combinatorial interactions between USP and other TFs whose cis-regulatory motifs were enriched at USP's binding sites . Many modules of JH– and maturation-related genes were co-regulated in both the fat body and brain , predicting that usp and cofactors influence shared transcriptional networks in both of these maturation-related tissues . Our findings demonstrate how “single gene effects” on behavioral plasticity can involve complex transcriptional networks , in both brain and peripheral tissues .
Many studies have demonstrated that certain individual genes can exert strong influences on behavior , including naturally-occurring behavioral differences [1]–[4] . These results seemingly contrast with quantitative genetic and genomic studies , which have shown that behavioral variation usually involves multiple causal loci [5]–[7] and changes in the expression of hundreds to thousands of genes [8] , [9] . Combining these perspectives leads to the idea that single genes influence behavior through their interactions with many other genes , but mechanisms linking behavior to single genes and gene networks are not well understood . Transcriptional regulatory frameworks have already provided great insights into developmental and disease phenotypes , showing how transcription factors ( TFs ) originally identified through their individual effects on phenotypes work together to produce body parts [10] and how their dysregulation can lead to cancer [11] . Recently , large-scale genetic and genomic studies have begun to model the genome-scale transcriptional regulatory networks underlying behavior [5] , [6] , [12] , but few studies have elucidated the molecular mechanisms linking specific genes to regulatory networks underlying specific behaviors . This is largely the case even for TFs that have been clearly demonstrated to regulate behavioral change ( cf . [3] , [13] ) . The honey bee ( Apis mellifera ) lives in complex societies characterized by multiple forms of division of labor [14] . In a honey bee colony , the queen reproduces while workers ( non-reproductive females ) perform all tasks related to colony growth and development . To do this efficiently , workers exhibit a division of labor; young bees work in the nest at tasks such as broodcare ( “nursing” ) for the first 2–3 weeks of adulthood and then shift to foraging outside the hive for nectar and pollen for the remainder of their 5–7 week life . The age at which this transition occurs , the “age at onset of foraging , ” is socially regulated and depends on colony needs , signaled by pheromones produced by the queen , brood , and older workers [15] , as well as other environmental and genetic factors [16] . The age at onset of foraging is a key behavioral trait in honey bees and other social insects , and has been shown to be related to a variety of other behavioral traits such as aspects of foraging performance and colony defense , and physiological traits such as lifespan and cognitive development [16] . Hormones and signaling pathways act downstream of both heritable and environmental influences on behavioral maturation [16] . Nurses and foragers differ in the expression of thousands of genes , both in the brain [9] and in the fat bodies [17] , a peripheral nutrient-sensing tissue analogous to vertebrate liver and adipose tissues [18] . Environmental and hormonal factors induce some of the same changes in gene expression that occur naturally during maturation , suggesting that their effects on behavior are rooted at the transcriptional level [19] . Moreover , reconstruction of a brain transcriptional regulatory network ( TRN ) for behavior demonstrated that a surprisingly large fraction of maturationally-related brain gene expression in the honey bee can be accurately predicted from the expression of TFs alone [20] . These results suggest direct links between specific TFs and behavior , but roles for specific TFs have not been experimentally demonstrated . We selected ultraspiracle ( usp; the insect ortholog of the Retinoid X Receptor , RXR [21] ) to aim for such an experimental demonstration for the following reasons . First , the USP cis-regulatory motif is enriched in the promoters of genes differentially expressed between nurses and foragers [22] . Second , in other insect species USP is linked to juvenile hormone ( JH ) , an endocrine regulator of honey bee behavioral maturation and maturation-related gene expression [23]–[25] . Third , in the bee usp is rapidly up-regulated in the fat bodies following JH treatments , its cis-regulatory motif is found in the promoters of some JH-responsive genes , and the expression of some JH-related genes is influenced by usp RNAi [26]–[28] . Moreover , USP's vertebrate homologs ( RXRs ) are master regulators of metabolism and nutritional physiology , mediating responses to a variety of nutritionally-related hormones ( e . g . , thyroid hormone ) and lipid-like molecules [29] , [30] . Changes in nutritional physiology are also linked to honey bee behavioral maturation [17] , [31] . However , despite its connections to hormonal and nutritional processes that influence behavioral maturation , few studies have shown a direct effect of RXR and USP on behavior in any species ( c . f . , [32] ) , and transcriptional mechanisms are unknown . We studied usp transcriptional effects in the fat bodies . Although brain circuits are the most proximal location for behavioral regulation , roles for the peripheral tissues and ganglia are also well established , especially in invertebrates [33] . In bees , the effects of nutrition and JH on behavioral maturation are thought to occur in part via their effects in the fat bodies [31] . Behavioral maturation in honey bees involves coordinated changes in peripheral and neural signaling , as occurs in most animal species including during human puberty [34] . The increase in circulating levels of JH in honey bees causes changes in endocrine gland size and function as well as changes in behavioral responsiveness to task-related stimuli [16] . Poor nutrition causes bees to initiate foraging precociously [35] , and the fat bodies , the tissue responsible for lipid storage in insects , are an important sensor for nutritional changes in other insect species [36] , [37] , and likely in bees as well . Bees lose 50% of their lipid stores as part of normal maturation [38] , and experimental inhibition of lipid storage induces precocious foraging similar to food deprivation [39] . JH treatments also cause precocious foraging [40] , [41] and JH titers rise naturally prior to the onset of foraging [42] , [43] , whereas removal of the JH-producing corpora allata glands delays foraging ontogeny [44] . JH action influences many tissues , including the brain [19] , [45] , [46] , and in addition a role for the fat bodies is indicated by the interactions of JH with the yolk protein vitellogenin ( Vg ) . Vg is a conserved yolk protein that is produced exclusively in the fat bodies and that has taken on novel hormone-like functions in honey bees , including a regulatory role in behavioral maturation explicitly linked to JH [4] , [47]–[49] . Together , these results suggest that the fat bodies have causal , integrative functions in the regulation of behavioral maturation , mediating responses to both nutritional status and JH . We show that RNAi fat body knockdown of usp delays the age at which bees initiate foraging . We then used a combination of transcriptomics , chromatin immunoprecipitation—genomic tiling microarrays ( ChIP-chip ) , and informatics to elucidate transcriptional targets of USP in the fat bodies and transcriptional regulatory network reconstruction in both fat body and brain to gain further insights into how USP and its targets might interact . Our results support the hypothesis that the basis for the usp behavioral and transcriptomic effects involve interactions with transcriptional cofactors to mediate responses to JH in both the fat bodies and brain .
Because JH accelerates behavioral maturation , and because JH treatment up-regulates usp [26] , we hypothesized that inhibition of usp would delay the onset of foraging . We focused on a potential role for usp in the fat bodies because of their known regulatory functions in this behavior , the JH and nutritional connections described above , and the known efficacy of fat body RNAi injections in honey bees [4] . Direct injection of dsUSP into the abdomen resulted in knockdown of usp transcripts and protein ( usp RNAi ) in fat body tissue ( Figure 1A , 1B ) but not in the brain ( Figure 1C ) . We placed bees treated with usp RNAi ( or control dsRNA ) into experimental colonies in the field and observed the age at which they initiated foraging . As predicted , usp RNAi caused a significant , ca . 15% , decrease in the number of bees initiating foraging during the 5 d observation period , across 9 independent trials ( Cox Proportional Hazards , P = 0 . 03; Figure 1D ) . Similarly , a previous study showed that removal of the JH-producing corpora allata glands delayed but did not block foraging ontogeny [44] , suggesting that molecular components of JH signaling affect the timing of behavioral maturation , and not its overall occurrence . We also found replicable differences in the strength of the usp RNAi effect for bees from different genetic sources ( Figure S1 ) , suggesting that there is naturally occurring genetic variation for sensitivity to usp; similar genetic variation has been reported for the effects of JH analog treatments [50] . Several considerations suggest that our results were not due to a toxic effect of RNAi . First , the behavioral effect was in the opposite direction to effects of stressors: e . g . , parasite infection [51] , social isolation [52] , or injection alone [4] , each of which leads to precocious foraging . Second , 50% of the treated bees did forage during the observation period , just later than the untreated bees . Our results demonstrate that usp has a causal effect on honey bee behavioral maturation . We hypothesized that usp influences behavior by regulating a network of direct and indirect target genes in the fat bodies . We first characterized usp-responsive genes through direct mRNA sequencing of fat tissue from bees treated with usp RNAi . Because USP's vertebrate homologs are known to have hormone-dependent effects on transcription ( reviewed in [53] ) , we measured the effects of usp RNAi in both a low hormone condition – i . e . , in the presence of endogenous JH only – and in a high hormone condition , following treatment with the JH analog ( JHA ) methoprene ( a 2×2 factorial experiment ) . Transcriptome profiling revealed 85 usp-responsive genes in the fat bodies ( False Discovery Rate [FDR]<0 . 1; Figure 2A; Table S1 ) . We confirmed 4 of these results by qPCR ( Figure S2 ) . All but 3 of the 85 usp RNAi-responsive genes responded statistically indistinguishably ( FDR>0 . 1 ) to usp RNAi in the two hormone conditions , suggesting that endogenous levels of JH ( or other factors ) were sufficient to induce transcriptional responses of these usp targets . Integrating these findings with other transcriptomics experiments [17] we found that 34% of usp targets were among those that we previously found to be differentially expressed in the fat bodies of nurses and foragers ( 29 of 85; 1 . 58x-enriched , P = 0 . 003; Figure 2B ) . These results suggest that usp regulates behavior at least in part by influencing ( directly or indirectly ) the transcription of maturationally-related genes in the fat bodies . We suspect that this list of 85 usp RNAi-responsive genes represents only a fraction of usp's targets , for the following reasons . RNAi resulted in a modest knockdown of usp ( ca . 35% ) , and we measured only a single time point ( 72 h after dsRNA injection ) . We also likely missed genes that respond to usp only under specific hormonal or nutritional conditions not present in this experiment; context-dependent responses to USP are well-known in other species [53] . Seeking to identify a broader set of putative direct target genes , we characterized USP's genomic binding sites with ChIP-chip , in 6 independent replicates using tissue from the fat bodies of nurses and foragers ( ChIP was performed using an antibody specific to honey bee USP; Figure S3 ) . This experiment revealed 1360 genomic binding sites for USP ( Figure 2A; Table S2; ChIP-qPCR validation for a few binding sites shown in Figure S4 ) . These sites were located within 10 kb of 848 putative direct target genes ( Table S2 ) . 759 binding sites ( 53% ) contained at least one copy of a well-characterized cis-regulatory DNA sequence bound by USP in Drosophila melanogaster; this was the most strongly enriched motif out of >600 motifs we examined ( the “GGGGTCACS” motif [54]; P<1e-75; Figure 2C ) . In addition , 182 of these 848 putative target genes are also located within 10 kb of a USP binding site in the Drosophila genome [55] . ( By these criteria , 5643 genes were considered putative targets of USP in Drosophila , of which 3068 had bee orthologs . The 182 target genes shared between the two species represents 45% of the bee targets with one-to-one orthologs but was not statistically enriched; P>0 . 05 ) . The combination of conserved cis-regulatory sequences and conserved target genes suggests extensive evolutionary conservation of the USP regulatory network , despite ca . 300 M years divergence between bees and flies . It appears that many putative direct USP target genes are involved in maturation . USP targets included components of signaling pathways that have previously been shown to influence the timing of behavioral maturation , such as inR1 ( a receptor for insulin-like peptides [56] ) and foraging ( a cGMP-dependent protein kinase [57] ) . 117 of the 848 putative direct USP target genes ( 14% ) were differentially expressed between nurses and foragers ( Figure 2B; this subset was not statistically enriched: P>0 . 05 ) . These results suggest that usp influences maturation through a subset of its direct targets , including several genes already known to function in behavioral maturation , but which had not been known to work together . That we did not find stronger overlap between USP targets and genes differentially expressed between nurses and foragers is not necessarily surprising; USP is known in other species to regulate distinct sets of targets via interactions with different cofactors [53] , so different subsets of USP targets are likely active in other contexts . Our experiments have identified both putative direct and indirect targets of USP that are components of a hierarchical transcriptional network underlying maturation . Five of the 85 genes that responded to usp RNAi were located within 10 kb of one of USP's genomic binding sites ( Figure 2B; Table S1 ) ; these 5 are likely direct targets . The remaining 80 genes are more likely indirect targets . Further evidence for a hierarchical structure of the USP regulatory network comes from the enrichment of USP's direct targets for other TFs ( Gene Ontology , “regulation of transcription” , P<3 . 02e-9 ) , including the bee homologs of 67 putative TFs from the FlyTF database ( Figure 2D ) . Some of these TFs are likely the direct regulators of USP's indirect targets , and they include TFs known to function in hormonal signaling cascades ( e . g . , Hr46 [Figure 2A] , E75 , Chd64 , and usp itself [25] , [46] , [58] ) and in the regulation of behavior ( e . g . , fruitless and the Egr homolog stripe; reviewed in [59] ) . Two of these TFs among USP's targets – E75 ( a nuclear hormone receptor critical for responses to JH during development [25] ) and Chd64 ( which physically interacts with USP as part of a protein complex bound to JH response elements [24] ) – were up-regulated in foragers compared to nurses ( Figure S5 ) , so these JH-related TFs may be particularly likely to function in feed-forward regulation of maturationally-related targets of USP . A third TF , SoxNeuro ( Figure 2A ) , was one of the few genes identified as a USP target by both ChIP-chip and transcriptomics , suggesting that it too could have an integral role in downstream responses to USP . Together , usp RNAi , transcriptomics , and ChIP-chip revealed direct and putatively indirect USP targets in the honey bee fat bodies , including a large fraction that were differentially regulated during maturation . We next explored hormonal , nutritional , and transcriptional mechanisms linking USP target genes to behavior . We explored the hypothesis that usp mediates responses to JH and nutritional status by integrating our results with additional transcriptomics experiments . We identified 182 genes in the fat bodies that were differentially expressed in response to JH analog treatment ( based on the usp RNAi x JHA mRNA-seq factorial experiment described above; Figure 3A , Figures S6 and S7; Table S3 ) . Most JHA-responsive genes were also differentially expressed between nurses and foragers ( 97 of 182 genes; Figure 3A ) , and their responses to JHA and maturation were strongly correlated ( r = 0 . 57; P = 9e-10; Figure 3B ) . JH thus induces “forager-like” gene expression in the fat bodies , as in the brain [19] . Our results indicate a close association between transcriptional responses in the fat bodies to USP , JH , and maturation . Nearly half of the 85 usp-responsive genes ( 42 genes; 47% ) also responded to JHA ( P = 6 . 1e-54 ) , including 18 that were additionally differentially expressed between nurses and foragers ( Figure 3A ) . Moreover , 33 of the 42 genes that responded to both usp RNAi and JHA ( 79% ) were downregulated by usp RNAi and upregulated by JHA . Therefore , USP and JH frequently activate the same genes , including those that are involved in maturation ( Figure 3C ) . The overlap between transcriptional targets of USP and JHA suggested the hypothesis that they work together to regulate transcription . If USP acts downstream of JH , transcriptional responses to JHA should be inhibited by usp RNAi . However , as noted above , we found significant usp RNAi x JHA interactions ( FDR<0 . 1 ) for only 3 of 85 usp RNAi-responsive genes . Our inability to detect statistical interactions could relate to technical limitations such as incomplete usp RNAi knockdown . Alternatively , USP and JH may independently regulate their shared transcriptional targets . To further explore the relationship between USP and JH , we focused on the 33 genes that were activated by both . Indeed , 27 of these 33 genes had smaller fold responses to JHA following usp RNAi ( Figure 3D ) . For instance , the response of SoxNeuro to JHA was almost completely blocked by usp RNAi ( Figure 2A ) . The predominance of decreased vs . increased fold changes was statistically significant ( P = 6e-4 ) and was not an artifact of a bias in the broader dataset ( Figure S8 ) . Taken together , these results support the hypothesis that USP mediates transcriptional responses to JH . We cannot discern if these effects of USP on JH signaling are direct or indirect . We performed similar analyses to determine whether usp is also involved in the effects of nutritional status on behavioral maturation . We showed in a previous study that the fat bodies of bees fed nutrient-rich vs . nutrient-poor diets differ in the expression of 3372 genes , about half of which were also differentially expressed between nurses and foragers [17] ( Figure 3D ) . In contrast to our findings for maturation- and JHA-responsive genes , there was no significant overlap between usp-responsive and diet-responsive genes ( 19 genes , 0 . 88x-depleted , P = 0 . 08; Figure 3D , 3E ) . There was also no apparent bias in the directions in which usp and diet influenced these genes ( 11 genes regulated in directions concordant with the effects of usp RNAi and diet quality on maturation; 8 genes discordant; Figure 3F ) . Therefore , usp likely acts downstream of JH , but not nutritional status , in regulating behavioral maturation . By what mode of action does USP mediate responses to JH and influence behavioral maturation ? In other species , USP ( i . e . , RXR ) most frequently regulates transcription via interactions with additional TFs with which it forms heterodimers , typically activating gene expression only when a hormone or other ligand is bound to one of these cofactors [53] . The ability of USP to interact with several different TFs could also explain why only a subset of USP targets in the bee are associated with maturation . We performed additional informatic analyses to explore potential mechanisms linking USP and transcriptional cofactors to JH and maturation . In its most common mode of action , USP binds the same genomic locations regardless of hormonal titers [53] . Several results suggest that differential binding of USP in the genomes of nurses and foragers is not an important mechanism by which this TF influences behavioral maturation . First , there were no statistically significant nurse-forager differences in USP genomic binding sites in fat body ( P>0 . 01 , FDR>0 . 9 for all binding sites; no fold difference >1 . 46; Figure 4A ) . Second , we found strong correlations in binding intensity between nurse and forager samples across all 1360 binding sites ( r = 0 . 96 , P≪2 . 2e-16; Figure S9 ) . Third , when we focused on the subset of USP target genes that showed even a hint of differential binding ( 116 genes with 1 . 25–1 . 46 fold , non-significant differences between nurses and foragers ) , we found no relationship between this differential binding and differential gene expression ( Pearson correlation , r = −0 . 1 , P = 0 . 34; Figure 4B ) . Fourth , although genes that responded to usp RNAi were often differentially expressed between nurses and foragers ( above ) , the direction of their responses to usp RNAi and to maturation were only weakly correlated ( Pearson correlation , r = 0 . 20 , P = 0 . 29; Figure 4C ) . These results suggest that while USP targets are frequently involved in maturation , maturation-related changes in USP binding or expression do not determine the direction of these transcriptional responses . Since usp is known in other contexts to activate transcription via TF cofactors [53] , [60] , we next focused on characterizing potential interactions with additional TFs . We considered two possible roles for transcriptional cofactors in USP-mediated , maturation-related , transcriptional responses to JH . First , because USP itself does not bind JH at physiological levels [23] , [25] , additional TFs might be involved in linking transcriptional responses to JH and USP . Second , because distinct subsets of USP targets were either high in foragers or high in nurses other TFs might be involved in distinguishing these sets of targets . We searched for molecular signatures of potential cofactors by scanning the genomic regions around USP's binding sites for matches to ca . 600 cis-regulatory motifs that had been identified previously in vertebrates or in D . melanogaster ( Methods ) . TFs in the same family often recognize very similar motifs , so this information alone cannot assign specific cofactors for USP . Rather , this analysis was designed to test general hypotheses about classes of potential cofactors . We first scanned the genomic regions around all USP binding sites to identify signatures of potential “general” transcriptional cofactors . As noted above , the strongest enrichment observed was for a USP motif ( “GGGGTCACS” , P = e-75 ) , but a handful of other motifs also appeared significantly enriched ( Figure 2C ) . One of the most enriched motifs was “GRCACGCKVS” ( P = e-56 ) , which matches a putative Juvenile Hormone Response Element ( JHRE ) recognized by the bHLH TF Methoprene-tolerant ( Met ) [24] , [61] ( Figure 2C; Figure 5A ) . This motif is likely recognized by multiple members of the bHLH family of TFs , but the possibility of MET binding is particularly intriguing . This TF is required for developmental responses to JH in some insect species [62] , binds JH with strong affinity [63] , and has been shown to form protein-protein interactions with USP in vitro [64] . We also observed that putative USP binding sites ( i . e . , the locations of “GGGGTCACS” motifs within peak regions identified by ChIP-chip ) and the nearest GRCACGCKVS motif ( putative bHLH binding site ) were consistently overlapping , 3 base pairs apart ( Figure 5B ) . Closely spaced pairs of motifs can indicate physical interaction between the TFs that recognize those motifs [65] , so this result supports the idea that USP and a bHLH TF such as MET work together to mediate responses to JH [25] , [64] . Motivated by this observation , we designed a statistical test for spacing constraints between adjacent pairs of binding sites for USP and each of the next 10 most highly enriched motifs within peak regions ( Figure 4D ) . Strongest evidence of such constraints was found for two motifs known to bind MET in Drosophila , CACGCGMC and GRCACGCKVS , and a motif recognized by ADF , BMGYBGYYGYNGMVBV , and we found significant spacing constraints ( P< = e-3 ) , for 7 out of the 10 tested motifs ( including MAZR , PPAR , GABP_B , and ARA ) . These results indicate that USP binding sites in honey bee fat body contain sequences recognized by multiple other TFs , with a particularly strong signature for a JH-associated motif that is putatively bound by bHLH TFs . Why do different subsets of USP targets show distinct responses during behavioral maturation ? In other species , USP regulates distinct targets and can shift from activation to repression of its targets depending on which other transcription factors are present [53] , [60] . To explore the idea that this might also occur in the context of behavioral maturation , we compared the cis-regulatory sequences present at USP binding sites near target genes with different classes of transcriptional response ( high in foragers , high in nurses ) . This analysis revealed a variety of motifs that differentiated these binding sites . For instance , “high-foraging” binding sites often contained the PPARG_RXRA motif , recognized in vertebrates by a heterodimer of USP and PPARγ ( a well-known nutritionally-related cofactor of USP [29] ) , but this motif was almost never present at “high-nursing” binding sites ( P = 0 . 003 ) ( Table S4 ) . Insect homologs of PPARs are not known , but this result suggests that a particular USP-containing heterodimer specifies foraging-related gene expression . In addition , high-nursing binding sites were more likely than high-foraging binding sites to contain motifs recognized by the TF broad , which acts downstream of JH in developmental contexts [58] ( “br_Z4” , P = 0 . 0001; “I_BRCZ3_01” , P = 0 . 0002 ) . These results suggest that , as in other contexts [53] , [60] , USP regulates behavioral maturation by interacting with context-specific transcriptional cofactors . We have thus far described transcriptional mechanisms by which JH and usp influence the expression of maturationally-related genes in the fat bodies . In addition to its effects on the fat bodies , JH is known to cause behavioral maturation-related changes in brain morphology [45] , brain chemistry [66] , and brain gene expression [19] . If our findings do indeed reflect the effects of usp on behavioral maturation , we would expect that similar transcriptional mechanisms underlie hormonally-mediated maturational changes in the brain . To explore this issue , we generated microarray transcriptome profiles from both the brains and fat bodies of 60 individual bees , collected following manipulations of nutritional and hormonal factors that are known to influence behavioral maturation in part via their action in the fat bodies: rich vs . poor diet and vitellogenin RNAi , respectively . Poor diet accelerates behavioral maturation ( measured as a precocious onset of foraging [39] ) , and this effect is also caused by vitellogenin RNAi knockdown [4] . We used a combination of Weighted Gene Co-expression Network Analysis [67] and CoherentCluster [68] to characterize modules of genes that were tightly co-expressed with each other in both tissues . We also assembled ( from previous publications and statistical analysis of our vg RNAi and diet microarray experiments ) lists of individual genes that responded in the fat bodies and brain to maturation [17] , [69] , JHA [19] , vg RNAi [17] , and diet [17] . We then integrated these datasets and our list of USP targets to explore the roles of each of these factors in coordinating gene expression changes in the periphery and brain . Our results suggest extensive co-regulation of gene expression responses in fat and brain , both for individual genes and for modules of co-expressed genes . Many genes influenced by maturation , JHA , vg , and diet were differentially expressed in both the fat bodies and brain , and the fold change responses of these genes to each of these factors were positively correlated between the two tissues ( Figure S10 ) . Individual genes with coordinated responses in the two tissues included components of the JH signaling pathway , including a TF that physically interacts with USP on the promoters of JH-responsive genes , Chd64 ( a USP target detected in our analyses , above ) , as well as a JH-degrading enzyme , JH epoxide hydrolase [70] , [71] ( Figure S11 ) . These results suggest that maturation influences many of the same genes in the fat bodies and in the brain , and that these concordant responses involve JH . Gene co-expression analysis suggested that these shared patterns of differential expression are due to co-regulation . We found 497 “coherent” modules of genes , i . e . , modules that were tightly co-expressed with each other in both fat and brain ( each module contained between 3 and 105 genes; Table S5 ) . Much of this brain-periphery co-expression was linked to maturation: 85 of 497 coherent modules were statistically enriched for genes that were differentially expressed in one or both tissues during maturation ( Figure 6A; Table S5 ) . Two results suggest a role for maturationally-related hormones in driving these coordinated responses . First , 61 out of 85 maturation-related modules were also enriched for genes that responded to at least one of the following – JHA , Vg RNAi , USP RNAi , or Diet – significantly more than expected by chance ( P = 1 . 6e-8; Figure 6A , 6B ) . Second , modules of genes that were co-expressed with one another in only one tissue were less likely to be enriched for genes influenced by JHA and Vg than were modules of genes co-expressed in both fat and brain ( Figure 6B ) . Together , these results suggest that shared hormonally-driven , transcriptional mechanisms underlie the coordinated peripheral and neuronal changes that occur during behavioral maturation .
We have shown that the transcription factor ultraspiracle influences both behavioral maturation and the expression of maturation-related genes in honey bees , most likely via interactions with transcriptional cofactors to mediate responses to JH , in both the fat bodies and brain . These results provide insights into the mechanisms linking social behavior to genes and gene networks . Our results fall short of final proof for any specific mechanism for USP's influence on behavioral maturation , yet our evidence , together with prior results from JH experiments [24] , [25] , [58] , [61]–[64] , support the conclusion that the effects of this hormone are mediated by a complex transcription-based mechanism involving USP . We present a verbal model ( Figure 7 ) for the largest class of USP gene targets detected in this study , which were down-regulated by usp RNAi and up-regulated by JHA . We propose that USP mediates responses to JH as part of a complex of proteins pre-assembled at the promoters of JH-responsive genes . This model can explain differential gene expression caused by both USP and JH despite the fact that USP was found to bind the same genomic locations in the fat bodies of nurses and foragers . Our model builds on existing knowledge about the modes of action of both USP and JH . In other species , USP forms heterodimers with other TFs to mediate responses to a variety of hormones and other lipid-soluble molecules [53] , [72] . These complexes of TFs are often pre-assembled at promoters and only influence transcription when hormones or other ligands are present [53]; this is consistent with our finding that USP binds in the same genomic locations in nurses and foragers . USP is known to regulate distinct sets of target genes by interacting with several different heterodimer partners [53] , [55] , which may explain why only a subset of USP targets were associated with honey bee maturation . Moreover , interactions between USP and other TFs in other species have been shown to cause some USP targets to be up-regulated and others down-regulated in response to the same hormone [60] . The distinct cis-regulatory motifs surrounding nursing-related and foraging-related targets of USP hint at a similar mechanism underlying transcriptional responses to JH during honey bee maturation . Together , these results suggest that USP functions in honey bee maturation by mechanisms similar to those known in other species for other USP-influenced phenotypes . JH has long been known to regulate behavioral maturation in honey bees [37]–[41] and other forms of behavioral plasticity in insects [25] , but underlying molecular mechanisms for its behavioral effects have remained elusive . Our study adds to a growing body of evidence that a complex of proteins including USP , MET , EcR , Chd64 , and FKBP39 mediates responses to JH [24] , [25] , [64] . A central role for MET in this complex is supported by its ability to bind physiological concentrations of JH [63] , and manipulations of MET influence JH-related developmental phenotypes in a variety of insect species [25] , [61] , [62] . Our study suggests that USP is also required for normal JH signaling , at least in the context of worker honey bee behavioral maturation . A previous study showed that USP and MET physically interact in vitro and in vivo [64] . Our discovery of co-localized cis-regulatory motifs recognized by USP and bHLH TFs provides further evidence that these TFs could work together to mediate responses to JH . Most known heterodimer partners of USP are other members of the Nuclear Hormone Receptor family of TFs , but MET is a bHLH TF . Thus , if our hypothesized interaction between USP and MET proves true , the JH signaling complex has similarities to the USP-containing heterodimers that mediate responses to other hormones but is an atypical variation on that theme . Our study provides a first attempt to experimentally characterize the transcriptional regulatory mechanisms underlying division of labor in honey bee colonies , but many questions remain , both about the roles of USP and of other TFs . Our ability to draw definitive conclusions about USP is limited by the incomplete RNAi knockdown of USP that occurred in our study . Would a stronger knockdown have led to a longer delay in foraging ontogeny , or influenced the expression of more genes , or completely block responses to JH ? We expect that these questions will be answered in the future as technology for genetic manipulation in honey bees improves . We also anticipate that some of the questions about the role of USP in JH signaling raised by this study can be addressed immediately in Drosophila melanogaster More generally , our results suggest intriguing hypotheses about the transcriptional regulatory networks ( TRNs ) underlying behavior . We found that USP binds genomic regions near genes encoding several other TFs , and USP's genomic binding sites were enriched for multiple cis-regulatory motifs recognized by distinct classes of TFs . Together , these results suggest that many TFs could be involved in behavioral maturation . Supporting this hypothesis , our recent study demonstrated that the computationally-predicted targets of 78 TFs were enriched for genes that are differentially expressed in the context of honey bee maturation [20] , and in the present study we characterized 16 of these 78 maturation-related TFs as direct targets of USP . These and other TFs were implicated in the regulation of other honey bee behavioral traits that are related to the age at onset of foraging , aspects of foraging performance and colony defense [20] . The potential involvement of such a large number of TFs underscores the complex , combinatorial nature of transcriptional networks underlying behavior . The success of both experimental and computational approaches to deduce some of the same regulatory relationships suggests that it should be possible to use a combined approach to understand comprehensively how genes and gene networks influence behavioral maturation . Future studies should test functional roles for additional TFs: understanding the role of MET and its hypothesized interactions with USP and JH will be of particular interest , and roles of predicted regulators of maturation that are not associated with JH should be tested as well . Our results suggest that maturation-related , hormonally-regulated transcriptional networks are at least partly preserved between the periphery and the brain . Coordination between the periphery and brain is indicated by the coincident changes in behavior and fat body physiology that occur during worker maturation , and by the ability of both peripheral signals ( e . g . , changes in abdominal nutrient stores ) and central signals ( e . g . , pheromones perceived as olfactory stimuli in the brain ) to induce these changes [39] , [73] . Our results build on previous demonstrations that JH mediates maturational changes in both the brain and periphery by showing that these diverse hormonal actions occur through overlapping transcriptional mechanisms in different tissues . Transcriptional regulation of usp by JH in honey bee brain has been shown previously [46]; future experiments should test the hypothesis that USP mediates transcriptional responses to JH in the brain via the same target genes that we characterized in the fat bodies . We speculate that JH and USP regulate many of the same direct target genes in both tissues , and that tissue-specific responses might result from interactions with unknown , tissue-specific transcription factors , leading to the activation of different downstream effector genes . Transcription factors influence behavior via a variety of mechanisms over different timescales . FoxP2 – which influences language abilities in humans – has been shown to regulate genes that are involved in brain development and that are differentially expressed in the brains of humans and chimpanzees [3] , suggesting that this TF induces evolutionary , lifelong differences in behavior through its effects on the development of neuronal circuits . Other transcription factors , such as Creb and Fos , induce target gene expression in a cell-specific manner following neuronal activity [74] , suggesting highly localized , acute timescale functions in the adult brain . Our results demonstrate transcription factor behavioral effects over an intermediate timescale – days to weeks – by mediating gene expression changes that occur outside the brain . The behavioral repertoires of individuals and species presumably arise from transcriptional regulatory mechanisms acting over all these timescales and cell types , together linking the genome and the environment to behavior .
Bees were maintained at the University of Illinois Beekeeping Facility according to standard beekeeping practices . We used exclusively bees from source colonies headed by single-drone inseminated queens to reduce within-trial genetic variation; all experiments were replicated in at least two independent trials using queens from distinct European genotypes . We used previously described dsUSP probes to knock down USP expression [27]; dsUSP was provided as a gift from Beeologics Inc . ( Rehovot , Israel ) . Control dsRNA was dsGFP ( also a gift from Beelogics ) or ( in a few behavioral trials ) dsRNA matching the pUC vector , synthesized with standard in vitro transcription methods using T7 RNA polymerase . One-day-old bees were injected intra-abdominally with 20 ug dsUSP or control dsRNA dissolved in 1 ul ddH2O , as described [48] . Bees were painted with an identifying mark on the thorax ( Testor's enamel ) and placed into Plexiglas cages containing ca . 25 bees with equal numbers from each group , fed pollen paste ( 45% pollen/45% honey/10% water ) and sugar syrup ( 50% sucrose w/v in water ) , replaced daily ( as in [56] ) . Experimental procedures for behavioral experiments were modified slightly from [56] . 3 days after dsRNA treatment , bees were placed into small , experimental colonies that also contained ca . 1000 1-d-old bees , a queen , and honeycomb frames containing honey and pollen . We observed the hive entrance during peak foraging times ( at least 3 h/d ) for the following 5 days and marked bees as they returned from their first foraging flight . To account for any differences between groups in survival and for absconding bees , we counted all the bees remaining in the hive on the evening after the last day of observations . We collected age-matched 8–10-day-old nurses observed placing their heads into honeycomb cells containing larvae , and 21–23-day-old foragers returning to the hive carrying nectar or pollen ( these were standard behavioral assays and typical ages of bees performing these tasks [57] , [75] ) . We dissected fat body tissue from freshly collected bees and immediately performed cross-linking reactions and isolated nuclei from fat cells pooled from 8 individual bees . Chromatin immunoprecipitation was then performed on fresh material or nuclei stored at −80°C for up to 1–2 months using the EZ-ChIP kit ( Millipore , Billerica , MA ) according to standard protocols and a custom antibody specific to honey bee USP ( Figure S3 ) . We used custom genomic tiling microarrays ( Nimblegen , Madison , WI ) with 50 bp probes and 100 bp resolution designed from the A . mellifera genome sequence Assembly 4 . 0 . Each two-color array was hybridized with genomic DNA pulled down using the a-USP antibody and with input genomic DNA , and the binding intensity of USP was calculated as their ratio . Hybridization and data extraction were performed according to standard operating procedures by NimbleGen . We used the Mpeak [76] and Tamalpais algorithms to identify specific peak regions bound by USP and described the union of regions identified by these programs as putative USP binding sites . We validated a few of the USP binding sites in foragers ( using an antibody that recognizes a different part of the USP protein; Figure S3 ) and qPCR . We selected binding sites located upstream of the 5ht7 and abl genes ( peaks were located at A . mellifera Assembly 4 Linkage Group ( LG ) 7 . 14 38312–38952 , and LG 14 . 21 26426–26967 , respectively ) , as well as two negative control regions ( LG 1 . 17 1–4700; LG 1 . 21 1000–5000 ) . ChIP was performed using a-USP and ( control ) pre-immunization antisera . qPCR was performed using an ABI Prism 7900 sequence detector . Specific binding of USP at each peak region was quantified as the ratio of DNA pulled down by a-USP/pre-immunization antisera at each peak region , normalized by the average of the two negative control genomic regions . Three biological replicates were performed using fat body tissue from foragers collected from three different colonies . Bees from the two source colonies showing the strongest behavioral responses to usp RNAi were treated combinatorially with usp RNAi and JH analog treatments in a 2×2 factorial design . The JH analog methoprene was added into food during the third day of caging at a concentration of 20 mg/g food . Bees were killed by flash freezing at the end of the third day of caging . Fat bodies and annealing cuticle were dissected away from the gut after treatment with RNAlater-ICE ( Ambion ) . Total RNA was extracted from dissected fat body tissue using RNeasy kits ( Qiagen ) . We confirmed knockdown of usp by RT-qPCR , performed as previously described [77] , and we selected individuals showing typical knockdown for mRNA sequencing . mRNA libraries were constructed from 4 biological replicates per group using the Illumina ( San Diego , CA ) mRNA-seq protocol ( June , 2010 version ) with multiplex adapters . Each replicate contained pooled RNA from 4 individual bees . We synthesized 75 nt mRNA sequences using an Illumina Genome Analyzer IIx , with 4 indexed libraries in each lane , to a read depth of ca . 4–9 million reads/library . Library construction and mRNA sequencing were performed at the University of Illinois W . M . Keck Center for Comparative and Functional Genomics . Reads were mapped to the A . mellifera Pre-release 2 Official Gene Set using the Bowtie rapid alignment tool [78] . Reads mapping to genomic locations outside these gene models and unmapped reads were not included in analyses of differential gene expression . We also mapped reads to the A . mellifera genome , Assembly 4 , primarily for visualization . We found sequences mapping to 10406 OGS gene models , 9323 of which had >5 reads in at least one library; this is similar to the transcript diversity quantified in this tissue using microarrays [17] . We characterized differentially expressed genes by implementing a generalized linear model and Analysis of Deviance in the DESeq package in R [79] , accounting for the effects of colony , dsRNA , and JHA . Few genes were found to have a significant dsRNA x JHA interaction after accounting for variance outliers , so this term was removed from the final statistical model . We searched for binding sites using a compendium of 602 cis-regulatory motifs compiled from FlyREG ( D . melanogaster ) , TRANSFAC ( D . melanogaster , Homo sapiens ) , Jaspar ( H . sapiens ) , and from [80] ( D . melanogaster ) ( essentially as in [81] ) . We scanned each of the 1360 USP binding loci ( ChIP peaks ) for one or more matches to a motif ( say “M” ) , using the Patser program with default parameters . We repeated this for 1360 random genomic segments selected to match the lengths of the USP binding loci , thus obtaining a 2×2 contingency table of 1360×2 = 2720 sequences , categorized as “USP-binding” vs . “random genomic” and “M present” vs . “M absent” . A Fisher's exact test provided a p-value for this contingency table , which was used as a measure of the statistical significance of association between M and USP bound loci . We considered the 117 USP-binding loci located near genes that are differentially regulated in foragers or in nurses , categorizing them as being “induced in foragers” as opposed to “induced in nurses” . These same loci were also categorized as “M present” vs . “M absent” ( M being the motif ) , as described above , and the resulting 2×2 contingency table was subjected to a Fisher's exact test . For any two motifs M1 and M2 , we used Patser to predict sites , and categorize all adjacent pairs of heterotypic sites , over all 1360 USP-bound loci , as having inter-site distance < = 25 bp or >25 bp . We artificially constructed “background” data sets where each of the 1360 original sequences had the locations of its binding sites randomly shuffled , pooled together 50 such data sets , and categorized the adjacent pairs of heterotypic sites as having inter-site distance < = 25 bp or >25 bp . We compared the counts from the original data set with the counts from the artificially constructed background sets , using a Fisher's exact test . We generated both fat body and brain gene expression profiles from 60 bees after manipulations of diet quality or vg RNAi . To manipulate diet quality , groups of 35 1-d-old bees were placed into Plexiglas cages and fed either a rich diet containing pollen paste ( 45% pollen/45% honey/10% water ) and sugar syrup ( sucrose 50% w/v in dH2O ) , or a poor diet containing sugar syrup only [56] . To induce vg RNAi , 5 ug vg dsRNA [48] in 1 ul saline was injected into the abdomens of 1-d-old bees , compared to bees injected with saline alone , or mock manipulated; bees were placed into Plexiglas cages and fed a rich diet . In both experiments , bees were killed by flash freezing after 3 d and stored at −80°C . RNA was extracted from dissected fat bodies ( as above ) and from dissected whole brains [57] of bees from the diet quality and vg RNAi experiments . Sample processing , microarray procedures , and statistical analyses were essentially as in [69]; we used separate loop designs for each experiment and tissue , each replicated with a total of 10–20 bees/group . RNA from the fat bodies or brains of individual bees was subjected to one round of linear amplification and labeled with fluorescent dye ( Cy3 or Cy5 ) using the MessageAmpII kit ( Ambion ) in combination with a Universal Labeling System ( Kreatech ) . Labeled aRNA was hybridized to a custom , oligonucleotide microarray containing 28 , 800 oligos , including 13 , 440 duplicately spotted experimental probes , primarily based on gene models from the Honeybee Genome Sequencing Project . Slides were scanned with an Axon 4000B scanner , and images were analyzed with GENEPIX software ( Agilent Technologies ) . Expression intensity data were normalized using a Loess transformation implemented in Beehive ( http://stagbeetle . animal . uiuc . edu/Beehive ) . A linear mixed-effects model implemented by using restricted maximum likelihood was used to describe the normalized log2-transformed gene intensities values , including the effects of experimental variables , dye , bee , and microarray . Effects were evaluated with an F-test statistic and the P-values were adjusted for multiple hypothesis testing by using a FDR criterion . The effects of diet quality and vg RNAi on fat body gene expression were described previously [17] . For brain datasets , we removed from the resulting gene lists genes that are abundantly expressed in the hypopharyngeal glands , a potential source of contamination . Microarray data meet Minimum Information about Microarray Experiment ( MIAME ) standards and are available at ArrayExpress ( http://www . ebi . ac . uk/microarray-as/ae/ ) : accession numbers E-MTAB-495 ( effects of maturation and diet on fat body tissue ) , E-MTAB-507 ( effects of diet on brain ) , E-MTAB-490 ( effects of Vg RNAi on brain ) . The diet quality and vg RNAi experiments together included a total of 60 bees for which we profiled gene expression in both the brain and fat bodies . We generated expression estimates for these individual bees by implementing a linear mixed-effects model including the effects of dye , bee , and microarray ( but not experimental variables ) . We then merged all the individual bee estimates from each tissue and performed a quantile normalization to create a uniform dataset suitable for gene co-expression analysis . We used the following combination of established methods to identify gene co-expression modules that were shared between fat and brain ( called “coherent” modules ) , as well as co-expression relationships specific to the fat bodies . We first used Weighted Gene Co-expression Network Analysis ( WGCNA ) [67] to identify large groups of co-expressed genes in the fat bodies ( each of these modules contains >30 genes ) . We then applied TightCluster [68] to extract smaller clusters with the most tightly co-expressed groups of genes in each of the modules generated by WGCNA . Finally , we used CoherentCluster [68] to extract subsets of genes within these fat body-based tight clusters which were also co-expressed with each other in the brain; we call the sets of genes that are co-expressed with each other in both tissues “coherent” clusters . In addition , we identified clusters of genes that were co-expressed with each other only in the fat bodies but not in the brain; these fat-specific clusters consist of the portions of tight clusters that remain after removing genes identified in coherent clusters . We examined the statistical enrichment of clusters for differentially-expressed genes responding to USP , JHA , Vg , and maturation treatments using Fisher's Exact Tests and Chi-square tests .
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Animals use behavior as one of the principal means of meeting their basic needs and responding flexibly to changes in their environment . An emerging insight is that changes in behavior are associated with massive changes in gene expression in the brain , but we know relatively little about how these changes are regulated . One important class of gene regulators are transcription factors ( TF ) , proteins that orchestrate the expression of tens to thousands of genes . We discovered that ultraspiracle ( USP ) , a TF previously known primarily for its role in development , regulates behavioral change in the honey bee; and we show that USP causes behaviorally related changes in gene expression by mediating responses to an endocrine regulator , juvenile hormone . We present evidence that these effects on gene expression occur through combinatorial interactions between USP and other TFs , and that these hormonally related transcriptional networks are preserved between two tissues with causal roles in behavioral plasticity: the brain and the fat body , a peripheral nutrient-sensing organ . These results suggest that behavior is subserved by complex interactions between genes and gene networks , occurring both in the brain and in peripheral tissues . More generally our results suggest that molecular systems biology is a promising paradigm by which to understand the mechanistic basis for behavior .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"systems",
"biology",
"genome",
"analysis",
"tools",
"genetics",
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2012
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The Transcription Factor Ultraspiracle Influences Honey Bee Social Behavior and Behavior-Related Gene Expression
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Somatic hypermutation ( SH ) generates point mutations within rearranged immunoglobulin ( Ig ) genes of activated B cells , providing genetic diversity for the affinity maturation of antibodies . SH requires the activation-induced cytidine deaminase ( AID ) protein and transcription of the mutation target sequence , but how the Ig gene specificity of mutations is achieved has remained elusive . We show here using a sensitive and carefully controlled assay that the Ig enhancers strongly activate SH in neighboring genes even though their stimulation of transcription is negligible . Mutations in certain E-box , NFκB , MEF2 , or Ets family binding sites—known to be important for the transcriptional role of Ig enhancers—impair or abolish the activity . Full activation of SH typically requires a combination of multiple Ig enhancer and enhancer-like elements . The mechanism is evolutionarily conserved , as mammalian Ig lambda and Ig heavy chain intron enhancers efficiently stimulate hypermutation in chicken cells . Our results demonstrate a novel regulatory function for Ig enhancers , indicating that they either recruit AID or alter the accessibility of the nearby transcription units .
The appearance of point mutations within the rearranged immunoglobulin ( Ig ) genes of B cells , which leads eventually to the selection and production of high-affinity antibodies , is called somatic hypermutation ( SH ) [1] , [2] . SH requires transcription of the Ig genes [3] and expression of the activation-induced cytidine deaminase ( AID ) protein encoded by the AICDA gene [4] , [5] . AID is believed to initiate all three types of B cell–specific Ig gene diversification—SH , Ig gene conversion ( GCV ) , and Ig class switch recombination—by deaminating cytidines within the Ig loci [6]–[8] . While many non-Ig genes accrue mutations in AID-expressing B cells as a result of SH , Ig genes mutate at levels that are typically several orders of magnitude greater than those of non-Ig genes [9]–[12] . The question of how SH is preferentially targeted to Ig loci has been studied and debated for over 20 years . Pioneering experiments using chimeric gene constructs in transgenic mice indicated that sequences overlapping with the Ig light chain and Ig heavy chain enhancers distinguish the Ig genes as mutation targets [13]–[15] . Other early transgene studies indicated that Ig V region sequences themselves are not required for SH [16] and that active heterologous promoters can support SH [13] , [17] . However , further insight into the nature of the putative cis-acting regulatory elements was hampered by the laborious transgene experimental system , the relatively low mutation rates of the chimeric genes , and the fluctuation of mutation rates among transgenic lines , perhaps due to integration site effects and copy number variations . A further problem arose from the fact that the putative hypermutation-stimulating sequences included the known enhancers , making it difficult to differentiate between the effects of these sequences on transgene hypermutation versus transgene transcription ( reviewed in [18] ) . The hypothesis that SH is targeted preferentially to Ig genes by the Ig enhancers was subsequently called into question when germline deletions of individual murine Ig enhancers—the same sequences previously implicated in the hypermutation of chimeric transgenes—did not abolish SH within the respective loci [19]–[21] . It also became apparent that expression of either AID or the related cytidine deaminases APOBEC-3A or APOBEC-3B increased mutation frequencies in the genomes of fibroblasts [22] , Escherichia coli [23] , yeast [24] , and human breast cancer cells [25] . These findings and others ( reviewed in [9] , [18] ) raised widespread doubts about the relevance of specific cis-acting SH targeting elements in Ig loci . In particular , Ig enhancers were no longer regarded as likely SH targeting elements , and it was increasingly felt that they increased SH solely by increasing Ig gene transcription . Attention has recently focused on RNA polymerase II ( Pol II ) –associated factors that interact with AID and play roles in transcriptional stalling [26] and RNA processing [27] , processes that are likely to be critical for generating the single strand DNA substrate required by AID ( reviewed in [9] , [28] ) . However , these broadly acting factors do not provide a ready explanation for the strong preference that SH exhibits for Ig genes over non-Ig genes . Consequently , this has remained a central unresolved issue in the field . The chicken B cell line DT40 , whose genome is easily modified by targeted gene integration [29] , is a powerful model to investigate AID-mediated gene diversification [30] . DT40 variegates its rearranged Ig light chain ( cIgλ ) gene primarily by GCV [31] , but diversification occurs by SH if either upstream GCV donor sequences or uracil DNA glycosylase ( UNG ) are missing [7] , [32] . Evidence for the stimulation of cIgλ GCV by cis-acting sequences in DT40 has been detected by the analysis of endogenous cIgλ gene diversification [33] , transgene GCV [34] , and transgene hypermutation [35] . Reminiscent of the early experiments in transgenic mice , SH of a green fluorescent protein ( GFP ) knock-in transgene in DT40 cells depended on the nearby presence of a 10-kb fragment of the cIgλ locus , which was named diversification activator ( DIVAC ) [35] . Deletion analysis of DIVAC led to the identification of two core regions downstream of the cIgλ C-region that cooperate with each other and with other parts of the 10-kb sequence to stimulate SH of the adjacent GFP transcription unit [36] . However , a clearer definition of the DIVAC code proved challenging using the original GFP assay because of functional redundancy within the 10-kb sequence and difficulty in measuring the DIVAC activity of elements shorter than 500 bp [35]–[37] . Furthermore , murine Ig lambda ( Igλ ) and Ig kappa ( Igκ ) enhancer sequences displayed disappointingly low DIVAC activity in DT40 cells [36] , [38] . Hence , the identity of key SH targeting sequences and the extent to which these sequences have been conserved during vertebrate evolution have remained undetermined . We have now developed a highly sensitive assay that allows analysis of the SH targeting activity of small DNA elements , largely overcoming the shortcomings of previous experimental strategies . Using this new assay , we demonstrate that chicken , mouse , and human Ig locus enhancers and enhancer-like elements are core DIVAC sequences that work together to target SH . Regardless of which species they derive from , these elements rely for function on a common set of well-characterized transcription factor binding motifs , highlighting the evolutionary conservation of the SH targeting mechanism . These findings are likely to have implications for the mistargeting of SH to non-Ig genes and the origins of B cell lymphoma .
We previously developed an assay for DIVAC function that made use of a reporter cassette , termed GFP2 , consisting of a strong viral promoter driving expression of GFP and a drug resistance gene ( Figure 1A ) [35] . In this assay , GFP2 , with or without a flanking test sequence , was inserted by homologous recombination into the DT40 genome , and GFP expression was monitored in subclones by flow cytometry . Loss of GFP expression was entirely dependent on AID , was due to point mutations in GFP , and could be stimulated more than 100-fold by the presence of a strong DIVAC element adjacent to the GFP2 cassette [35] . Importantly , three previous studies demonstrated that DIVAC-dependent stimulation of GFP mutation was not accompanied by substantial changes in GFP transcription as measured by several methods , demonstrating that DIVAC stimulates SH by a mechanism independent of an increase in transcription [35]–[37] . To increase the sensitivity of the DIVAC assay , we modified the GFP2 reporter by the insertion of a 5′ untranslated sequence upstream of the methionine start codon and a hypermutation target sequence between the start codon and the GFP open reading frame , yielding the new reporter GFP4 ( Figure 1A ) . The 249-bp hypermutation target sequence consists of repetitions of TGG , CAA , and CAG codons frequently positioned in the context of SH hotspot motifs WRCY/RGYW ( W = A or T; R = A or G; Y = C or T ) . Transition mutations at the second or third position of the TGG codons or at the first position of the CAA and CAG codons will introduce nonsense mutations , precluding the translation of the GFP open reading frame ( Figure S1 ) . To further increase the frequency at which mutations and stop codons are generated , the GFP4 assay is performed in UNG-deficient cells , which accumulate exclusively C-to-T and G-to-A transition mutations and display a 7-fold increased rate of SH [32] , most likely because AID-induced uracils cannot be excised and repaired before replication . To assay DIVAC-GFP4 combinations at a defined chromosomal position , we generated a recipient cell line , UNG−/−AIDR/puro , in which ( i ) both endogenous UNG genes were disrupted and the coding sequences of both endogenous AICDA genes were deleted , ( ii ) AID expression was reconstituted by inserting an AICDA cDNA expression cassette under the influence of the β-actin promoter into one AICDA locus , and ( iii ) the position of the second AICDA locus was marked by a puromycin resistance gene . When this cell line is transfected by AICDA locus–targeting constructs containing DIVAC-GFP4 , targeted integrants into the marked AICDA locus are easily identified by the loss of puromycin resistance . Alignment of the cIgλ locus with the corresponding sequence of turkey , zebra finch , and ground finch revealed seven evolutionarily conserved sequence contigs downstream of the C-region ( Figures 1B and S2 ) . Two of these corresponded closely to regions we had previously demonstrated to be important for DIVAC function in the context of larger DNA elements [36]: the cIgλ enhancer ( cIgλE ) [39] and the 3′Core . The conserved sequence regions were cloned into the upstream DIVAC insertion site of GFP4 ( the default site used in all experiments except where indicated ) and transfected into UNG−/−AIDR/puro cells . Primary transfectants with targeted integration of a construct were subcloned , and 24 subclones were analyzed for GFP loss by flow cytometry 12 d after subcloning ( Figure 1C and 1D ) . Transfectants containing cIgλE or 3′Core , in either orientation ( reverse orientation indicated by “R” ) , showed median GFP loss levels of 20%–30% , whereas levels of GFP loss in transfectants of the other conserved sequences ( Con1–Con5 ) were close to the 1 . 7% median value observed in the no DIVAC control transfectant , UNG−/−AIDR . Interestingly , the Con2 sequence , which displayed activity close to background on its own , substantially increased GFP loss when combined with cIgλE in Con2+cIgλE cells ( 44 . 6% ) . The highest levels of GFP loss were seen when cIgλE and the 3′Core were combined ( 63 . 7% ) or when they were tested together with their intervening sequence ( cIgλE↔3′Core; 70 . 5% ) . Importantly , GFP loss in UNG−/−AID−/−cIgλE↔3′Core cells ( lacking the AICDA expression cassette ) was almost 3 , 000-fold lower than in cIgλE↔3′Core cells and about 60-fold lower than in UNG−/−AIDR cells . These results illustrate several points . First , the DIVAC-GFP4 assay is capable of detecting robust stimulation of SH by short DNA fragments , which heretofore has not been possible . Second , these results directly confirm the role of cIgλE and 3′Core as core DIVAC elements [36] . Third , in the absence of DIVAC , GFP loss from GFP4 in UNG−/− cells is 15- to 20-fold higher than we detect with GFP2 in wild-type cells ( see below , and [35] , [36] ) , likely reflecting both the increased sensitivity of GFP4 and an increase of DIVAC-independent mutations in the UNG-deficient background . Finally , in the absence of AID , UNG deficiency does not lead to substantial GFP loss , even in the presence of a strong DIVAC element . Hence , despite the repair-deficient context , both DIVAC-dependent and DIVAC-independent GFP loss in the GFP4 assay require AID . Sequencing of the hypermutation target region amplified from cIgλE↔3′Core cells 6 wk after subcloning revealed frequent transition mutations at G/C bases with a hotspot preference as expected for SH in UNG-deficient DT40 cells ( Figure S1 ) . Many of these mutations yielded stop codons , explaining the efficient GFP loss seen in cIgλE↔3′Core cells . cIgλE includes an E-box as well as NFκB ( nuclear factor kappa B ) , MEF2 ( myocyte-specific enhancer factor 2 ) , and PU . 1-IRF4 ( interferon regulatory family-4 ) binding motifs , all of which are remarkably conserved among avian species ( Figure S2B ) . Deletions starting either from the 5′ or the 3′ end of cIgλE progressively decreased GFP loss in the DIVAC assay ( Figure 2A and 2B ) . Once the 5′ deletions reached the NFκB motif ( 5′Δ37 ) , GFP loss fell to background levels . Similarly , 3′ end deletions including the IRF4 motif in 3′Δ49 cells strongly reduced GFP loss . The role of specific binding site motifs was further investigated by mutation of consensus residues in these sites ( Figure 2A and 2C ) . Whereas mutations in the NFκB , MEF2 , PU . 1 , or IRF4 motifs strongly decreased GFP loss , mutations in the E-box caused a more modest reduction , and a mutation in the spacer between the PU . 1 and IRF4 motifs was well tolerated ( Figure 2C ) . These results indicate that cIgλE requires the integrity of multiple transcription factor binding sites in its 5′ and 3′ halves for full activity . Little was known about 3′Core , the second autonomous DIVAC sequence of the chicken Igλ locus . Deletion of the first 42 and the last 99 bp did not affect GFP loss ( 5′Δ42_3′Δ99 ) , whereas many deletions in the central part of the fragment reduced GFP loss ( Figure S3A and S3B ) . Search algorithms for transcription factor binding motifs predicted , among others , six evolutionarily conserved binding motifs in the parts of 3′Core where deletions compromised activity: three E-boxes and three other putative sites , referred to as pCBF ( core binding factor ) , pC/EBP ( CCAAT enhancer binding protein ) , and pPU . 1 ( Figure S2C ) ( where “p” designates a putative binding site for which experimental evidence linking it to the factor is lacking ) . Deletion or mutation of any one of these motifs , with the exception of pPU . 1 , reduced GFP loss substantially , with the strongest effects seen for E-box2 , pCBF , and pC/EBP , which lie close together in the central part of the fragment ( Figure S3C and S3D ) . Thus , evolutionarily conserved transcription factor binding motifs are also critical for the DIVAC function of 3′Core . We note that many more sites were predicted in silico than were tested , and the factors that might bind to these and the tested sites , particularly pCBF and pC/EBP , remain unknown . Alignment of human , murine , and chicken Igλ enhancer sequences revealed striking conservation of the E-box and NFκB , MEF2 , PU . 1 , and IRF4 binding motifs [40] , [41] , while the mammalian sequences possess an additional E-box about 50 bp downstream of the PU . 1 site ( Figure S4A ) . Since the conserved transcription factor binding motifs were important for the DIVAC function of cIgλE , we reasoned that the mammalian enhancers might also be active DIVAC elements despite low sequence conservation of the intervening sequences . We began by testing the human Igλ enhancer ( hIgλE ) in either the upstream or downstream insertion site of GFP4 ( Figure 1A ) , which yielded a remarkable 46% GFP loss ( Figure 3B ) , almost twice the activity of cIgλE ( 27 . 2% ) . Removal of the upstream E-box in 5′Δ56 did not decrease DIVAC activity , whereas larger 5′ deletions reduced activity ( Figure 3A and 3B ) . However , even after removal of the upstream E-box , NFκB , and MEF2 sites , the 5′Δ84 fragment was still capable of supporting 23 . 6% GFP loss , almost as high as the activity of full-length cIgλE and much higher than the activity of the comparable deletion fragment ( 5′Δ59 ) of cIgλE ( Figure 2B ) . These results suggest that the 3′ portion of hIgλE contains important elements and that the downstream E-box might compensate for loss of the upstream E-box-NFκB-MEF2 sites . Consistent with this , a 3′ deletion including the downstream E-box ( 3′Δ46 ) reduced GFP loss to 20%—roughly the activity of full-length cIgλE—and a larger 3′ deletion removing the composite PU . 1-IRF4 site ( 3′Δ108 ) strongly reduced GFP loss to 6% ( Figure 3B ) , similar to the low activity of the comparable cIgλE 3′Δ68 fragment ( Figure 2B ) . In the strongly active hIgλE , point mutations in individual motifs reduced activity , although typically less than 2-fold , and only mutation of both components of the composite PU . 1-IRF4 site had a strong effect on activity ( Figure 3A and 3B ) . Therefore , hIgλE is both more active and apparently more robust than cIgλE , being less sensitive to mutation of individual motifs . The major difference between the human and chicken enhancers appears to lie in sequences in their 3′ portions . These results demonstrate , to our knowledge for the first time , a substantial conservation of DIVAC function from human to chicken sequences . They also reveal parallels between the enhancement of SH and the enhancement of transcription by the Igλ enhancer because the transcription factor binding sites long known to be important for the regulation of Igλ transcription [41]–[43] are also critical for DIVAC function . Sequence homologues of mammalian Ig heavy chain intron enhancers ( IgHEi ) could not be identified in birds , and an enhancer in the intron between the duck Jμ and Cμ segments showed no obvious conservation with mammalian counterparts apart from the presence of multiple E-boxes [44] . Human ( hIgHEi ) and murine ( mIgHEi ) enhancer fragments contain conserved YY1 ( yin yang 1 ) ( μE1 ) , E-box ( μE2 and μE4 ) , Ets1 ( μA ) , PU . 1 ( μB ) , IRF , and Octamer transcription factor binding sites , and less well conserved regions μE5 and μE3 [45] , [46] ( Figure 4A and S4B ) . Since these sites overlap substantially with those important for DIVAC function in cIgλE , 3′Core , and hIgλE , we reasoned that the mammalian IgHEi elements might also have SH targeting activity . Strikingly , hIgHEi and mIgHEi yielded high levels of GFP loss ( 62 . 1% and 47 . 3% , respectively; Figure 4B ) , well above that of cIgλE and 3′Core , and similar to that observed with hIgλE . To investigate the role of the well-known binding sites , hIgHEi was subject to deletion and mutation analysis . Whereas a 5′ deletion of hIgHEi including the μE1 , μE2 , μA , and μB sites only moderately decreased GFP loss in 5′Δ109 and 5′Δ136 cells , 3′ deletions including the Octamer , μE4 , and IRF sites strongly decreased GFP loss in 3′Δ67 and 3′Δ136 cells . Consistent with the importance of the 3′ part of hIgHEi , mutations of either the μE4 or IRF site strongly decreased GFP loss , whereas an Octamer site mutation had little effect . Thus , the binding sites in the 5′ portion , although able to boost activity of the 3′ portion , are unable to compensate for loss of the IRF or μE4 sites in the 3′ portion . We conclude that mammalian IgHEi sequences are potent DIVAC elements in chicken cells . Homologues of the mammalian Ig kappa chain ( Igκ ) enhancers are also not present in avian species , which contain only a single Igλ light chain locus . The three Igκ enhancers , intron ( IgκEi ) , 3′ ( IgκE3′ ) , and Ed ( IgκEd ) [47] , of mice and humans ( Figures 5A and S5 ) induced low or modest levels of GFP loss when assayed on their own ( Figure 5B ) , consistent with previous analyses [36] , [38] . However , when two Igκ enhancers were combined ( IgκEi+IgκE3′ or IgκE3′+IgκEd ) , GFP loss markedly increased , and when the three human Igκ enhancers were combined , GFP loss reached 50 . 9% ( Figure 5B ) . This shows that the known synergy of the Igκ enhancers with respect to the activation of Igκ transcription ( [47] and references therein ) also holds true for their DIVAC function , even in an avian B cell line lacking an endogenous Igκ locus . To confirm our results in a repair-proficient cellular context ( UNG-proficient DT40 cells ) and in a different genomic integration site ( the deleted rearranged Igλ locus ) , we tested various cIgλ DIVAC elements using the GFP2 assay . The full cIgλ DIVAC region ( the 9 . 8-kb W fragment that includes the rearranged VJλ region and all downstream cIgλ sequences ) yielded about 10% GFP loss using GFP2 ( Figure S6B and S6C ) , consistent with our previous study [35] . In general , the rank order of activities of DIVAC elements was similar between the GFP2 and GFP4 assays ( compare Figures S6C and 1D ) . Comparison of median GFP loss levels indicated that the GFP2 assay is approximately 20–50 times less sensitive than the GFP4 assay ( e . g . , for cIgλE and 3′Core , respectively: 27 . 2% and 33 . 2% median GFP loss with GFP4 , and 0 . 54% and 0 . 75% median GFP loss with GFP2 ) . However , with the cIgλE↔3′Core fragment , GFP loss in the GFP2 assay ( 6 . 7% ) was only about 10-fold lower than in the GFP4 assay ( 70 . 5% ) , probably because of saturation of the GFP4 assay in the presence of this highly active DIVAC element ( see Protocol S1 ) . We also used the GFP2 assay to confirm that Con2 ( which lacks activity on its own ) was able to substantially boost the activity of cIgλE ( Figure S6C ) . A limited deletion and mutation analysis of Con2 ( Figure S6A ) using the GFP2 assay ( Figure S6C ) and the GFP4 assay ( Figure S6D ) demonstrated that functional cooperation between Con2 and cIgλE required only the 3′ portion of Con2 and was dependent on one of the two putative IRF binding motifs ( pIRF-down ) in this region . We conclude that there is good congruence between the results of the GFP4 and GFP2 assays and that the less sensitive GFP2 assay is preferable for analysis of highly active DIVAC elements . The murine Igλ locus contains two enhancers , mIgλE3-1 and mIgλE2-4 , due to a duplication of a pair of J-C regions and their downstream enhancer ( Figure 6A ) [48] . These enhancers are relatively weak DIVAC elements on their own ( 0 . 4%–0 . 5% GFP loss in the GFP2 assay; Figure 6B ) , consistent with our previous analysis [36] . This suggested the need for other sequences in the locus to cooperate with mIgλE3-1 and mIgλE2-4 to support efficient SH of murine Igλ ( note that cooperation between mIgλE3-1 and mIgλE2-4 is not possible in some rearranged Igλ loci because rearrangement of upstream V2 or V3 gene segments to the JC3 or JC1 clusters deletes mIgλE2-4 ) . However , the identity of such putative cooperating elements was unclear because other murine Igλ enhancers were not known . Intriguingly , BLAST searches revealed the presence of IgλE homologues 20–25 kb downstream of mIgλE3-1 and mIgλE2-4 ( Figure 6A ) , which we refer to as mIgλE3-1s and mIgλE2-4s because of their resemblance to shadow enhancers [49] . The newly identified elements are 95% identical to one another and about 70% identical to the canonical enhancers , with the conservation including many of the transcription factor binding motifs shown to be important for DIVAC function of the chicken and human Igλ enhancers ( Figure S4A ) . When tested for DIVAC function , mIgλE3-1s and mIgλE2-4s were substantially more active than the canonical enhancers in both the GFP2 ( Figure 6B ) and GFP4 assays ( data not shown ) . Strikingly , the combination of a shadow enhancer with its neighboring canonical enhancer induced GFP loss strongly and synergistically ( Figure 6B ) , in the case of mIgλE2-4 plus mIgλE2-4s to levels almost as high as that seen for the entire cIgλ W fragment . These results reveal that strong SH targeting elements can be constructed from combinations of enhancers and enhancer-like elements in the murine Igλ locus , as is true also for chicken Igλ . Furthermore , they demonstrate our ability to identify strong DIVAC elements in the murine Igλ locus on the assumption that Igλ enhancer-like sequences activate SH . We extended this by investigating the activity of other combinations of elements , continuing to use the GFP2 assay . Consistent with the GFP4 data , hIgλE , hIgHEi , and the combined murine Igκ enhancers supported levels of GFP loss that were more than 20-fold above the background of AIDR cells ( 0 . 1% ) , whereas the 5′Δ84 deletion mutant of hIgλE was less active ( Figure 6C ) . Duplication of the truncated 5′Δ84 or the full-length hIgλE increased levels of GFP loss from about 0 . 6% and 2 . 4% to about 2 . 0% and 6% , respectively , showing that even the interaction between identical sequences can lead to a synergistic increase of DIVAC function , similar to the well-known effects of multimerization of enhancer sequences on transcriptional activity [50] . Consistent with previous studies of the GFP2 reporter [35] , [36] or modifications thereof [37] , mRNA levels from GFP4 were either not significantly or only marginally ( up to 2-fold ) increased by the presence of chicken or mammalian DIVAC fragments compared to the no DIVAC control ( Figure 7A–7C ) . Therefore , as with the GFP2 assay , DIVAC elements stimulate mutation in the GFP4 assay by a mechanism that is independent of an increase in GFP transcription . Given the relatively strong DIVAC function associated with the mIgλ shadow enhancers , we wondered whether they also possessed transcriptional enhancer activity . To test this , sequences were cloned downstream of a minimal promoter–luciferase reporter and transfected into the UNG−/−AIDR recipient cell line used for the GFP4 studies . Both mIgλE3-1s and mIgλE2-4s were able to stimulate luciferase expression above that of the empty vector ( no DIVAC ) control , but both exhibited significantly less enhancer activity than their canonical mIgλ enhancer counterparts ( Figure 7D ) , despite being stronger DIVAC elements . This discordance between transcriptional enhancer activity and DIVAC function further supports the conclusion that DIVAC operates by a mechanism distinct from that of stimulating transcription . A very recent study , published while our manuscript was under revision , identified the two mIgλ shadow enhancers based on epigenetic criteria and demonstrated that they possess B lineage–specific enhancer activity [51] .
Using a highly sensitive , well-controlled assay we provide conclusive evidence that SH is targeted by Ig enhancer and Ig enhancer-like sequences . The phenomenon is strikingly conserved during vertebrate evolution , as even short mammalian Igλ and IgH enhancer fragments raised mutation rates more than 20-fold in chicken cells . SH activating sequences , or DIVAC , not only physically overlap the Ig enhancers but also closely resemble transcriptional enhancers in their mode of action by ( i ) requiring multiple transcription factor binding sites , ( ii ) functioning independent of orientation and when positioned either upstream or downstream of the transcription unit , and ( iii ) increasing activity through the collaboration of multiple enhancer-like regions , each of which depends on transcription factor binding motifs . The recognition of Ig enhancers as SH targeting sequences yields a conceptual framework within which to reevaluate earlier studies . Most notably , the new results vindicate the early transgenic experiments that showed overlap of SH stimulating sequences with the Igλ , IgH intron , and Igκ enhancers [13] , [14] and synergistic effects between the Igκ intron and Igκ 3′ enhancer sequences [13] , [15] . The failure of either Igκ intron or 3′ enhancer knockouts in mice to abrogate hypermutation [19] , [20] is consistent with the contributions of multiple , partially redundant Igκ enhancers to DIVAC function . Similarly , the failure of a previous study to identify SH targeting function associated with the Igκ intron and 3′ enhancers in DT40 cells [38] was likely due to use of a less sensitive assay and the absence of the Igκ distal enhancer . In addition , evidence that E-box [37] , [52] , [53] , NFκB [34] , MEF2 [34] , and PU . 1-IRF4 [54] , [55] binding sites play a role in the targeting of SH or GCV can be explained by the importance of these sites within the context of Ig enhancers and enhancer-like sequences . The results presented here provide the foundation for models of the cis-acting regulatory regions that target SH to a variety of Ig loci . The chicken Igλ locus is best understood and offers several lessons that might be generally applicable . In cIgλ , the enhancer cooperates with an evolutionarily conserved downstream element ( 3′Core ) that itself possesses low levels of transcriptional enhancer activity ( Figure 7D ) but contains functionally important transcription factor binding motifs well known from Ig enhancers ( Figures S2 and S3 ) . However , it is clear that these two elements depend on additional sequences ( e . g . , Con2 and the region between cIgλE and 3′Core ) for full DIVAC function ( Figures 1 and S6 ) [35] , [36] . The mouse Igλ and human and mouse Igκ loci offer parallels , with DIVAC function involving the combined action of two or more well-separated enhancer or enhancer-like elements . By analogy with cIgλ , it is tempting to think that other surrounding sequences further contribute to the full SH targeting activity of mammalian Ig loci . The human Igλ enhancer , the human and mouse IgH intron enhancers , and a combination of the known Igκ enhancers increase SH 20- to 30-fold in our assays , well below the 100-fold stimulation achieved by the full cIgλ DIVAC ( Figure S6C ) . Indeed , previous analyses showing that deletion of mIgHEi or hIgHEi from the endogenous loci did not abolish SH [21] , [56] are consistent with the existence of other compensatory targeting elements , a strong candidate for which is the large 3′ regulatory region more than 200 kb downstream of IgHEi [57] , [58] . The identities of the trans-acting factors that bind Ig enhancers to stimulate SH are not known , although some candidates have been identified in previous studies and others can be inferred from the binding motifs whose integrity we show is important for DIVAC function . Substantial data support a role for E-box binding factors , including the E2a-encoded proteins E12 and E47 [53] . Disruption of E2a in DT40 cells reduced the frequency of SH/GCV [59] , [60] as did overexpression of the E protein inhibitors Id1 and Id3 [61] . E12 and E47 prefer to bind the CASSTG ( S = C or G ) subtype of E-box [62] , and while mutation of this subtype reduces DIVAC function , mutation of E-boxes predicted to be bound poorly by E12/E47 does also [37] . Existing data leave unresolved the identity of the E-box binding factor ( s ) that contribute to DIVAC function . Studies in DT40 have also implicated NFκB , PU . 1 , and IRF4 as trans factors relevant for the targeting of SH/GCV [34] , [55] . Despite the fact that transcription and hypermutation enhancers make use of overlapping binding motifs and likely an overlapping set of trans factors , our data provide a compelling argument that the two processes operate by distinct mechanisms and , in particular , that DIVAC does not operate by increasing transcription . It may not be a coincidence that enhancers , able to exquisitely regulate cell type– and gene-specific expression , have assumed the vital role of targeting SH to the Ig loci . The complex structure of DIVACs—distinct configurations of a common set of transcription factor binding motifs , with robust activity relying on multiple , and to some extent redundant , sequences—may reflect the formidable task of fine tuning and restricting SH . It might also reflect piecemeal evolution of DIVAC , with each Ig locus cobbling together an idiosyncratic collection of SH targeting elements . Chromosomal translocations near DIVACs likely increase the mutation rate in the neighborhood of the translocation breakpoint , as confirmed for the case of IgH to c-Myc locus translocations [63] . It is also possible that non-Ig genes like BCL6 that mutate at substantial rates in AID-expressing B cells [10]–[12] do so because of DIVAC-like sequences in their neighborhoods . In support of this , a recent computational analysis found that promoter-proximal E-box , C/EBPβ , and YY1 binding motifs ( all of which are found in some of the DIVAC elements identified here ) were predictive of off-target SH of non-Ig genes [64] . Little is known about how gene-specific enhancers and particularly Ig enhancers distinguish themselves from other enhancers that may contain the same or similar transcription factor binding sites . Despite this limitation in our understanding of enhancer function , plausible models for how SH is targeted to Ig genes can be formulated based on what is known about the interaction of enhancers with the transcription initiation complex ( Figure S7 ) . One possibility is that a DIVAC-bound factor or a combination of factors actively recruit AID . A not mutually exclusive alternative is that DIVACs induce changes in the Pol II transcription initiation or elongation complex , making the transcribed DNA more accessible to AID . This hypothesis might explain why the accumulation of SH events rises rapidly downstream of the transcription start site and then falls off exponentially [3] , [65] , and might establish a connection between DIVACs and stalled transcription [9] , [36] , [66] , [67] or RNA exosome complexes [27] . Interestingly , members of the APOBEC family can induce showers of clustered mutations in breast cancer and yeast cells that are believed to be related to single stranded DNA in the neighborhood of DNA double strand breaks [24] , [25] , setting a precedent for how a change in DNA conformation can target deaminases to particular regions of the genome .
The GFP4 cassette ( Figure 1A ) —which resembles GFP2 [35] but contains a 5′ untranslated sequence , the hypermutation target sequence ( Figure S1 ) , and , for increased GFP brightness , the GFPnovo2 open reading frame [68]—was custom synthesized ( Blue Heron Biotechnology ) and cloned into the BamHI site of an AICDA locus–targeting construct [69] , yielding the GFP4-containing , AICDA locus–targeting construct pAICDA_GFP4 . A variant of pAICDA_GFP4 , named pAICDA_GFP4D , was made in which the SpeI/NheI sites upstream of GFP4 were deleted and a unique NheI site was introduced downstream of GFP4 . The cloning of DIVAC sequences into the GFP4 or GFP2 targeting vectors is described in Protocol S1 . An UNG-deficient DT40 clone with both endogenous AICDA alleles deleted [32] was reconstituted with AID by the targeted integration of a bicistronic AICDA/gpt expression cassette into one of the AICDA loci [35] . The second AICDA locus was subsequently marked by the targeted integration of a puromycin resistance gene driven by the chicken β-actin promoter , yielding the recipient UNG−/−AIDR/puro cell clone for transfections of GFP4 targeting constructs . The ΨV−IgL− clone in which the rearranged Igλ locus was replaced by a puromycin resistance cassette [35] was used for transfections of GFP2 targeting constructs . DT40 cell culture , transfection , drug selection , and the identification of transfectants with targeted integration of GFP2 constructs were performed as described previously [35] . Transfectants with targeted integration of GFP4 constructs were also detected by the appearance of puromycin sensitivity . The AID-negative UNG−/−AID−/−cIgλE↔3′Core clone was derived from the cIgλE↔3′Core transfectant by cre recombinase–mediated removal of the LoxP-flanked AICDA/gpt expression cassette [70] . GFP expression from GFP2 transfectants was assessed by flow cytometry at day 14 after subcloning , as described previously [35] , [36] , whereas GFP4 transfectants were assessed at day 12 after subcloning . Details of the flow cytometry analysis are provided in Protocol S1 . Genomic DNA was isolated from a subclone of cIgλE↔3′Core after 6 wk of culture and used for the amplification of GFP4 sequences by PCR using Phusion polymerase ( New England Biolabs ) . The PCR fragments were cloned using the In-Fusion Cloning Kit ( Clontech ) into the linearized pUC19 provided with the kit and sequenced . Thirty-four sequences covering the first 500 transcribed bases of GFP4 were aligned to the GFP4 sequence to detect sequence variation ( Figure S1 ) . Orthologues of the Igλ locus were identified in the turkey , zebra finch , and ground finch genomes using the W fragment of cIgλ in low stringency blastn BLAST of the reference genome database and Blat genome searches of the respective genome sequences . BLAST and Blat searches were also used to identify the murine Igλ shadow enhancers and map them within the murine Igλ locus . The bird Igλ orthologues were aligned using the ClustalW2 web interface ( http://www . ebi . ac . uk/Tools/msa/clustalw2/ ) to detect sequence contigs conserved during avian evolution . ClustalW2 was also used to create the other sequence alignments shown in Figures S2 , S4 , and S5 . Searches for conserved transcription factor binding sites were performed using the TESS ( Transcription Element Search Software ) program [71] . Two-tailed unpaired t-tests were used to compare relative GFP transcript and luciferase levels in Figures S2D and S2E . Reverse transcription quantitative PCR analysis was carried out on transfectants containing various DIVAC-GFP4 constructs after the cells were treated with 4-OH tamoxifen and subcloned to delete the AID expression cassette . This avoided potential effects on transcript levels due to nonsense-mediated mRNA decay . The resulting AID-negative cells used for analysis were stably GFP-positive ( data not shown ) . RNA was extracted from 5×106 cells using the RNeasy Mini kit ( Qiagen ) , and the cDNA was prepared from 1 µg of RNA using the iScript cDNA synthesis kit ( Bio-Rad ) . Quantitative PCR was performed using the DyNAmo HS SYBR Green qPCR kit ( Thermo Scientific ) . GFP transcript levels were normalized to 18S rRNA levels . Samples were denatured for 15 min at 95°C , followed by 40 cycles of 30 s at 94°C , 30 s at 60°C , and 30 s at 72°C . The primers used were as follows: GFPup-F 5′-ggaatatactttgccaagaagcgtt-3′ , GFP5up-R 5′-accatcgttgccagaaccatt-3′ , GFPcds-F 5′-gagcaaagaccccaacgaga-3′ , GFPcds-R 5′-gtccatgccgagagtgatcc-3′ , 18S-F 5′-taaaggaattgacggaaggg-3′ , and 18S-R 5′-tgtcaatcctgtccgtgtc-3′ . RNA for Northern blot analysis was prepared from GFP2 or GFP4 cell lines with the RNeasy kit ( Qiagen ) or TRIzol reagent ( Invitogen ) . 10 µg of total RNA was run on a gel , transferred to a membrane , and hybridized with a GFP probe . The blot was then stripped and reprobed with a GAPDH probe as a loading control . Using Image Lab software ( Bio-Rad ) , bands were quantitated and normalized to the corresponding GAPDH signal , and values were presented relative to the GFP4 no DIVAC control . The probes were PCR-amplified DNA products made with the corresponding primers: GFPp-F 5′-accatggtgagcaagggcga-3′ , GFPp-R 5′-ctaggacttgtacagctcgtccatgc-3′; GAPDHp-F 5′-accagggctgccgtcctctc-3′ , GAPDHp-R 5′-ttctccatggtggtgaagac-3′ . Test sequences were cloned between SalI and BamHI sites downstream of the firefly Luc2 gene of the minimal promoter containing pGL4 . 23 vector ( Promega ) . 20 µg of the plasmid was co-transfected into UNG−/−AIDR/puro cells with 2 . 5–5 . 0 µg of pGL4 . 75 Renilla luciferase control vector ( Promega ) using the Amaxa Nucleofector kit V ( Nucleofector program B-023 ) ( Lonza ) . The relative activity of firefly luciferase to Renilla luciferase was determined using the Dual-Glo Luciferase Assay System ( Promega ) according to the manufacturer's protocol .
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During the B cell immune response , immunoglobulin ( Ig ) genes are subject to a unique mutation process known as somatic hypermutation that allows the immune system to generate high-affinity antibodies . Somatic hypermutation preferentially affects Ig genes , relative to other genes , and this is important in preventing catastrophic levels of general genomic mutations that could lead to B cell cancers . We hypothesized that this preferential targeting of somatic hypermutation is assisted by specific DNA sequences in or near Ig genes that focus the action of the mutation machinery on those genes . In this study , we show that Ig genes across species—from human , mouse , and chicken—do indeed contain such mutation targeting sequences and that they coincide with transcriptional regulatory regions known as enhancers . We show that combinations of Ig enhancers cooperate to achieve strong mutation targeting and that this action depends on well-known transcription factor binding sites in these enhancer elements . Our findings establish an evolutionarily conserved function for enhancers in somatic hypermutation targeting , which operates by a mechanism distinct from the conventional enhancer function of increasing levels of transcription . We propose that combinations of Ig enhancers target somatic mutation to Ig genes by recruiting the mutation machinery and/or by making the Ig genes better substrates for mutation .
|
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2014
|
Targeting Of Somatic Hypermutation By immunoglobulin Enhancer And Enhancer-Like Sequences
|
Visceral leishmaniasis ( VL ) is a deadly vector-borne disease . Approximately 90% of Indian VL cases occur in Bihar , where the sand fly , Phlebotomus argentipes , is the principal vector . Sand fly control in Bihar consists of indoor residual spraying ( IRS ) , the practice of spraying the inner walls of village dwellings with insecticides . Prior researchers have evaluated success of IRS-control by estimating vector abundance in village houses , but the number of sampling periods ( n = 2–3 ) were minimal , and outdoor-resting P . argentipes were neglected . We describe a large-scale field study , performed in 24 villages within two Bihari districts , during which P . argentipes were collected biweekly over 47-weeks , in cattle enclosures , houses , and outdoors in peri-domestic vegetation . The objectives of this study were to provide updated P . argentipes ecological field data , and determine if program-initiated IRS-treatment had led to noticeable differences in vector abundance . P . argentipes ( n = 126 , 901 ) relative abundance was greatest during the summer months ( June-August ) when minimum temperatures were highest . P . argentipes were most frequently collected from cattle enclosures ( ~46% total; ~56% blood fed ) . Many sand flies were found to have taken blood from multiple sources , with ~81% having blood fed on humans and ~60% blood feeding on bovines . Nonparametric statistical tests were determined most appropriate for evaluating IRS-treatment . Differences in P . argentipes abundance in houses , cattle enclosures and vegetation were detected between IRS-treated and untreated villages in only ~9% of evaluation periods occurring during the peak period of human-vector exposure ( June-August ) and in ~8% of the total observations . No significant differences were detected between the numbers of P . argentipes collected in vegetation close to the experimental villages . The results of this study provide updated data regarding P . argentipes seasonal abundance , spatial distribution , and host preferances , and suggest vector abundance has not significantly declined in IRS-treated villages . We suggest that IRS be supplemented with vector control strategies targeting exophagic , exophilic P . argentipes , and that disease surveillance be accompanied by rigorous vector population monitoring .
On the Indian subcontinent , researchers have made attempts to answer questions regarding aspects of P . argentipes field ecology , including: seasonality , spatial distribution , and host preference . Researchers have suggested that the abundance of P . argentipes is associated with ecological parameters such as temperature and precipitation [10] . Results of a more recent 12-month study conducted in three Bihari villages in the Saran district , during which 52 , 653 sand flies were trapped using light traps , suggest P . argentipes numbers are typically highest during the months when evening temperatures are warmest and precipitation is increased ( June , July , August ) and lowest during the coolest months ( January , February , December ) [11] . These results further suggested spatial distribution of P . argentipes was not limited to the inside of village dwellings , but also included outlying village vegetation . P . argentipes females are regarded as anautogenous , needing a blood meal to produce each batch of eggs [12] , and results of several studies suggest they blood feed almost exclusively on bovines ( cattle and domestic buffalo ) and humans within rural villages [13–17] . Because P . argentipes is believed to be endophilic and endophagic [18] , the vector control approach utilized for decades in Bihar has been indoor residual spraying ( IRS ) , a method of applying insecticide to the inner walls of village houses and cattle dwellings . Explicit data addressing the impact of IRS on P . argentipes is limited [19–21] and , therefore , the impact of IRS on field populations is largely unknown . A cluster randomized trial was conducted by [22] in which IRS was performed in village dwellings in India ( DDT ) , Nepal ( alpha-cypermethrin ) , and Bangladesh ( deltamethrin ) , during which pre-treatment light trap collections , performed over two consecutive nights in November , were compared with post-treatment light trap collections performed over two consecutive nights in April of the following year . One issue with this study design is the lengthy period ( ~5-months ) between IRS treatment and sample collection , during which multiple factors could influence sand fly abundance . The authors mention other caveats as being small sample sizes within sites , limiting the reliability of site-specific analysis , and the fact that the study was conducted under controlled conditions not easily applicable in a national vector control program . Given the latter limitation , other researchers have collected P . argentipes from treated and untreated households during program-initiated DDT spraying in India [23–26] . While the results of these IRS-evaluation studies are interesting and useful , we would argue that they all share two limitations 1 ) a minimal number of post-IRS P . argentipes collection periods and 2 ) a universal neglect of outdoor sand fly population sampling . Post-treatment P . argentipes collections during the previously mentioned studies were typically conducted at ~1-month and ~5-6-months post-treatment . P . argentipes have been collected in large numbers from peri-domestic vegetation [11] , and villagers sleep outdoors during warmer months [8] when vector abundance and biting rates are high [27] . Hence the exophilic , exophagic sand fly population should be monitored . Considering the sensitivity of vector abundance to environmental factors such as temperature and precipitation and the tendency P . argentipes to be captured in light traps positioned in vegetation and cattle enclosures [11 , 28–29] , we suggest the current IRS-treatment programs could be better evaluated if P . argentipes were collected at greater frequencies and if trap locations were diversified to include cattle enclosures and outdoor locations such as peri-domestic vegetation . In this paper , we describe a large-scale , 11-month field study , conducted in 24 villages in two VL-endemic districts in Bihar , India , in which sand flies were collected using United States Centers for Disease Control and Prevention ( CDC ) light traps ( Bioquip Products , Rancho Dominguez , CA , USA; John W . Hock Company , Gainesville , FL , USA ) during 24 collection periods conducted over 47-weeks . The objectives of this study were to 1 ) provide updated P . argentipes ecological data within villages in two Bihari districts; and 2 ) determine if relative P . argentipes abundance in IRS-treated villages was reduced when compared with untreated villages . By utilizing methods previously described by [11] and [17] , but using a much larger sample size , field-collected P . argentipes were used to estimate: spatial distribution ( cattle enclosures , houses , vegetation ) , temporal fluctuations in relative abundance , and host preferences of blood fed females . Additionally , we used IRS data for the study villages to compare relative P . argentipes abundance in IRS-treated and untreated villages during the 2016 season . The results of this study will provide 1 ) useful , current ecological information regarding P . argentipes seasonal abundance , spatial distribution , and host preference in two VL-endemic districts; and 2 ) an alternative means of evaluating IRS-treatment through biweekly vector monitoring , helping to determine whether integrated methods of vector management should be recommended in Bihar .
The study was conducted in the Saran and Muzaffarpur districts of Bihar , India . Saran and Muzaffarpur are adjacent districts located ~30 km northwest ( 25 . 8560° N , 84 . 8568° E ) ~60 km north ( 26 . 121736° N , 85 . 373700° E ) of Patna , respectively . Summers are warm with maximum temperatures often ranging from 35–40°C . The winter months ( December-February ) are typically much cooler [11] . The rainy season typically occurs from July-September and April is generally the driest month . All villages were part of a large-scale VL-incidence survey performed in 60 villages within each district ( n = 120 ) in 2015 . At the beginning of 2016 , 12 villages in each district were selected for sand fly collection ( n = 24 ) ( Fig 1 ) with the aim of collecting sand flies from February-December . Prior to study initiation , it was discovered that two rounds of IRS application had been performed within several villages in both districts in 2015 and that application would be repeated in 2016 . To evaluate the impact of IRS on vector abundance , we selected 8 IRS-treated villages and 4 untreated villages within each district ( n = 16; n = 8 ) for CDC light trap collection . The main criteria for village selection were a population of >1 , 000 villagers , confirmed cases of VL spanning 2013–2015 , and IRS-status ( treated or untreated ) . All villages shared similar bioclimatic and agricultural characteristics . Livestock ownership was common with cows ( Bos taurus , Bos indicus ) , domestic buffalo ( Bubalus bubalus ) , and domestic goats ( Capra aegagrus hircus ) prevalent in each village . Dwellings were constructed primarily of thatch , mud , brick , and/or concrete , consisting mainly of human houses and cattle enclosures ( cattle sheds , houses cohabitated by humans and bovine ) . At night , livestock were typically tethered to stakes , and were kept in rooms within cattle enclosures or outdoors adjacent to human dwellings and/or cattle enclosures . Local weather data including daily temperature ( °C ) , relative humidity ( % ) , and precipitation ( mm ) ( February 10-December 31 , 2016 ) were collected from the closest , attainable weather monitoring station , located at Jay Prakash Narayan International Airport ( VEPT ) in Patna [30] . CDC light traps were used to collect sand flies in study villages . Twelve ( 12 ) CDC light traps were set , in fixed locations , within each of the 24 study villages ( n = 288 trap-nights per collection week ) biweekly from February 10-December 29 , 2016 ( 24 collection periods ) . During each collection period , CDC light trapping was performed over two consecutive nights with 12 villages being sampled on night-1 and the remainder on night-2 . Collection was not conducted in January because adult sand flies are typically not active during this period [11] . CDC traps were set in randomly selected homes ( n = 4 ) , cattle enclosures ( cattle sheds or dwellings cohabitated by bovines and humans ) ( n = 4 ) , and outdoors in peri-domestic village vegetation ( n = 4 ) to better estimate P . argentipes spatial distribution . Traps were identified by a unique code and waypoints taken with a handheld GPS ( Garmin Etrex 30 , Olathe , KS , USA ) . Traps were positioned with the fan ~1 m above the ground [11] . In peri-domestic vegetation , CDC traps were fitted with protective lids to shield the mechanical components from falling debris or rain . Traps were set at ~18:00 and removed at ~06:00 the following morning . Trap catches were individually numbered , transported to the laboratory in Patna , and frozen at -20°C until further processing . Because dry-ice was not available within the state of Bihar , we were unable to compare the efficiency CDC light traps with CO2 traps . Permission was received from village residents prior to conducting light trapping . Individual CDC light trap catches were uniquely numbered and identified by trap location and date of capture . Captured sand flies were separated from other arthropods , counted , identified morphologically by species , sexed , and females confirmed as unfed , blood fed , or gravid . If sand fly species was uncertain , specimens were placed under a dissecting microscope and identified morphologically by observing the male genitalia and the female spermatheca , the latter which required dissection [31] . Molecular species confirmation of sand flies was performed routinely on a random sample of specimens from each village . Whole sand fly DNA was extracted individually according to manufacturing instructions using the DNAzol reagent ( ThermoFisher Scientific , Waltham , MA , USA ) . Individual sand fly DNA was amplified using forward primer 5’ -TCG AAT CTA TGG GTG GT-3’ and reverse primer 5’- CAC AAT CCC AAC CAC GAA G-3’ for the 18S rRNA target gene . Restriction endonuclease digestion was performed on the PCR product of 18S r RNA using HAE II and HAE III enzymes . Banding pattern after Gel electrophoresis confirmed the identification of P . argentipes [32] . After completing counts and identification , blood fed female P . argentipes were separated and placed into dry 1 . 5 ml centrifuge tubes and stored at -20°C . Sand fly heads were removed from the bodies prior to analysis . DNA was then extracted from the abdomen and thorax of blood fed sand flies . The cytochrome b gene region ( 344 bp conserved mitochondria gene ) was amplified using bio-tinilated universal primers designed earlier by [33] . Cow blood was used as positive control and sterile water as negative control . The amplified products were used in reverse line blotting hybridization as probes , followed by chromogenic detection . Immobilization , hybridization and detection were done according to methods of [33] . Previously developed source-specific probes for human , cow , buffalo , goat , and chicken were used to analyze the blood meal contents for the blood fed sand flies collected during this study . These techniques allowed for detecting multiple host species in a single P . argentipes blood meal . These procedures are described in detail by [17 , 33] . P . argentipes successfully analyzed by cytochrome b amplification and reverse line blotting were categorized by blood meal content ( human , bovine , goat , etc . ) and trap placement ( cattle enclosure , house , vegetation ) . A Pearson’s chi square ( X2 ) test was used to estimate dependence of blood meal content on the spatial distribution of blood fed P . argentipes ( trap placement ) ( p = 0 . 05 ) . P . argentipes distribution was further evaluated by comparing the relative abundance of all P . argentipes within trap placements . Differences between and within trap catches in cattle enclosures , houses , and vegetation in Muzaffarpur , Saran , and cumulative districts were assessed using Analysis of Variance ( ANOVA ) ( p = 0 . 05 ) followed by Tukey’s W procedure ( p = 0 . 05 ) . To estimate relative P . argentipes seasonal fluctuations in abundance , we compared overall P . argentipes seasonal abundance in Muzaffarpur and Saran and compared seasonal abundance in cattle enclosures , houses , and vegetation within each district . More specifically , we compared the mean P . argentipes per trap-night per month ( n = 11 ) and per collection period ( n = 24 ) within districts and trap placements . A nonparametric Sign test ( p = 0 . 05 ) was used to estimate whether changes in monthly and biweekly abundance were significantly different between districts and between trap placements within districts . That is , whether P . argentipes abundance tended to increase and decrease in relative unison throughout the calendar year . The specific 2016 IRS spray dates for each village were provided by CARE India ( Patna , Bihar ) ( S1 Table ) , written on the walls of the village dwellings by the applicators , and were also confirmed by the home owners . The inner walls of houses and cattle enclosures in IRS-treated villages had been sprayed with two rounds of alpha-cypermethrin ( 5% ) wettable powder ( WP ) at a rate of 25mg/m2 , and the dates of application varied on a village-to-village basis . The first round of IRS application ranged from April 1–May 30 in Muzaffarpur and April 6-June 2 in Saran ( S1 Table ) . The second round of IRS application ranged from August 20-November 14 and September 15-November 28 in Muzzaffarpur and Saran , respectively . All IRS-treated study villages received two rounds of IRS application in 2015 . None of the untreated study villages had received IRS-treatment in over three years . In response to insecticide resistance , alpha-cypermethrin ( a synthetic pyrethroid ) has replaced DDT in 15 Bihari districts [34] . Because CDC light trap collections were performed during on-going program-initiated IRS-treatment , we were unable to collect pre-treatment ( baseline ) data . We first compared the mean P . argentipes per trap-night per collection period ( ±SE ) in IRS-treated and untreated villages within each district . We were specifically interested in differences occurring during the months with the highest relative vector abundance ( June-August ) and , hence , whether the first round of IRS application may have influenced P . argentipes abundance during this period of peak human-vector exposure . Nonparametric methods deemed sufficient for most skewed analyses [35] , were considered most appropriate to analyze differences between IRS-treated and untreated villages . Hence , a Wilcoxon rank sum test was used to estimate differences in relative abundance of P . argentipes in IRS-treated and untreated villages ( p = 0 . 05 ) by district . One of the villages in Saran ( CI-210 ) , initially untreated during the first round in 2016 , received IRS application during the second round ( September 21 ) , due to newly reported cases within the village , and therefore was excluded from the statistical analysis after September . We additionally excluded February and December from statistical analysis due to low vector abundance .
During 24 collection periods , occurring over 47-weeks ( 6 , 349 trap-nights ) , a total of 155 , 908 sand flies were captured , counted , and identified ( S3 Table ) , of which 126 , 901 were P . argentipes ( Males = 76 , 904; unfed females = 44 , 133; blood fed females = 2 , 299; gravid ( no blood seen ) females = 3 , 565 ) ( Table 1 ) . Individual trap-night yields ranged from 0–3 , 248 P . argentipes . A total of 76 , 516 and 50 , 385 P . argentipes were caught in CDC light traps in villages in Muzaffarpur and Saran , respectively . Twenty-four thousand two hundred eighty ( 24 , 280 ) Sergentomyia spp . and 1 , 477 P . papatasi were also collected . In total , 38 , 583 and 30 , 236 female sand flies were identified by dissection and PCR analysis , respectively ( S1 Fig , S2 Fig ) . Three thousand two hundred twenty-four ( 3 , 224 ) female Grassomyia indica were also identified . Twenty six ( 26 ) females could not be identified . Of the 2 , 299 blood fed P . argentipes collected , a total of 1 , 583 were successfully analyzed using cytochrome b PCR and reverse-line blot analysis ( Fig 2 ) . Blood meal content consisted of human , bovine , and goat blood , exclusively . Approximately 60% P . argentipes blood meals were positive for more than one host species . Of all blood fed P . argentipes successfully analyzed , the majority were positive for human ( ~81% ) and/or bovine ( ~60% ) blood . Blood meal status was dependent on trap placement ( X2; p<0 . 0001 ) , primarily in cattle enclosures . Overall , blood fed female P . argentipes were most frequently collected in cattle enclosures ( ~56% ) followed by houses ( ~30% ) , and vegetation ( ~14% ) ( Fig 2 ) . As would be expected , more P . argentipes containing bovine blood meals were collected in cattle enclosures ( ~56% ) than in houses ( ~31% ) or vegetation ( ~13% ) . But more P . argentipes containing human blood meals were also collected in cattle enclosures ( ~55% ) than in houses ( ~31% ) . This greater dependence on cattle enclosures was observed in all P . argentipes blood meal content types except for mixed human , bovine and goat blood meals , which were collected primarily from houses ( ~50% ) , followed by cattle enclosures ( ~38% ) and vegetation ( ~12% ) . The overall P . argentipes spatial distribution was relatively similar to that of blood fed females , with more being collected from cattle enclosures ( ~46% ) followed by houses ( ~30% ) and outlying vegetation ( ~24% ) ( Table 1 ) . The mean P . argentipes per trap-night were determined to be significantly different between trap placements in Muzaffarpur ( ANOVA; p<0 . 0001 ) , in Saran ( ANOVA; p = 0 . 0018 ) , and in cumulative districts ( ANOVA; p<0 . 0001 ) . Differences within trap placements were estimated between cattle enclosures and vegetation in Muzaffarpur ( Tukey’s W; p<0 . 0001 ) , in Saran ( Tukey’s W; p = 0 . 0234 ) , and in cumulative districts ( Tukey’s W; p<0 . 0001 ) ; between cattle enclosures and houses in Saran ( Tukeys W; p = 0 . 0020 ) and in cumulative districts ( Tukey’s W; p = 0 . 0015 ) ; and between houses and vegetation in Muzaffarpur ( Tukey’s W; p = 0 . 0397 ) . The mean number of P . argentipes per trap-night per collection period in Muzaffarpur and Saran are indicated in Fig 6 and Fig 7 , respectively . As noted , the first round of IRS applications was conducted between early April and late May to early June in both districts . Results of collections suggest that IRS-treatment failed to prevent the P . argentipes abundance in IRS-treated villages from increasing post-treatment June-August . Additionally , they indicate that if P . argentipes abundance was decreased in IRS-treated villages , the decrease was only temporary . In Muzaffarpur , the relative P . argentipes abundance per trapping period in IRS-treated and untreated villages were similar ( Fig 6A ) . The most notable difference occurred during collection period-11 ( June ) in houses where the mean P . argentipes per trap-night in IRS-treated villages was markedly lower ( Fig 6C ) . However , the mean was comparable to the untreated villages during collection period-12 . In Saran , the mean number of P . argentipes in cattle enclosures suggested differences in P . argentipes abundance occurred in June ( collection periods-9 , 10 , 11 ) ( Fig 7B ) but standard error was sizable because of outliers in one village ( CI-210 ) , particularly during collection period-11 , and differences were less evident from collection period-12 onwards . Noticeable differences between P . argentipes abundance in IRS treated and untreated villages did not occur after the second round of IRS . Nonparametric means of statistical analysis were used to estimate differences in P . argentipes abundance in IRS-treated and untreated villages because of the large standard errors reported . In total , of 40 cumulative evaluation periods ( 20 collection periods x 2 districts ) and 120 trap placement evaluation periods ( 20 collection periods x 3 trap placements x 2 districts ) , significant differences were detected for 8 periods ( 20% ) and 10 periods ( ~8% ) , respectively ( S4 Table ) . Of 56 evaluation periods occurring June-August , statistical differences were detected in 5 periods ( ~9% ) , 4 occurring in June and 1 in July . Significant differences were most frequently detected during analysis of cumulative trap placements ( n = 8 ) followed by houses ( n = 5 ) cattle enclosures ( n = 4 ) and vegetation ( n = 1 ) .
We have collected abundant , contemporary ecological data from similar geographical locations in Bihar , which are suggested to produce more precise epidemiological models [60] . Our study is primarily an entomological field evaluation and in future studies the methods we implemented should be modified to include a baseline collection period and should be coupled with explicit epidemiological surveillance . Ecologically , our results suggest P . argentipes to 1 ) feed opportunistically on humans and bovines , 2 ) show a preference for cattle enclosures , and 3 ) be present outdoors in village vegetation . Because the majority of Bihari villagers sleep outdoors during periods when vector abundance is high , it is likely that many P . argentipes feed exophagically . This theory is supported by the lack of difference in P . argentipes abundance within IRS-treated and untreated villages , by previous field observations and blood meal analysis [11 , 17] , and through observing P . argentipes infesting cattle during peak biting periods . Logically , IRS can only be efficacious in reducing endophagic vectors , which reinforces a need for supplemental vector control practices to reduce outdoor feeding populations . Because P . argentipes feeds heavily on bovines , endectocide-treated cattle may provide an appropriate means of reducing vectors unexposed to IRS-treated dwellings . By considering a complimentary form of vector control to better target exophagic , exophilic P . argentipes and by conducting explicit , frequent P . argentipes collection in combination with active and passive case detection , we could expand upon both integrated vector management and disease-vector surveilance , two of the main VL-reduction strategies discussed by the WHO [8] . As a result , we should be able to better protect outdoor-sleeping villagers and better estimate the sustainability of VL-reduction through program-initiated vector control .
|
Visceral leishmaniasis is a disease caused by a deadly vector-borne parasite ( Leishmania donovani ) transmitted to man by phlebotomine sand flies . Indoor residual spraying ( IRS ) , performed within village dwellings , is the primary means of sand fly control performed in Bihar , India and more explicit methods of evaluating the success of control are warranted . A field-based study was conducted to collect ecological sand fly data for use in evaluating the effectiveness of IRS in reducing relative sand fly abundance . Results indicate that sand flies blood feed primarily on humans and cattle and are most frequently found within cattle enclosures . Results further suggest IRS-treatment has a limited impact on vector density . Our approach incorporates detailed evaluation of sand fly spatial distribution ( cattle enclosures , houses , vegetation ) , seasonal fluctuations in abundance , host blood meal preferences within Bihari villages , and dates of IRS performed within treated villages . Hence , this study provides an explicit means of monitoring vector populations and evaluating control measures in Bihar .
|
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2018
|
Bionomics of Phlebotomus argentipes in villages in Bihar, India with insights into efficacy of IRS-based control measures
|
A hallmark feature of Williams-Beuren Syndrome ( WBS ) is a generalized arteriopathy due to elastin deficiency , presenting as stenoses of medium and large arteries and leading to hypertension and other cardiovascular complications . Deletion of a functional NCF1 gene copy has been shown to protect a proportion of WBS patients against hypertension , likely through reduced NADPH-oxidase ( NOX ) –mediated oxidative stress . DD mice , carrying a 0 . 67 Mb heterozygous deletion including the Eln gene , presented with a generalized arteriopathy , hypertension , and cardiac hypertrophy , associated with elevated angiotensin II ( angII ) , oxidative stress parameters , and Ncf1 expression . Genetic ( by crossing with Ncf1 mutant ) and/or pharmacological ( with ang II type 1 receptor blocker , losartan , or NOX inhibitor apocynin ) reduction of NOX activity controlled hormonal and biochemical parameters in DD mice , resulting in normalized blood pressure and improved cardiovascular histology . We provide strong evidence for implication of the redox system in the pathophysiology of the cardiovascular disease in a mouse model of WBS . The phenotype of these mice can be ameliorated by either genetic or pharmacological intervention reducing NOX activity , likely through reduced angII–mediated oxidative stress . Therefore , anti-NOX therapy merits evaluation to prevent the potentially serious cardiovascular complications of WBS , as well as in other cardiovascular disorders mediated by similar pathogenic mechanism .
Williams-Beuren syndrome ( WBS [MIM 194050] ) is a developmental disorder with multisystemic manifestations and a prevalence of ∼1/10 , 000 newborns , caused by a segmental aneusomy of 1 . 55–1 . 83 Mb at chromosomal band 7q11 . 23 , which includes ELN ( coding for elastin [MIM 130160] ) and 25–27 additional genes [1] , [2] . The recurrent WBS deletion common to most patients is mediated by nonallelic homologous recombination between regional segmental duplications that flank the WBS critical region [3] . In addition to distinctive craniofacial characteristics and mild mental retardation with social disinhibition and hyperacusis , a hallmark feature of WBS is a generalized arteriopathy presenting as narrowing of the large elastic arteries [4] . Histological characterization of arterial vessel walls of WBS patients showed increased number and disorganized lamellar structures , fragmented elastic fibers , and hypertrophy of smooth muscle cells [5] . This large arterial vessel remodeling which is a consequence of abnormalities in vascular development , is thought to be responsible for the cardiovascular disease manifested in 84% of WBS patients [4] , [6] . Identical vascular features , most prominently supravalvular aortic stenosis , are also found in patients with heterozygous deletions or disruptions of the ELN gene , implicating elastin haploinsufficiency in this phenotype [5] , [7] . The arteriopathy is the main cause of serious morbidity in WBS , including systemic hypertension and possible complications such as stroke , cardiac ischemia , and sudden death [8] , [9] . Animal models provide further evidence for elastin deficiency as the main cause of cardiovascular disease in WBS , underscoring the prominent role of the elastic matrix in the morphogenesis and homeostasis of the vessel wall [10] . Heterozygous knockout mice with only one copy of the Eln gene reproduce many of the alterations observed in the WBS vascular phenotype [11] , [12] . Hypertension is a consistent feature of Eln+/− mice , associated with elevated plasma renin activity ( PRA ) and angiotensin II ( angII ) levels , that can be blocked by the administration of angII type 1 receptor ( AT1R ) antagonists [13] . In addition to direct effects on the vasculature , many of the cellular actions of angII are mediated by the activation of the NADPH-oxidase ( NOX ) , thus stimulating the formation of reactive oxygen species ( ROS ) . Evidence is accumulating that increased oxidative stress has a relevant pathophysiological role in cardiovascular disease , including hypertension , atherosclerosis , and heart failure [14] . In WBS , the dosage of the NCF1 gene , encoding the p47phox subunit of NOX , is a strong modifier of the risk of hypertension . Hypertension was significantly less prevalent in patients whose deletion included NCF1 , indicating that hemizygosity for NCF1 was a protective factor against hypertension in WBS . Decreased p47phox protein , superoxide anion production , and protein nitrosylation levels , were all observed in cell lines from patients hemizygous at NCF1 [15] . Reduced angII-mediated oxidative stress in the vasculature was the proposed mechanism behind this protective effect . Indeed , studies performed in Ncf1 knockout mice have revealed that p47phox is one of the major effectors of angII action . The administration of angII did not lead to increased superoxide production or blood pressure elevation in homozygous knockout animals , as it did in wild-type mice [16] . The aim of the present study was to evaluate whether oxidative stress significantly contributes to the cardiovascular phenotype of a mouse model for WBS , and whether reduction of NOX activity by genetic modification and/or by pharmacological inhibition might have a potential benefit in the rescue of this phenotype . By using non-invasive blood pressure measurements , histological , biochemical and molecular analyses , we have documented a negative correlation between NOX activity and the cardiovascular phenotype in a mouse model of WBS , as well as prevention of many of the manifestations by using anti-NOX therapies .
Previously reported mice bearing a heterozygous deletion of half of the orthologous region of the WBS locus ( 0 . 67 Mb , from Limk1 to Trim50 , including Eln ) , called DD , were used as a model for the WBS cardiovascular phenotype [11] , [17] . We confirmed the elevated systolic , diastolic and mean blood pressures of 16-weeks old DD mice , ∼40% higher than their wild-type littermates on average , without increased heart rate ( Table 1 and Table S1 ) . Hypertension was already present at 8 weeks of age and persisted throughout life ( Table S2 ) without reducing life-expectancy , since these animals have been kept alive for more than 2 years with no instances of early death or unexpected morbidity [11] . As previously reported [17] , body weight was significantly reduced for DD mice at all ages when compared to wild-type ( P<0 . 001 ) ( Table 1 ) . Post-mortem evaluation at 16 and 32 weeks revealed significantly larger hearts in DD mice , measured as the heart wet-weight relative to the body weight ( P<0 . 001 ) . The cardiac hypertrophy was associated with increased cardiomyocyte size both in left and right ventricles ( P<0 . 001 ) ( Table 1 , Tables S3 and S4 ) . Vascular histology and morphologic examination provided insight into the structure of the aorta . DD mice showed fragmented , disorganized and jagged elastin sheets when compared to wild-type vessels in sections of the ascending aortic wall stained with elastic VVG , as previously reported [11] . We also observed a significantly increased arterial wall thickness ( P<0 . 001 ) , with small changes in the number of lamellar units ( Table 1 ) . The expression of three genes encoding components of the angII biosynthetic pathway , angII precursor ( angiotensinogen , Agt ) , renin ( Ren ) and angII converting enzyme ( Ace ) , was increased 2 to 5 fold by qRT-PCR on mRNA of several tissues ( heart , aorta , lung and kidney ) ( Figure S1 ) . Accordingly , DD mice showed significantly elevated angII peptide plasma levels ( P<0 . 001 ) ( Table 1 ) . Plasma renin activity levels were , however , highly variable even within groups , thus preventing inter-group comparisons . The level of oxidative stress in ascending aortas was determined using two experimental avenues , quantifying the levels of superoxide anion and protein nitrosylation ( Table 1 ) . These assays demonstrated higher levels of oxidative stress in DD mice when compared to those in wild-type littermates . These results confirm the relationship between hypertension , elevated angII and increased oxidative stress in these mice . NCF1 gene dosage had been shown to modify the risk of hypertension in WBS patients [15] . Interestingly , while DD mice were consistently hypertensive , PD mice ( heterozygous 0 . 45 Mb deletion , from Gtf2i to Limk1 ) were normotensive and mean blood pressure in D/P mice ( harboring both deletions in trans ) was only slightly increased by ∼10% [11] . Although the Ncf1 gene is located outside the PD deletion ( Figure 1A ) , we investigated whether the expression levels of Ncf1 could be affected in these mice , by qRT-PCR in three different tissues and using Eln ( hemizygously deleted in DD and D/P and not deleted in PD ) as control . DD mice showed a ∼3 fold increase of Ncf1 mRNA , while the expression was reduced in PD animals , and elevated but only ∼2 fold in D/P mice , correlating with the blood pressure ( Figure 1B ) . The low basal Ncf1 expression in PD and the relatively lower ( compared to DD ) in D/P strongly suggest that there may be a cis regulator element controlling Ncf1 expression in the PD deletion . It also indicates that Ncf1 is a strong modifier for the cardiovascular phenotype secondary to elastin deficiency . We then investigated the expression of other genes related to the NOX system . On average , transcript levels of all genes but Cyba were significantly increased in DD animals with respect to wild-type . In contrast , they were not significantly different in PD mice , and D/P mice showed elevated expression of Nox2 along with the ∼2-fold increase of Ncf1 levels ( Figure 1C and Figure S2 ) . The elevated transcriptional NOX levels observed in DD mice could be the basis for the excessive ROS and protein nitrosylation documented in the aortic wall . We then crossed mice homozygous for a spontaneous loss of function mutation in Ncf1 [18] with DD mice , in order to generate double heterozygotes in trans ( DD/Ncf1− ) , resembling the genotype of WBS patients with lower risk of hypertension and deletions that include the NCF1 locus . At 16 weeks of age , DD/Ncf1− animals had normal blood pressure similar to wild-type littermates ( Figure 2A ) . AngII plasma levels were reduced with respect to DD mice ( P = 0 . 036 ) , although still elevated compared to wild-type values ( P = 0 . 002 ) ( Figure 2B ) , and were accompanied by a significant reduction of mRNA expression of the angII biosynthetic pathway genes ( Figure 2C ) . The hearts of DD/Ncf1− mice were 20% smaller than those of DD mice ( P = 0 . 039 ) , although they still were slightly larger than those of wild-type animals ( P = 0 . 046 ) ( Figure 2D and Table S3 ) . Heart size reduction in DD/Ncf1− mice was accompanied with a decrease in the size of the cardiomyocytes of the left and right ventricles ( P = 0 . 025 and 0 . 039 respectively ) . We also observed a reduction of the aortic wall thickness ( P = 0 . 007 ) , with a slight improvement in the organization of the elastin sheets ( Figure 2E ) . Expression of NOX-related genes was down regulated in DD/Ncf1− as compared to DD animals , reaching values similar to wild-type littermates . Consistently with these data , DD/Ncf1− mice showed significantly reduced levels of protein nitrosylation ( P<0 . 001 ) and superoxide anion ( P<0 . 001 ) in their ascending aortas compared to DD mice ( Figure 2F ) . The evidence for genetic complementation prompted us to investigate whether the treatment of DD mice with either losartan ( an AT1R antagonist ) or apocynin ( a NOX inhibitor ) could rescue the abnormal cardiovascular parameters . Both pre- and postnatal-onset treatments with losartan or apocynin corrected the elevated blood pressure levels seen in 16 week-old DD mice ( Figure 3A ) . Blood pressure control was associated with a significant reduction of angII plasma levels in all treated with respect to untreated DD mice , although the levels still remained higher than those of wild-type mice ( Figure 3B ) . Both drugs acted synergistically with the genetic reduction of Ncf1 gene dosage , as shown by the below normal values of angII in treated DD/Ncf1− mice ( Table S5 ) . The therapeutic effect was evident at the gene transcription level , since a significant reduction of transcripts encoding three angII biosynthetic pathway proteins was observed both in DD ( Figure 3C ) and DD/Ncf1− mice ( Table S6 ) . A reduction in ROS production was also noted in the ascending aortas of treated DD mice ( Figure 4A and Table S7 ) , as well as down regulation of several oxidative stress genes , including Ncf1 , Ncf2 , Nox2 and Nox4 ( Figure 4B and Table S8 ) . Either apocynin or losartan therapy also completely prevented the cardiac hypertrophy of DD mice . Treated DD animals displayed heart weights and cardiomyocyte sizes similar to those of wild-type counterparts , significantly smaller than those of the untreated DD group ( Figure 5A and 5B ) . A mild improvement of the arterial wall thickness was also evident in all animals treated with both medications , along with reduced elastic fiber fragmentation during histological observation ( Figure 5C and 5D ) . All evaluated parameters of cardiovascular phenotypic rescue ( blood pressure , heart size , vascular morphology ) persisted at 32 weeks of age in treated mice . We found a high proportion of fetal deaths ( ∼32% ) associated with the prenatal administration of losartan ( Table S9 ) . No difference in the expected Mendelian proportions was observed in the offspring of heterozygous crosses ( DD x Ncf1+/− ) . We also observed premature postnatal deaths in ∼15% of the treated mice with prenatal-onset losartan ( Table S9 ) , mostly due to renal failure before the age of sacrifice , with similar frequencies among genotypes . Our data are in agreement with previous reports contraindicating losartan in pregnancy due to its potential teratogenicity [19] , [20] . No specific genotype was associated with increased susceptibility to losartan toxicity . On the other hand , no instances of prenatal death or early postnatal complications were observed in apocynin treated animals , and no other complications were observed in any of the groups treated with postnatal onset . Note: The full dataset of clinical , morphological , biochemical and molecular parameters at the different time-points , including the effects of treatment on wild-type and DD/Ncf1− animals , is provided as supplementary information ( Tables S1 , S2 , S3 , S4 , S5 , S6 , S7 , S8 , S9 ) .
The majority of patients with WBS ( 84% ) manifest cardiovascular problems throughout their lives , particularly an arteriopathy consisting of stenosis of medium and large size arteries that can be present at birth [4] . Hypertension is found in 40%–70% of patients , even during childhood , and there is a significant risk of other cardiovascular complications , such as stroke , cardiac ischemia , and sudden death [4] , [6] , [8] , [21] , [22] , [23] , [24] , [25] . Surgical treatment of focal vascular lesions is required in ∼20% of cases and frequently relies on vascular grafts or balloon dilatation angioplasty [4] . Although β-adrenergic blocker and calcium channel blocker drugs have been utilized , there is insufficient evidence to recommend a specific drug therapy for hypertension [8] , [22] , [26] , [27] . Molecules that can either promote elastin biosynthesis or suppress vascular smooth muscle cell proliferation and migration , such as minoxidil [28] , glucocorticoids [29] , and retinoids [30] have been proposed as possible approaches to the treatment of cardiovascular disease , but none of them have shown to be clinically effective yet . Therefore , additional insight into the pathophysiology is needed to define better-targeted therapies as alternatives to current protocols to prevent the common complications of WBS arteriopathy . A recently developed mouse model with elastin deficiency ( DD ) has provided further insight into the cardiovascular disease of WBS [17] . DD mice develop morphological changes in the aortic wall ( thickening with disorganized elastin fibers ) leading to chronic hypertension and cardiac hypertrophy . As in the case of Eln+/− mice , hypertension of DD mice is related to elevated angII plasma levels , and we have also shown over-expression of several NOX-related genes ( Ncf1 , Ncf2 , Nox2/Cybb and Nox4 ) and significantly increased oxidative stress in these mice . AngII is an important physiological regulator of blood pressure and cardiac function , with hypertensive , growth , and remodeling effects mediated through AT1R . AngII acting through AT1R is also known to stimulate NOX generating ROS in a variety of cells [31] . Chronic infusion of angII in rats increases vascular NOX-derived ROS preceded by a prominent expression of the p47phox subunit of NOX in the vasculature and kidney [32] . Although some of the ROS serve as signaling molecules in the cells , excessive production is damaging and has been implicated in the progression of many disease processes . In WBS patients , deletions are almost identical in size and mediated by non allelic homologous recombination , but the deletion breakpoints determine whether a functional copy of the NCF1 gene is included or not in the deleted interval [33] . Patients with ELN deletion and only one functional NCF1 allele have a 4-fold decreased risk of hypertension compared with those with more than one copy of NCF1 [15] . Interestingly , mean blood pressure in adult D/P mice , combining DD and PD deletions , was only slightly increased by ∼10% , suggesting a modifying effect of gene ( s ) within or near the PD region on blood pressure in these mice [11] . In addition , the presence of the PD deletion somehow decreased Ncf1 expression , being likely the main modifier for the non-significant blood pressure increase in D/P mice . Similarly , by genetic crossing , we have demonstrated that the loss of a functional copy of Ncf1 in DD mice completely restored oxidative stress and plasma angII levels preventing development of serious cardiovascular anomalies . These data reinforced the idea that pharmacological NOX inhibition could be efficient in the treatment of DD mice . Genetic ablation or pharmacological inhibition of Nox4 has proven to have a remarkable neuroprotective role in a mouse model for cerebral ischemia [34] . Antioxidants could also decrease blood pressure in several models of hypertension with proven implication of angII and the redox system , acting to scavenge the ROS produced by NOX , but their clinical effectiveness is limited [35] . The AT1R blocker losartan is known to lower blood pressure and rescue vascular wall alterations in other connective tissue defects by inhibiting the TGF-β signaling [14] , [36] . Losartan inhibits the growth and remodeling effects of angII but also the NOX generated ROS , all mediated through AT1R . On the other hand , apocynin is a naturally occurring methoxy-substituted catechol , experimentally used as a more specific inhibitor of NOX with anti-inflammatory activity demonstrated in a variety of cell and animal models . In resting cells , p47phox is folded in on itself through intramolecular interactions between the autoinhibitory region and the bis-SH3 and PX domains . These interactions are destabilized by phosphorylated serine residues within the autoinhibitory region , allowing p47phox to adopt an activated open conformation . Apocynin is thought to inhibit NADPH-oxidase assembly by preventing phosphorylation of the autoinhibitory region of p47phox , along with some scavenger activity of hydrogen peroxide [37] . Despite the controversy about the specific mode of action to decrease NOX activity , apocynin has been successfully used in a mouse model to treat hypertension and faster arterial thrombosis [38] . We have evaluated the efficacy and safety of both drugs , losartan and apocynin , in our mouse model of WBS cardiovascular pathology using previously titrated dosages with prenatal and postnatal onset of the therapies [36] , [38] . Both treatments were highly effective in the prevention of the development of cardiovascular anomalies in DD mice . Similar effects were manifested by reducing the consequences of NOX activity , with almost complete control of hormonal and biochemical parameters in plasma and tissues , and resulting in normalized blood pressure and improved cardiovascular histology . There was an improvement in aortic wall thickness and architecture without complete reversion of the developmental anomalies secondary to elastin deficiency . Apocynin was as efficient as losartan in prenatal onset , with excellent tolerance and without secondary effects . However , a high proportion of fetal and premature postnatal deaths were associated with the prenatal administration of losartan , supporting its contraindication in pregnancy [19] , [20] . Both drugs had significant beneficial effects after postnatal onset of the intervention , with excellent tolerance and no secondary effects . In conclusion , both , losartan and apocynin , have significant efficacy in the treatment of the cardiovascular phenotype of a mouse model for WBS . Losartan is already approved for human use , while apocynin has been used by inhaler in some clinical trials [39] . The validation of apocynin for human use and the development of additional specific inhibitors of NOX are of great interest , given their potential therapeutic utility in some forms of cardiovascular disease [40] . We believe that these drugs merit evaluation as potential therapeutic agents to prevent the serious cardiovascular problems in human patients with WBS .
Animal procedures were conducted in strict accordance with the guidelines of the European Communities Directive 86/609/ EEC regulating animal research and were approved by the local Committee of Ethical Animal Experimentation ( CEEA-PRBB ) . Previously reported mice bearing a heterozygous deletion of half of the orthologous region of the WBS locus on chromosomal band 5G1 ( 0 . 67 Mb from Limk1 to Trim50 , including Eln ) , called DD for distal deletion , were used as a model for the WBS cardiovascular phenotype [11] , [17] . Mice with the proximal half-deletion of the orthologous WBS locus ( 0 . 45 Mb from Limk1 to Gtf1i ) , called PD , and the double mutants in trans ( with homozygous Limk1 deletion ) , D/P [17] were also used for some studies . Heterozygous DD animals were crossed with mice bearing a homozygous loss of function mutation of the Ncf1 gene ( B6 ( Cg ) -Ncf1m1J ) [18] to obtain double mutants in the first generation ( DD/Ncf1− ) , harbouring then a mutant allele ( DD deletion and Ncf1 mutation ) in each chromosome ( Figure 1A ) . All mice were bred on a majority C57BL/6J background ( 97% ) . Tail clipping was performed within 4 weeks of birth to determine the genotype of each mouse using PCR and appropriate primers ( See primer sequences in Table S10 ) . Fifteen different groups of mice ( 7–15 littermate animals per group , 5 groups per genotype: wild-type , DD or DD/Ncf1− ) , were used in this study for a total of n = 208 . The 5 groups per genotype corresponded to untreated animals ( NT ) , treated with losartan ( Coozar , MSD ) with prenatal ( LP ) or postnatal onset ( LN ) , and treated with apocynin ( Sigma ) with prenatal ( AP ) or postnatal onset ( AN ) . As previously described , drugs were administered in the drinking water with final concentrations of 0 . 002 g/day for losartan [36] and 2 . 5×10−4 g/day for apocynin [41] . In the groups of prenatal initiation , pregnant females started treatment at 14 . 5 dpc and therapy was continued throughout lactation . Postnatal treatments started at 7 weeks of age . In both cases , mice continued on oral therapy until 16 or 32 weeks of age , when they were sacrificed . Drinking water with drugs were refreshed every 3 days and protected from light by wrapping the drinking water container with aluminum foil . We recorded drinking volumes for untreated and treated mice in order to avoid any interference in the drinking water because of drugs supplement ( Table S11 ) . Systolic , mean , and diastolic blood pressure were measured in conscious male mice on three separate occasions by using a tail cuff system ( Non-Invasive Blood Pressure System , PanLab ) , while holding the mice in a black box on a heated stage . In order to improve measurement consistency , multiple sessions were performed to train each mouse . At least 12 readings ( 4 per session ) were made for each mouse ( n = 7–15 per group ) . Animals were sacrificed at two time points ( 16 or 32-week-old ) . Immediately following sacrifice , all the organs in the thoracic cage ( thymus , lung , heart and aorta ) were removed in block and fixed in 10% buffered formalin at 4°C for 16 hours . Hearts and aorta were dissected , washed , and weighed ( wet weight ) . Hearts and vessels were processed for paraffin embedding . Wall thickness and lamellar units were analyzed using 5 µm cross-sections of the ascending aorta ( transected immediately below the level of the brachiocephalic artery ) stained with Verhoeff-van Gieson ( VVG ) to visualize elastic lamina . Wall thickness at 10 different representative locations was measured and averaged by an observer blinded to genotype and treatment arm for each mouse . The number of medial lamellar units ( MLUs ) at 4 sites was assessed and averaged by 2 separate blinded observers . These axial cross-sections were imaged with an Olympus BXS1 microscope with epifluorescence and phase-contrast optics equipped with the Olympus DP71 camera , and images were captured with the CellB Digital Imaging system software . MLUs counting and wall thickness were quantified using Adobe Photoshop CS ( Adobe Systems ) . RNA was extracted from the visceral organs of the thorax by using TRIZOL reagent ( Invitrogen ) according to the manufacturer's instructions , followed by a second spin columns ( Qiagen ) purification . To avoid possible contamination of gDNA , all samples were analyzed before conversion to cDNA using standard PCR . In addition , primers were designed in different exons to avoid undesired amplification . cDNA was prepared from 1 µg total RNA using random hexamers and SuperScript II RNase H- reverse transcriptase ( Invitrogen ) . The expression of genes involved in the angII biosynthetic pathway ( Agt , Ren and Ace ) and NOX-related oxidative stress ( Ncf1 , Ncf2 , Nox2/Cybb , Nox4 , Cyba and Rac2 ) were evaluated by quantitative real-time PCR ( qRT-PCR ) . After diluting the cDNA ( from 1∶10 to 1∶100 , depending on the tissue ) , 5 µl were used as template for qRT-PCR using an ABI5700 thermocycler ( Applied Biosystems ) with the FastStart DNA Master SYBR Green Kit ( Roche ) and gene specific primers . Characteristics of primers are given in Table S10 . Amplification of the Rps28 transcript served as RNA control for relative quantification . Each sample and the corresponding negative controls for each pair of primers were analyzed in triplicate at least in two independent experiments . Threshold cycle values were set manually and analyzed using the comparative method [42] . Blood was collected from the mouse heart into EDTA tubes immediately after sacrifice . Plasma was collected after centrifugation at 1 , 500 g for 10 minutes and stored at −80°C until use . AngII levels were determined with the Renin Fluorometric Assay Kit Sensolyte 520 following the manufacturer's instructions . Formalin-fixed , paraffin-embedded transverse sections ( 5 µm in thickness ) were mounted on polylysine-coated glass slides . After blockade with 5% bovine serum albumin plus 0 . 1% Triton X-100 in phosphate-buffered saline overnight at 4°C , the sections were incubated for 90 min at 37°C with the fluorescent probe DHE ( Calbiochem , Darmstadt , Germany ) . In the presence of O2- , DHE is oxidized to ethidium , which intercalates with DNA , and yields bright red fluorescence . After washing with PBS plus 0 . 1% Triton X-100 , sections were mounted and visualized by fluorescence microscopy ( Olympus BX51 , Japan ) . DHE fluorescence intensity was analyzed with NIH ImageJ software ( v1 . 43 , April 2010;U . S . National Institutes of Health , Bethesda , MD ) as previously described [43] . The fluorescence intensity is proportional to the amount of superoxide anion . Thereafter , sections were incubated with 40 , 6-diamindino-2-fenilindol ( DAPI ) ( 300 nM ) for 5 min at 37°C , reactive with fluorescent blue , marking the interlayer between DNA base pairs of cell nuclei . DAPI staining of cell nuclei helps detect true DHE staining ( present in the nucleus ) versus nonspecific staining . The specificity of the immunostaining was evaluated by the omission of the dye ( negative controls ) . For the quantification of fluorescence , we also subtracted the background present in the negative control , in an attempt to eliminate any autofluorescence . All comparisons were made on cuts prepared with the same experimental conditions and the same day . The distribution of 3-nitrotyrosine residues , as an indirect marker of peroxynitrite ( ONOO- ) production , was evaluated by indirect immunofluorescence . In brief , arterial sections were blocked for 2 h at 37°C and incubated overnight at 4°C with a polyclonal anti-nitrotyrosine antibody ( dilution 1∶100; Chemicon International , Temecula , CA , USA ) . All data are presented as means ± SD . Statistical analysis was performed using ANOVA with a post hoc Bonferroni comparison between multiple groups . In specific cases of two-group comparisons we performed t-test . Values of p<0 . 05 were considered significant .
|
Williams-Beuren Syndrome ( WBS ) is a rare developmental disorder characterized by distinctive facial , neurobehavioral , and cardiovascular features , caused by a heterozygous loss of genetic material ( deletion ) at the 7q11 . 23 chromosomal band . Elastin protein deficiency , due to deletion of one copy of the ELN gene , is responsible for developmental anomalies in arterial wall remodeling , predisposing WBS patients to high blood pressure and other serious cardiovascular complications . We have previously shown that a fraction of WBS patients who lack a copy of the NCF1 gene , which codes for p47phox , a subunit of NADPH-oxidase ( NOX ) , have lower cardiovascular risk associated with decreased oxidative stress . Here , we used a mouse model of elastin deficiency to better define the contribution of NOX–mediated oxidative stress to the cardiovascular phenotype of WBS and to confirm the role of Ncf1 as a major modulator . In addition , pharmacological inhibition of NOX activation or synthesis with either losartan or apocynin significantly rescued the cardiovascular phenotype of these mice , suggesting that these drugs should also be evaluated in human patients .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"animal",
"models",
"medicine",
"model",
"organisms",
"genetics",
"biology",
"genetics",
"and",
"genomics",
"cardiovascular"
] |
2012
|
Reduction of NADPH-Oxidase Activity Ameliorates the Cardiovascular Phenotype in a Mouse Model of Williams-Beuren Syndrome
|
Hepatitis C virus ( HCV ) infection induces formation of a membranous web structure in the host cell cytoplasm where the viral genome replicates and virions assemble . The membranous web is thought to concentrate viral components and hide viral RNA from pattern recognition receptors . We have uncovered a role for nuclear pore complex proteins ( Nups ) and nuclear transport factors ( NTFs ) in the membranous web . We show that HCV infection leads to increased levels of cytoplasmic Nups that accumulate at sites enriched for HCV proteins . Moreover , we detected interactions between specific HCV proteins and both Nups and NTFs . We hypothesize that cytoplasmically positioned Nups facilitate formation of the membranous web and contribute to the compartmentalization of viral replication . Accordingly , we show that transport cargo proteins normally targeted to the nucleus are capable of entering regions of the membranous web , and that depletion of specific Nups or Kaps inhibits HCV replication and assembly .
Hepatitis C virus ( HCV ) is a positive strand RNA virus of the Flaviviradae family , a blood borne pathogen and a major cause of liver disease worldwide , with an estimated 170 million people infected [1] . Approximately 30% of chronically infected patients develop progressive liver disease , including cirrhosis and end stage liver disease [2] . HCV is now the leading indication for liver transplantation in North America [3] . Recruitment and rearrangement of host cell membranes has been observed during the life cycle of numerous viruses including hepatitis B virus , cytomegalovirus , and all positive strand RNA viruses [4] , [5] , [6] , [7] . In HCV-infected cells , these membrane structures , termed the membranous web , consist of numerous double membrane vesicles , as well as multivesicular units and lipid droplets surrounded by membranes [8] , [9] , [10] , which arise primarily from the endoplasmic reticulum ( ER ) as well as from other membranes derived from the secretory pathway . The membranous web constitutes a virally-induced organelle that has been shown to be a separate compartment from the cytoplasm [11] , [12] . Though the precise structure and function of the membranous web remains unclear , it is proposed to have a variety of functions including viral packaging and egress , and concentration and synchronization of viral replication and assembly . In addition , it has been proposed to facilitate avoidance of host cell cytoplasmic pattern recognition receptors ( PRRs ) [5] , [13] . Owing to their small genome size , some viruses hijack host proteins for their own purposes . However , in the case of HCV , which does not have an obvious nuclear component to its life cycle , it is difficult to reconcile the number of viral interactions with components of the nuclear transport machinery [14] , [15] , [16] , [17] , [18] , [19] , [20] . These nuclear transport components include soluble nuclear transport factors ( NTFs ) , many of which are members of a family of proteins termed karyopherins ( Kaps ) . Kaps bind nuclear localization/import signal ( NLS ) or nuclear export signal ( NES ) containing molecules in the cytoplasm or nucleus and escort these cargos across the nuclear envelope ( NE ) through passageways formed by large macromolecular structures termed nuclear pore complexes ( NPCs ) ( reviewed in [21] ) . Each NPC is comprised of ∼30 distinct proteins , called nucleoporins ( Nups ) , that form a cylindrical channel lined by Nups that facilitate movement of the NTF across the nuclear envelope . Studies examining a number of viruses have reported interactions between viral proteins and NTFs and/or Nups . In some cases , these interactions support nuclear functions of viral proteins or act to alter host cell nuclear transport [22] , [23] , [24] . However , in situations where the virus life cycle has no clear nuclear intermediate , such as with HCV , the function of viral protein interactions with NTFs or Nups is unclear [18] , [19] , [25] . For example , four of the ten HCV proteins have been shown to contain putative NLS sequences , and can enter the nucleus when mutated or produced outside of the context of viral infection , but only the core protein has been suggested to enter the nucleus of HCV-infected hepatocytes ( [15] , [17] , [25] , [26]; unpublished data ) . We have investigated the potential functions of Nups and NTFs in supporting HCV infection . Initially , we monitored the consequences of HCV infection on Nups . Following HCV infection , we observed an increase in cytoplasmic levels of various Nups and their recruitment to regions of cytoplasm containing HCV replication or assembly complexes . Consistent with these observations , we show an association between various HCV proteins and specific Nups , as well as the NTFs Kap β3/IPO5 and Kap α . These interactions appear to play a key role in the viral life cycle , as depletion of specific Nups or Kap β3/IPO5 inhibits HCV replication . Furthermore , we present data that support a model in which the critical function of the nuclear transport components in HCV replication and assembly occurs in the cytoplasm where they contribute to the structure and function of the membranous web .
Observations that several HCV proteins , such as core and NS5A , interact with nuclear transport factors are perplexing , given that these proteins are membrane associated and detected in the cytoplasm , where they participate in HCV replication or assembly . Core , for example , is detected in distinct regions of the cytoplasm and associated with membranes surrounding lipid droplets ( Figure S1A ) [10] . These regions of core concentration lie primarily within areas of the cytoplasm that contain the membranous web [10] . Consistent with this concept , regions of the cytoplasm containing the bulk of the core protein appear largely devoid of microtubules , presumably being excluded by the membranous web ( Figure S1B ) . NS5A and NS3 are also detected in the membranous web [10] and in these regions of microtubule exclusion ( Figure S1C , and data not shown ) . However , like several nonstructural proteins , NS5A and NS3 exhibit a broader cytoplasmic distribution outside the membranous web owning to their presence within the ER [10] , [27] . We postulated that interactions between HCV components and the nuclear transport machinery could contribute to cytoplasmic processes . Cytoplasmic functions for Kaps have been documented [28] , and in many cell types a population of NPCs ( termed annulate lamellae ) are present in the endoplasmic reticulum ( ER ) where they form transcisternal pores across parallel ER membranes similar to those in the NE . These cytoplasmic NPCs are transport competent , however , what roles they play are unknown [29] , [30] . We hypothesized that , during HCV infection , cytoplasmic NPCs might function in the membranous web . To investigate this idea , we first examined subcellular localization of various Nups in Huh7 . 5 cells infected HCV genotype 2a strain JFH-1 . In uninfected cells , immunofluorescence microscopy analysis using antibodies directed against various Nups , including Nup358 , Nup155 , Nup53 , Nup153 , Nup98 , and NDC1 , an integral membrane component of NPCs , revealed a punctate NE pattern representative of NPCs and cytoplasmic foci characteristic of annulate lamellae ( Figure 1A ) . In HCV-infected cells , a similar NE signal was observed , however cytoplasmic levels of each Nup were increased ( Figure 1A , 1B , and S2A ) . Strikingly , the cytoplasmic Nup signals often colocalized with core protein , notably around lipid droplets , and in regions of the cytoplasm with reduced microtubules and containing NS5A-positive membranes ( Figure 1A , S2B , S4 , and S6 ) . A spatial relationship between core and the Nups was further demonstrated by line graphs of fluorescence intensity through regions containing lipid droplets ( Figure S3 ) . Interestingly , this redistribution of Nups to cytoplasmic compartments was also observed in cells infected other positive-strand RNA viruses , including Hepatitis A virus and Dengue virus , suggesting that there may be a conserved role for cytoplasmic Nups in positive-strand RNA virus infection ( Figure S5 ) . We further evaluated the consequences of HCV infection on Nup localization and the physical proximity of Nups and HCV proteins using subcellular fractionation . Subcellular fractionation procedures have previously detected HCV proteins in membranes fractions with sedimentation characteristics similar to microsomes and mitochondrial-associated membranes ( MAM ) [31] , [32] , [33] . We also detected an enrichment of HCV proteins in similar membrane fractions but not in more rapidly sedimenting nuclei and mitochondria containing fractions ( Figure 1C ) . In uninfected cells , Nups are primarily detected in nuclear fractions , with the lower levels of these proteins detected in microsomes and the more rapidly sedimenting MAM fraction likely arising from annulate lamellae . Consistent with our immunofluorescence microscopy analysis showing a close association of Nups with core and the membranous web , we observed that HCV infected cells contained increased amounts of various Nups in the microsomal and MAM fractions together with core and the non-structural proteins NS3 and NS5A ( Figure 1C ) . By contrast , Nup amounts in nuclear fractions were unchanged in the HCV infected cells as compared to their uninfected counterparts . Barely detectable amounts of lamin B are seen in the MAM fractions of infected cells , perhaps reflecting a minor nuclear contamination of these fractions or the binding of lamin B to the Nups in these fractions . Nuclear contamination does not explain the increased levels of Nups in the MAM fraction as the Nup∶lamin B signal ratio is strikingly higher in this fraction as compared to the nuclear fraction . To investigate the molecular basis for the interactions of HCV core and other viral proteins with NPCs , we performed immunoprecipitation experiments . Two approaches were used to assess HCV protein-Nup interactions . To examine the interactions of core with NPCs , Nups present in a post-nuclear supernatant derived from HCV infected cell lysates were immunoprecipitated using a monoclonal antibody ( mAb414 ) that binds a shared epitope present in several Nups , including Nup358 , Nup214 , Nup153 , and Nup62 . Consistent with our immunofluorescence results , western analysis of the immunopurified Nups detected associated core protein ( Figure 2A ) . Similarly , we examined whether immune-purified HCV proteins were bound to Nups . As the purification of HCV proteins from infected cells was unsuccessful , we chose to introduce genes encoding individual HCV proteins tagged with a V5 epitope into HEK293T cells and immunoprecipitate the tagged proteins using anti-V5 antibodies . Western analysis of immunopurified core , NS5A , and NS4A detected Nups associated with a subset of these proteins ( Figure 2B ) . Nup107 and Nup153 , components of the NPC scaffold and attached filaments , were detected in association with HCV core and NS5A , while Nup358 , Nup214 , Nup98 , and Nup62 were not detected in these immunoprecipitates . Another component of the NPC scaffold , Nup155 , was detected in association with NS5A but not with the core protein . By contrast , we failed to detect any Nups bound to immunoprecipitated NS4A . The interactions of core and NS5A with Nups were further validated by immunofluorescence analysis of cells expressing genes encoding these tagged HCV proteins or the JFH-1 subgenomic replicon . In cells producing core or NS5A , regions of the cytoplasm containing these proteins also showed colocalizing Nups , including Nup155 and Nup98 ( Figure 2C and S6A ) ; moreover , these Nups appeared reduced at the NE . A similar phenotype was not observed in cells expressing NS4A , leading us to conclude that core and NS5A are among those HCV proteins that interact with Nups . We also examined the localization of several Nups in cell expressing the JFH-1 subgenomic replicon , which lacks the coding region for core through the NS2 protein of the HCV polyprotein . Cells containing the replicon develop membrane alterations similar to the HCV-induced membranous-web [34] . In these cells we detected increased cytoplasmic levels of Nup358 and extensive colocalization of Nup155 with membrane-associated NS5A ( Figure S7 ) . Consistent with these observations , in HCV infected cells , cytoplasmic Nup155 exhibited an ∼55% overlap with NS5A ( Figure S6B and S6C ) , and , more generally , the increased cytoplasmic NPC foci seen during infection occupy similar regions of the cytoplasm as membranous web-associated NS5A ( as revealed using anti-Nup98 antibodies; Figure S5B ) . Interestingly , some Nups that were recruited to the cytoplasmic membranes in the HCV infected cells were not altered in their distribution in the replicon containing cells , including Nup98 and NDC1 ( Figure S7 ) . These results imply that those HCV proteins missing from the replicon containing cells , such as core , are required for the recruitment of additional Nups to cytoplasmic membranes . The accumulation of Nups in the vicinity of HCV assembly sites could arise from redistribution of cellular pools , increased cellular levels of these proteins , or a combination of both events . To assess the potential contribution of increased Nup synthesis , we examined cellular levels of various Nup mRNA transcripts at time points after HCV infection of Huh7 . 5 cells ( Figure 3A ) . We found that mRNA levels of Nups composing the cytoplasmic filaments of the NPC ( Nup88 , Nup214 and Nup358 ) , and one that is part of the nuclear basket ( Nup153 ) were reproducibly elevated 1 . 5- to 2-fold four days after HCV infection ( Figure 3A ) in a manner that qualitatively paralleled increasing HCV RNA levels . In addition , Nup358 showed a reproducible biphasic pattern with an additional peak visible at 2 days after infection . By contrast , levels of transcripts encoding for several Nups that make up the scaffold of the NPC ( including Nup155 , Nup107 , Nup53 , and Nup205 ) and two associated Nups , Nup62 and Nup98 , showed little or no change during HCV infection . Consistent with the changes in transcript levels , quantitative analysis of a subset of these Nups by western blotting showed increased levels of Nup98 , Nup153 , and Nup358 , but not Nup155 ( Figure 3B ) . These results indicate that a subset of Nups are up-regulated during HCV infection while others show no statistically significant change . Thus , the Nups recruited to sites of viral assembly are likely to arise from both constitutive and HCV-induced Nup expression . To further understand the physical and functional basis for the interaction of HCV proteins with Nups , we considered the potential role of putative NLS sequences ( i . e . potential nuclear transport factor binding domains ) present in several HCV proteins , including core and NS5A . NLS sequences can bind Kaps , which , in turn , could mediate the interactions of the HCV proteins with Nups . Therefore , we tested whether these HCV proteins were capable of binding Kaps and whether this interaction was responsible for their binding to Nups . The NLS sequences in Core and NS5A are predicted to bind specific members of the Kap family: Kap β3/IPO5 and the Kap α/β1 complex ( [18] , [19] unpublished data ) . Thus , we immunoprecipitated V5-tagged core , NS5A , or NS4A from cell extracts and probed for associated Kap β3/IPO5 and the Kap α/β1 complex . Antibodies directed against Kap β3/IPO5 and Kap α detected these proteins in association with core and NS5A but not NS4A , which lacks a predicted NLS ( Figure 4A ) . These interactions could be inhibited by competing NLS-containing peptides . Treatment of cells with cell-penetrating Kap β3/IPO5-specific NLS peptides or the overexpression of a Kap α-specific NLS ( cNLS ) -containing reporter protein blocked the interactions of core and NS5A with these Kaps . However , disruption of Kap interactions with core and NS5A did not alter their binding to Nup153 , Nup107 , or Nup155 , showing that binding of these HCV proteins to Nups is not mediated by Kaps ( Figure 4A and S8A ) . Considering the redistribution of Nups observed in HCV infected cells , we examined whether the cellular distribution of Kap β3/IPO5 was also altered in these cells . In uninfected cells , immunofluorescence microscopy analysis detected Kap β3/IPO5 both within the nucleus and the cytoplasm . In HCV infected cells , a similar distribution was observed , however Kap β3/IPO5 appeared enriched in regions of the cytoplasm adjacent to , or occupied by , HCV core protein ( Figure 4B ) . This recruitment of Kap β3/IPO5 to centers of HCV assembly does not appear to arise from increased expression of the Kap β3/IPO5 encoding gene , as cellular levels Kap β3/IPO5 mRNA were not significantly increased in HCV infected cells ( Figure S8B ) . Similarly , levels of Kap β1 and Kap α mRNAs were also not significantly changed . To examine the relevance of interactions between HCV proteins and Nups or Kaps , we investigated the consequences of reducing cellular levels of specific Nups and Kap β3/IPO5 on HCV replication . Lentivirus expressing shRNAs were used to reduce levels of targeted proteins . Using this approach , mRNA and protein levels for each of the targeted genes were decreased by >60% in Huh7 . 5 cells by 4 days after lentivirus transduction ( Figure S9A and S9B ) with little effect on cell viability ( Figure S9C ) . Cells were coinfected with lentivirus and HCV and , 4 days post infection , intracellular and extracellular HCV RNA levels were determined using quantitative real-time PCR ( qPCR ) ( Figure 5A , 5B , and S9E ) . The results of these experiments revealed that intracellular levels of viral RNA were significantly decreased upon depletion of Nup98 or Nup153 ( Figure 5A and S9E ) , while reduced levels of Nup155 , NDC1 , or Kap β3/IPO5 , or treatment with lentivirus encoding a scrambled control sequence , had no effect . Consistent with these observations , quantitative western blotting revealed a decrease in HCV core protein levels in Nup98- or Nup153-depleted cells ( Figure 5C ) . In accordance with decreased intracellular viral RNA levels , cells depleted of Nup98 or Nup153 also showed similar decreases in the levels of secreted virus ( Figure 5B ) . Although Nup155- or Kap β3/IPO5-depleted cells showed no change in intracellular levels of HCV RNA , extracellular levels of secreted virus were decreased in these cells ( Figure 5B ) , suggesting a requirement for Nup155 and Kap β3/IPO5 at a post-replication stage of virus assembly or in viral egress . These divergent effects of Nup depletions on intracellular versus extracellular RNA levels suggest functions for Nups at different stages of the HCV infectious cycle . On the basis of our results , we concluded that at least a subset of Nups and Kap β3/IPO5 function in the production of secreted HCV . To further evaluate the relationship between the extracellular HCV RNA and the state of extracellular virus in the Nup and Kap depleted cells , cells were co-infected with HCV and lentivirus , and the infectious titer of virus in the medium was determined ( Figure 5D ) . Consistent with our results showing decreases in extracellular HCV RNA levels , we also observed decreases in HCV infectious titers following depletion of Nup98 , Nup153 , Nup155 and Kap β3/IPO5 ( Figure 5D ) . When normalized to the amount of released HCV RNA , we observed that , with the exception of Nup155 depleted cells showing a slight decrease in the specific infectivity of viral particles , none of the knockdown cells produced viral particles with a significantly lower specific infectivity than virus from control cells ( Figure S9D ) . Thus , we concluded that depletion of specific Nups decreases the efficiency of HCV replication and/or assembly but did not change viral particle infectivity . Our observation that depletion of Kap β3/IPO5 reduces levels of secreted virus led us to further examine the role of Kaps in HCV infection by inhibiting interactions with NLS-containing targets in vivo using synthetic NLS-containing peptides [23] , [35] . As discussed in the previous section , these peptides can disrupt interaction of HCV proteins with Kaps in cells , however , they do not significantly affect cell viability ( Figure S9F ) . As shown in Figure 5E and 5F , treatment of HCV infected cells with the Kap α-NLS peptides significantly decreased both intracellular and extracellular HCV RNA levels . In comparison , Kap β3/IPO5-NLS peptides resulted in only a slight decrease in intracellular HCV RNA levels , but a significant decrease in the levels of secreted virus ( Figure 5E and 5F ) . These data are similar to that obtained upon depletion of Kap β3/IPO5 ( Figure 5A , 5B , and 5D ) . Combined with the decrease in HCV titers observed upon depletion of Kap β3/IPO5 ( Figure 5B and 5D ) , the decrease in HCV titers following treatment of infected cells with NLS peptides provides further evidence that the nuclear transport pathways are important for viral infection and that , like Nups , different Kaps may contribute to distinct stages in the viral life cycle . The HCV-induced membranous web is thought to restrict access of cytoplasmic factors , such as pattern recognition receptors , from sites of HCV replication and assembly . The accumulation of Nups and Kap β3/IPO5 in the vicinity of HCV replication and assembly sites led us to investigate the potential role of the nuclear transport machinery in mediating access of molecules to compartments within the membranous web . We hypothesized that , like their role at the NE , the presences of nuclear transport factors within the membranous web could facilitate access of NLS-containing molecules , including NLS-containing HCV proteins such as core and NS5A , into regions within the membranous web through their interactions with Kaps . Therefore , we examined whether NLS-containing proteins could access regions of the membranous web in HCV-infected Huh7 . 5 cells using a chimeric gene encoding two tandemly repeated GFP proteins fused to a canonical SV40 NLS ( cNLS ) . The cNLS-GFP fusion protein accumulated efficiently in the nuclei of uninfected cells , with little or no signal visible in the cytoplasm or associated with cytoplasmic membrane structures ( Figure 6A ) . HCV infected cells also exhibited a robust nuclear accumulation of the cNLS-GFP fusion protein suggesting nuclear import was functional in these cells . However , in the HCV infected cells significantly higher levels of cytoplasmic signal were detected ( Figure 6C ) . Importantly , the cNLS-GFP fusion protein was not diffusely distributed throughout the cytoplasm , but rather it appeared in distinct regions of the cytoplasm that were adjacent to or occupied by core and NS5A ( Figure 6A and S10 ) . The concentration of the cNLS reporter in regions of the cytoplasm occupied by the membranous web are consistent with a role for Kaps and NPCs in regulating access to compartments within the membranous web . A key factor responsible for the accumulation of cargoes in the nucleoplasm is the small GTPase Ran , which , when bound to GTP , binds import Kaps and induces release of attached cargoes . Therefore , we examined whether in HCV infected cells Ran was present in the cytoplasm in addition to it normal concentration in the nucleoplasm . In uninfected Huh7 . 5 cells , Ran was detected primarily to the nucleus , consistent with previous studies [36] . However , in HCV infected cells , we detected a clear change in the localization pattern of Ran . While still present in the nucleus , infected cells contained cytoplasmic pools of Ran largely concentrated in multiple foci ( Figure 6B ) . Quantification of the fluorescent intensity in cytoplasmic and nuclear compartments of uninfected and HCV infected cells confirmed the increase in cytoplasmic Ran levels ( Figure 6C ) . This cytoplasmic localization of Ran further supports the conclusion that the nuclear transport machinery functions in the cytoplasmic compartment to support HCV replication/assembly .
The organization , composition , and functions of membrane structures induced by positive strand RNA viruses remain largely ill-defined . During HCV infection , it has been postulated that the membranous web functions in viral egress , concentration and synchronization of viral replication and assembly , as well as avoidance from host cytoplasmic PRRs [5] , [13] . All of these functions are thought to require the existence of a permeability barrier between the cytosol and the interior of the membranous web . Here we report that Nups accumulate in the membranous web at sites of HCV replication or assembly . Consistent with these observations , we detect various HCV proteins in association with specific Nups and Kaps . Importantly , these proteins play a role in HCV infection . Insight into the function of these interactions came from the observation that a reporter protein normally found exclusively in the nucleus is also targeted to regions of the cytoplasm occupied by the membranous web . We hypothesize that Nups and Kaps present in the virally-induced membranous web facilitate virus replication , in part , through their ability to sequester molecules , both host and HCV proteins , required for HCV replication and assembly ( Figure 7 ) . The existence of assembled cytoplasmic NPCs crossing ER membranes , or annulate lamellae , has been reported in many cell types [29] , [30] . These NPCs are capable of transporting NLS-containing cargo across the ER [37] , but the function they play in the cytoplasm is unclear . We have observed that , during HCV infection , multiple Nups are redistributed to cytoplasmic membranes enriched with HCV proteins ( Figure 1 , S2 , S3 , S4 , and S6 ) . Indeed , HCV infection resulted in the re-localization of all the Nups examined , representing most of the major subcomplexes of the NPC , to regions of the cytoplasm populated by HCV proteins . These results lead us to conclude that intact cytoplasmic NPCs , or derivatives of these structures , are present in areas of HCV replication or assembly . This concept is consistent with previous electron microscopy studies of HCV infected cells where various double membrane structures , topologically analogous to ER-like structures housing cytoplasmic NPCs , were detected in the membranous web [9] , [38] , [39] . Changes in the localization of Nups in HCV infected cells led us to investigate corresponding changes in mRNA transcript and protein levels . Our qPCR analysis of Nup and Kap transcript levels revealed that only a subset are elevated in HCV infected cells , indicating that Nups recruited to the membranous web are likely derived from both existing cellular Nup pools and increased synthesis . Several Nups have previously been observed to be up-regulated upon innate immune stimulation of specific cell types [40] , [41] . Similar innate immune activation has also been observed upon HCV infection leading to the possibility that the observed up-regulation of Nups in HCV infected cells may result from HCV-mediated immune activation [42] , [43] . However , this does not appear to be the case as treatment of Huh7 . 5 cells with various immune stimulants did not alter cellular levels of mRNAs encoding many of the Nups examined in this study ( Figure S11 ) . Thus , the increase in Nup levels observed following HCV infection likely occurs through a mechanism distinct from immune activation . Immunoprecipitation experiments revealed that several Nups associate with HCV core and NS5A . Additionally , the HCV channel forming protein , p7 , has previously been shown to interact with Nup214 in a yeast two-hybrid system [44] . Importantly , the interactions between Nups and HCV core or NS5A are not mediated by Kaps ( Figure 4A and S8A ) . Thus , their interactions are unlikely to reflect a transport intermediate where HCV proteins are moving as cargo through the NPC . Rather , these data are consistent with a direct association between HCV proteins and Nups . These interactions are predicted to contribute to the recruitment of specific Nups , as well as associated subcomplexes or assembled NPCs , to the forming membranous web . It is possible that the interaction of HCV proteins with Nups and kaps could potentially alter host cell nucleocytoplasmic transport in such a way that facilitates HCV replication . For example some viruses , including polio and influenza , inhibit nuclear transport by inducing Nup degradation , to improve viral replication [22] , [23] , [24] . However , it seems less likely that HCV proteins target Nups and Kaps for a similar reason as we do not detect degradation of Nups , and nuclear import of the cNLS-GFP reporter , while exhibiting some cytoplasmic localization , appears robust in HCV-infected cells ( Figure 1 , 6 , and S10 ) . Instead , we hypothesize that interactions of HCV protein and Nups and Kaps reflect two , potentially simultaneously acting , functions conducted by these proteins within the membranous web . One proposed role is based on the observation that a subset of Nups , including Nup107 and Nup155 , are structurally related to secretory vesicle coat proteins . These proteins have highly conserved domain structures and their interactions with membranes and membrane proteins is proposed to facilitate the convex membrane curvature of the pore membrane domains that connect the inner and outer nuclear membranes and attach to the scaffold structures of the NPC [45] . In addition , while lacking structural similarity to coat proteins , Nup153 interacts with the vesicle coat protein COPI and this association has been linked to the post-mitotic NE membrane assembly [46] . The association of Nup153 with HCV proteins and COPI is also intriguing in light of studies showing a role for the COPI coatomer complex in HCV replication [47] . Considering these observations , we speculate that Nups may be recruited to the membranous web , in part , to usurp their functions in contouring of membranes . Importantly , the curvature of membrane domains at sites of viral particle budding into the ER lumen is topologically similar to the pore membrane [48] , [49] . These ideas are consistent with the physical association of Nup155 and Nup153 with core and NS5A and the visible close association of these Nups with core enriched regions adjacent to lipid droplets ( Figure 1 ) . Our data also support a second , more conventional role for Nups within the membranous web as part of assembled or partially assembled NPCs and as regulators of Kap/cargo movement . We envisage that the membranous web-associated NPCs selectively allow NLS-containing molecules to access regions within the membranous web . Such a mechanism would explain several observations . For example , previous studies have identified , or inferred the presence of , NLS-like sequences or NTF binding domains in HCV proteins , including in core [17] , NS5A [15] , and NS3 [14] , and our own analysis has also revealed NLS-like sequences in these proteins and NS2 ( unpublished data ) . Moreover , previous studies have detected interactions between HCV proteins and Kaps [18] , [19] , [20] , and we have found that core and NS5A proteins interact with Kap β3/IPO5 and Kap α ( Figure 4 ) . However , since these proteins are ER-associated and detected in the cytoplasm during infection , we propose that these import signals function in the cytoplasm . A requirement for a transport regulatory mechanism within the membranous web is inferred by the observed compartmentalization properties of this structure , including several studies showing HCV RNA and proteins within the membranous web are resistant to RNase and protease treatment [12] . This physical separation is also revealed by the exclusion of tubulin from regions of the cytoplasm occupied by HCV proteins ( Figure S1B and S1C ) . Various HCV and host proteins synthesized in the cytosol must overcome this barrier to enter regions of the web where HCV replication and assembly occurs . We propose that NLS sequences within HCV proteins as well as several host-cell nuclear factors detected in the membranous web [50] , [51] function to facilitate movement of these proteins from the cytosol through NPCs positioned in the membranous web to regions of HCV replication and assembly . Importantly , cytoplasmic proteins lacking NLSs , such as PRRs , would be inhibited from accessing viral RNA; events potentially contributing to the ability of HCV to maintain a chronic infection . This concept of NPC-mediated transport functioning within the membranous web is directly supported by our observations that a cNLS-GFP reporter protein is visibly enriched in regions of the cytoplasm occupied by HCV proteins ( Figure 6 and S10 ) . Furthermore , the concentration of Kap β3/IPO5 in regions of the cytoplasm occupied by core ( Figure 4 ) supports a function for this NTF in the membranous web . Consistent with these proposed functions for the nuclear transport machinery within the membranous web , we observe that various Nups and Kaps are required for HCV production . For example , we detected an inhibition of HCV replication or assembly following depletion of Nup98 , Nup153 , or Nup155 ( Figure 5 ) . Depleted levels of mRNA and protein for each Nup appeared similar ( Figure S9 ) , however , the consequences of their depletion on HCV replication were not . While depletion of Nup98 or Nup153 reduced both intracellular levels of viral RNA and secreted virus , depletion of Nup155 led to a specific decrease in secreted virus but no significant change in intracellular levels of viral RNA . These results are consistent with Nup98 and Nup153 being required prior to or coincident with in HCV RNA replication . By contrast , Nup155 depleted cells show no defect in intracellular HCV RNA accumulation implying that Nup155 contributes to post-replication processes such as effective viral packaging or egress . These differential effects of Nup depletion remain to be further characterized and we envisage several potential scenarios that would explain these results . They may arise from the different functional roles of these Nups within NPCs , thus depleting individual Nups would lead to distinct changes in the functionality of the NPC , including alterations in the functions of specific transport pathways . In support of this idea , inhibition of Kap α transport by treatment with competitive peptides mirrors the effects of depleting Nup98 or Nup153 , namely decreasing both intracellular HCV RNA and secreted virus . Conversely , depletion of Kap β3/IPO5 , or competitive inhibition of its in vivo function with peptides , results in a phenotype similar to depletion of Nup155 , namely a decrease in secreted virus but no change in the intracellular levels of HCV RNA ( Figure 5 ) . Thus , distinct transport pathways may have functions at different stages of the HCV lifecycle , likely as defined by their cargos . Alternatively , the structural integrity and general transport functions of the NPC appear to be differentially tolerant to changes in the levels of individual Nups . This is revealed , for example , by depletion of NDC1 , where NPCs remain functional despite significant depletion [52] of what is thought to be an essential component of the NPC [53] , [54] . The presence of depleted , but functional , NPCs would explain our observation that depletion of NDC1 did not significantly alter HCV replication . Another possibility is that these Nups and Kaps also contribute to virus production through additional functions unlinked to transport . For example , as discussed above , Nups such as Nup155 likely influence membrane structure; moreover , various Nups , including Nup98 and Nup155 have been linked to the maintenance of chromatin structure and the regulation of transcription ( reviewed in [45] , [55] ) . Our model suggesting that Nups form functional NPCs within the membranous web implies NPCs are capable of functioning outside the confines of the nuclear envelope . Indeed previous studies have shown that annulate lamellae can transport NLS-coupled gold particles across the ER [37] . Moreover , a recent report demonstrated that NPCs are present at the transition zone of cilia in mammalian cells and that a transport mechanism similar to that of nucleocytoplasmic transport is utilized to transport proteins between the cilia and the adjacent cytoplasm [56] , [57] . Interestingly , these cilia associated NPCs appear to lack certain Nups suggesting they represent derivatives of the NE embedded structures . Whether the NPCs we have detected associated with the membranous web also represent a variant form of NPCs remains to be determined . This seems plausible as our replicon data imply that HCV proteins do not recruit intact NPCs to the membranous web but rather distinct HCV proteins may recruit different subsets of Nups . We can envisage that the concerted activities of the HCV proteins could promote assembly of NPCs or a variant form of this structure in the membranous web . The ability of HCV to exploit the functions of the Nups and Kaps for the purpose of creating an environment conducive to its replication and assembly may represent a mechanism widely used by positive-strand RNA viruses . For example , we have observed increased amounts of cytoplasmic Nup98-containing foci that likely represent NPCs in cells infected with hepatitis A virus and dengue virus ( Figure S5 ) . Consistent with this observation , electron microscopy studies have reported increased levels of annulate lamellae in hepatitis A virus infected cells [58] , as well as cells infected with Japanese Encephalitis virus and Rubella virus [59] , [60] , [61] . These results lead us to conclude that Nups represent a conserved target of positive-strand RNA viruses and , with greater mechanistic understanding , a potential target for antiviral intervention .
HEK293T , A549 , Huh7 . 5 , and Huh7 cells were maintained in DMEM ( Sigma ) containing 10% FBS ( Sigma ) . For HCV infection , Huh7 . 5 cells were seeded at a density of 2 . 5×105 cells/well in 6-well tissue culture plates , and 24 hrs after plating , they were infected with 3 RNA genome equivalents of a serially passaged JFH-1 strain of HCV . For HAV infection , Huh7 cells were grown to 70% confluence and infected with HAV/p16 virus at and MOI of 0 . 1 . Cells were analyzed by immunofluorescence 3 weeks after infection . For dengue virus infection , A549 cells were grown to a density of 2 . 5×105 cells/well in 12-well tissue culture plates , and infected with DENV-2 strain of dengue virus at an MOI of 1 . Dengue virus infected cells were examined by immunofluorescence 48 hrs after infection . Huh7 cells containing the JFH-1 strain subgenomic replicon ( encoding NS3 through to the C-terminus of the HCV polyprotein ) were maintained in DMEM containing 10% FBS and 500 mg/mL G418 . For immune stimulation experiments Huh7 . 5 cells were incubated over a time course of 24 hrs with human recombinant interferon alpha ( 1000 units/mL ) ( Intron A , DIN02238675 ) , human recombinant interferon gamma ( 500 units/mL ) ( PBL , 11500-1 ) , human recombinant TNF-α ( 100 ng/mL ) ( Sigma , T152 ) or γ-irradiated LPS ( 100 ng/mL ) ( Sigma , L7770 ) . Total RNA was extracted from cells using Trizol ( Invitrogen , 15596018 ) or from supernatants using a High Pure Viral Nucleic Acid Kit ( Roche , 11858874001 ) . Relative mRNA transcript levels were determined by real-time PCR using either SYBR-green or TaqMan master mixes . For details , see extended Materials and Methods . Immunofluorescence and western blotting were done as previously described [53] . Further details and a list of primary and secondary antibodies are provided in the extended Materials and Methods . Quantification of nuclear and cytoplasmic fluorescence levels was done using ImageJ . Cell outlines were determined using DIC images . The percent cytoplasmic colocalization of Nups with HCV core or the NS5A protein was calculated in ImageJ using the Manders overlap coefficients as previously described [62] . Pearson's colocalization coefficients were calculated as previously described using the JACoP plugin for ImageJ [63] . Subcellular fractionation was performed as previously described [31] , [33] . For details , see extended Materials and Methods . Expression constructs for production of HCV proteins were made using sequences from the H77 strain . DNA sequences encoding core ( amino-acid residues 1–191 ) , NS4A ( residues 1638–1711 ) and NS5A ( residues 1973–2416 ) were cloned into a pcDNA3 . 1/nV5-DEST expression vector ( Invitrogen , 12290010 ) using the gateway cloning system ( Invitrogen ) . The cNLS-GFP construct has been previously described [64] . Constructs were transfected into HEK293T or Huh7 . 5 cells using lipofectamine 2000 reagent ( Invitrogen , 11668019 ) and expressed for 48 hrs . HEK293T cells were transfected with pcDNA3 . 1/nV5-DEST plasmid alone or encoding HCV core , NS4A , or NS5A and proteins were immunoprecipitated using anti-V5 antibodies . Interactions were evaluated by western blotting using antibodies described in the extended Materials and Methods . Synthetic peptides including Kap α-NLS-penetratin , Kap β3/IPO5-NLS [23] , [35] , and penetratin [65] have been previously described . For details , see extended Materials and Methods . Lentivirus particles were produced using previously described viral packaging vectors [66] and pLKO . 1 vectors containing the various shRNA sequences ( Sigma ) listed in Table S2 . Lentiviral titers were determined by infecting HEK293T cells with serially diluted lentiviral stocks and selecting for transduced cells with Puromycin . For lentiviral transduction , 2 . 5×105 Huh7 . 5 cells were plated in 6-well tissue culture plates and infected with lentivirus at an MOI of 2 . Four days after infection , cells were harvested and transcript or protein levels were evaluated using qPCR or western blotting . A MTT ( 3- ( 4 , 5-dimethylthiazol-2-yl ) -2 , 5-diphenyltetrazolium bromide ) cell viability assay was used to determine cytotoxicity of shRNA expression or peptide treatment [67] , [68] . HCV particles were harvested from the culture media of cells coinfected with HCV and lentivirus . Media was then diluted to make viral stocks containing 1×105 HCV RNA copies/mL . These viral stocks were serially diluted and added to Huh7 . 5 cells grown in optical 96 well plates . 2 days after infection , viral focus-forming units were determined by indirect immunofluorescence microscopy using antibodies specific to HCV core protein . The values for specific infectivity were calculated by dividing the number of Focus forming units by the total number of HCV RNA copies added to the cells ( FFU/HCV RNA copy ) . The values for infectious titer represent the number of focus forming units per mL in the culture medium harvested from each of the coinfected samples . The values for specific infectivity and infectious titer show an average over a count of 6 wells , and each experiment was repeated 3 times . Nup53- NM_138285 , Nup62- NM_012346 , Nup88- NM_002532 , Nup98- NM_016320 , Nup107- NM_020401 , Nup153- NM_005124 , Nup155- NM_153485 , Nup205- NM_015135 , Nup214- NM_005085 , Nup358- NM_006267 , NDC1- NM_001168551 , Lamin B- NM_032737 , Kap β3/IPO5- NM_002271 , Kap α1- NM_002264 , Kap α6- NM_012316 , Imp β1- NM_002265 , IFNα- V00548 , IFNγ- V00543 , TNFα- NM_000594 , β Tubulin- NM_006000 , VAC- NM_003374 . 2 , FACL4- AB061713 . 1 , HPRT- NM_000194 . 2 , HCV JFH- HM049503 , and HCV H77- JX472013 .
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Hepatitis C virus ( HCV ) is a positive strand RNA virus and is a major cause of liver disease worldwide , affecting more than 170 million individuals . Infection of cells with HCV leads to rearrangement of cytoplasmic host cell membranes into viral replication and assembly complexes collectively known as the membranous web . This membranous web is thought to be involved in concentrating viral components and immune evasion , though the mechanisms by which these functions are achieved remains an important question in the field . Here , we report that nuclear envelope structures that transport macromolecules into and out of the nucleus , termed nuclear pore complexes ( NPCs ) , are also present in the membranous web of cells infected with HCV and other positive strand RNA viruses . Our results suggest that these NPCs function to regulate access of proteins into the interior of the membranous web , thus contributing to the establishment of an environment conducive to viral replication and viral immune evasion . Consistent with this idea , we show that NPC proteins are required for HCV assembly . Our discovery that nuclear transport proteins play a role in HCV replication , and potentially other viral infections , may lead to the discovery of new targets for antiviral therapies .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2013
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Hepatitis C Virus-Induced Cytoplasmic Organelles Use the Nuclear Transport Machinery to Establish an Environment Conducive to Virus Replication
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Trypanosoma brucei ( T . b . ) gambiense Human African trypanosomiasis ( HAT; sleeping sickness ) is a fatal disease . Until 2009 , available treatments for 2nd stage HAT were complicated to use , expensive ( eflornithine monotherapy ) , or toxic , and insufficiently effective in certain areas ( melarsoprol ) . Recently , nifurtimox-eflornithine combination therapy ( NECT ) demonstrated good safety and efficacy in a randomised controlled trial ( RCT ) and was added to the World Health Organisation ( WHO ) essential medicines list ( EML ) . Documentation of its safety profile in field conditions will support its wider use . In a multicentre , open label , single arm , phase IIIb study of the use of NECT for 2nd stage T . b . gambiense HAT , all patients admitted to the trial centres who fulfilled inclusion criteria were treated with NECT . The primary outcome was the proportion of patients discharged alive from hospital . Safety was further assessed based on treatment emergent adverse events ( AEs ) occurring during hospitalisation . 629 patients were treated in six HAT treatment facilities in the Democratic Republic of the Congo ( DRC ) , including 100 children under 12 , 14 pregnant and 33 breastfeeding women . The proportion of patients discharged alive after treatment completion was 98 . 4% ( 619/629; 95%CI [97 . 1%; 99 . 1%] ) . Of the 10 patients who died during hospitalisation , 8 presented in a bad or very bad health condition at baseline; one death was assessed as unlikely related to treatment . No major or unexpected safety concerns arose in any patient group . Most common AEs were gastro-intestinal ( 61% ) , general ( 46% ) , nervous system ( mostly central; 34% ) and metabolic disorders ( 26% ) . The overall safety profile was similar to previously published findings . In field conditions and in a wider population , including children , NECT displayed a similar tolerability profile to that described in more stringent clinical trial conditions . The in-hospital safety was comparable to published results , and long term efficacy will be confirmed after 24 months follow-up . The trial is registered at ClinicalTrials . gov , number NCT00906880 .
Human African trypanosomiasis ( HAT ) is one of the most neglected tropical diseases ( NTDs ) , suffering from a lack of optimal control tools and insufficient research efforts . It affects people in sub-Saharan Africa who often live in remote and/or insecure areas , with limited access to health care [1] . Owing to past and ongoing surveillance and control efforts of the National Control programmes from the affected countries supported mainly by WHO ( World Health Organisation ) , BTC ( Belgian Development Agency ) and MSF ( Médecins Sans Frontières ) , less than 10'000 HAT patients are currently reported each year [2] . However , funding to keep up an adequate level of control activities is nowadays increasingly difficult to obtain . Moreover , in almost all cases , HAT is fatal if untreated . T . b . gambiense accounts for 95% of currently reported HAT cases . The disease progresses from 1st stage ( infecting blood and lymph ) to 2nd stage ( affecting the central nervous system ) , which ultimately leads to severe sleep disturbances , other neurological and psychiatric disorders , coma and eventually death . Until 2009 , treatment for the 2nd stage of T . b . gambiense HAT was limited to melarsoprol , an arsenic derivative , or eflornithine . Treatment with melarsoprol is associated with high toxicity , is sometimes fatal ( mean 9 . 4% ( range 2 . 7–34% ) [3] ) , and displays high rates of failure in several geographic areas [4] , [5] , [6] , [7] , [8] , [9] . Although safer and more effective [4] , eflornithine monotherapy comes with burdensome treatment administration requiring 56 slow infusions administered every 6 hours over 2 weeks that are difficult to implement outside well-staffed hospital settings . A new treatment alternative , nifurtimox-eflornithine combination therapy ( NECT ) , was added to the World Health Organisation's Essential Medicines List ( WHO EML ) in 2009 for the treatment of 2nd stage T . b . gambiense HAT [10] , based on its efficacy and safety profile observed in a randomised controlled trial conducted in a well-defined study population [11] . NECT is easier to administer and requires fewer hospital resources than eflornithine monotherapy , with only 14 slow infusions administered every 12 hours for 1 week , with a concurrent 10 days oral treatment with nifurtimox . The needed quantity of eflornithine for NECT ( and consequently the drug production and transportation cost ) is 2 times lower than for the eflornithine monotherapy regimen . NECT now stands as the preferred first-line treatment for 2nd stage T . b . gambiense HAT . However , it is not yet the ideal HAT treatment and to enable its wider use in remote rural settings , financial and logistical barriers must be overcome , health care staff must be well trained , and , importantly , the safety profile under such conditions needs to be better known . This study aimed to further document the clinical tolerability , feasibility and effectiveness of treatment with NECT in field conditions , i . e . with less stringent inclusion/exclusion criteria and in a larger population including children , pregnant and breastfeeding women and patients with a HAT treatment history .
The primary objective was to assess the clinical response of NECT for the treatment of 2nd stage T . b . gambiense HAT in field conditions . Secondary objectives included assessing the incidence and type of adverse events ( AE ) , the feasibility of NECT implementation by the health facilities and the effectiveness of NECT at 24 months after treatment . Patient follow-up is still ongoing at the time of publication , therefore the current analysis deals with in-hospitalisation safety only . The primary outcome was the proportion of patients discharged alive from the hospital ( treatment facility ) . This was directly assessed by the site Investigators after treatment at the time of discharge . Secondary outcomes were frequency , nature , severity and relatedness of adverse events and adherence to treatment ( interruptions , cessations , dose deviations , length of hospitalisation ) . This was a multicentre , open label , single arm , phase IIIb study of the therapeutic use of NECT for treatment of 2nd stage T . b . gambiense HAT in the Democratic Republic of the Congo ( DRC ) . The study took place in two of the most endemic provinces , Bandundu and Kasai Oriental , at five HAT treatment facilities operated by the national HAT control program ( Programme National de Lutte contre la Trypanosomiase Humaine Africaine , PNLTHA ) : Bandundu , Dipumba , Katanda , Kwamouth and Ngandajika and one treatment facility operated by the BDOM/KIKWIT ( Bureau Diocesain d'Oeuvres Médicales ) and supervised by the PNLTHA: General Hospital of Yasa Bonga . All second stage HAT patients admitted to the treatment facilities and routinely diagnosed according to the national guidelines and who gave their Informed Consent for participation , were included in the trial . At inclusion , special attention was given to children and pregnant and breastfeeding women . It was under the Investigator's decision to include these sub-populations . Exclusion criteria were inability to take oral medication and impossibility to use a nasogastric tube , treatment failure after previous NECT treatment or any other condition for which the Investigator judged that another treatment was warranted . The patients were hospitalised and treated with NECT and monitored for adverse events . During the trial , an independent Data Safety Monitoring Board ( DSMB ) reviewed the data for patient safety . No interim analyses were done . Prior to NECT initiation , the patients received standard pre-treatment ( according to the facilities' guidelines , usually antimalarial , anthelminthic and antipyretic/analgesic medication ) . All patients received NECT [11] , the co-administration of nifurtimox ( oral 15 mg/kg/day , three times a day ) for 10 days and eflornithine ( slow intravenous infusions , 400 mg/kg/day , twice a day ) for 7 days . All patients , including the children and offspring of pregnant or breastfeeding women , were monitored during treatment and followed-up for safety issues . Patients underwent daily evaluations , including vital signs , physical examination , adverse event monitoring , and recording of concomitant medications throughout the admission and treatment period , as well as at the end of treatment , just before patients were discharged from hospital . The severity of the treatment emergent adverse events was graded by the Investigator , according to the Common Toxicity Criteria for adverse events ( CTCAE , v03 [12] from grades 1 to 5 as mild , moderate , severe , life-threatening and death ) and related to the treatment according to the Investigator's analysis . Treatment emergent adverse events were evaluated as being probably , possibly or not related to the study drugs . Follow-up assessment visits were scheduled 6 , 12 , 18 and 24 months after the end of the treatment . Twenty-four months after end of treatment , patients will be assessed for the final effectiveness of treatment; these results will be reported later . According to available literature [4] , [11] , [13] , [14] , the proportion of patients discharged alive from hospital ( P1 ) was expected to be equal or superior to 98% . The proportion of patients discharged alive from hospital ( under the null hypothesis ) was assumed to be equal to P0 = 96% , which corresponds to the average of proportions obtained in previous studies on eflornithine . The sample size was calculated according to a confidence interval approach with a precision of 2% . According to Fleiss' method [15] , a sample size of 620 achieved 80% power to detect a difference ( P1-P0 ) of 2% using a two-sided binomial test . The target significance level was 5% . Sample size was performed using PASS 2008 [16] . The main analysis set was the safety population , which included all subjects who received at least one dose of study drug . For this trial design , safety and ITT ( intention to treat ) populations are the same . As the percentage of patients who had protocol deviations was very small ( 0 . 7% ) , the per-protocol population was not analysed . All statistical evaluations were descriptive , as the aim of the study was to further document NECT implemented in field conditions and the trial was open-label and uncontrolled . Means , standard deviations and number of patients were provided for continuous variables , as well as frequency distributions for binary and categorical variables . An exact Wilson 95% confidence interval was calculated for the primary outcome . Statistical analyses were performed using the SAS software version 9 . 1 ( SAS Institute , Cary , NC ) . All data were captured at the participating treatment facility on patient case report forms ( CRFs ) . Data were double-entered and discrepancies reviewed and corrected against the hard copy CRF . Adverse events were coded with the MedDRA dictionary ( version 11 . 0 [17] ) and concomitant treatments with the WHO-Drug dictionary [18] . This research was conducted in full accordance with the ethical principles for medical research involving human subjects , as expressed in the Declaration of Helsinki and following amendments . Eligible patients were asked to meet the study Investigator or his delegate , who explained the study protocol in detail according to the patient information sheet , and requested written consent from the patient or , in case of minors , severely ill or mentally impaired patients unable to fully consent , from her/his parent ( s ) /guardian ( s ) . Whenever possible ( depending on age and level of understanding ) , the children received the information and their assent was obtained . Two Ethics Committees approved the study protocol: the Ethics Committee of both cantons of Basel ( EKBB , Basel , Switzerland; 26 February , 2009 ) and the local Ethics Committee in the DRC ( Comite d'Ethique sur la Trypanosomiase Humaine Africaine , Kinshasa , Democratic Republic of the Congo , 7 May 2009 ) . All interventions ( including follow-up visits ) were free of charge to the patients .
Patients who reported passively or were sent by the mobile teams to the HAT treatment facilities were screened and diagnosed for HAT . 726 patients were diagnosed as HAT cases , of them , 680 were classified in stage 2 and potentially eligible for participation , 49 were excluded by the Investigators or failed to show up for treatment , 1 refused and 630 gave their informed consent . One patient died prior to receiving any medication and was not included in the analysis . In total , 629 patients were treated with NECT at the HAT treatment facilities . Reasons for non-inclusion and the patient flow are detailed in Figure 1 . The demographic , diagnostic and clinical characteristics varied between the treatment facilities ( Table 1 ) . About 16% of the patients were children below 12 years of age . Katanda had the highest ( 22% ) rate of children admitted and Bandundu ( 9% ) the lowest . The overall ratio of males to females was 1 . 3 ( range 0 . 9 to 2 . 0 ) . Amongst the patients , there were 33 ( 5% ) breastfeeding women , 14 ( 2% ) pregnant women and 135 ( 22% ) patients with HAT treatment history ( range 3% to 39% ) . Of patients with a HAT history , two thirds were considered true relapses ( 84/135 ) who were treated for HAT within 2 years prior admission to the current treatment ( Table 1 ) . There were 188 ( 30% ) malnourished patients ( 172 ( 33% ) adults and 16 ( 16% ) children aged below 12 years , ; based on age specific underweight classification of anthropometric data according to WHO [19] , [20] ) . However , the extent of malnutrition amongst the study population varied between the facilities , with Dipumba reporting the lowest ( 15% ) and Bandundu and Yasa Bonga the highest proportion ( 40% ) of undernourished patients admitted . All patients were diagnosed as stage 2 HAT patients with >5 leucocytes per µl CSF ( cerebro-spinal fluid ) , of those , 89% ( 558/629 ) were parasitologically confirmed and 11% ( 71/629 ) based on their raised WBC ( white blood cell ) levels in CSF and clinical signs . The latter presented either positive serology ( 7% , 44/629 ) or had already a HAT history ( 4% , 27/629 , data not shown ) . The clinical and diagnostic findings differed slightly between sites . Common disease symptoms by frequency of occurrence were sleeping disorders ( nocturnal insomnia and/or diurnal somnolence ) , headache , asthenia , fever , pruritus and weight loss , each affecting more than half of all patients ( Table 1 ) . All patients were hospitalised for the entire treatment period . Ninety-nine percent ( 621/629 ) and 94% ( 590/629 ) of the patients received the complete eflornithine and nifurtimox doses , respectively ( as per protocol 14 intravenous doses of eflornithine and 30 oral doses of nifurtimox ) . Minor divergences occurred in the timing of drug administration or by administration of additional doses of nifurtimox ( at the end of treatment ) and/or in case of vomiting within the first 30 minutes after the intake . Major treatment deviations were due to overdosing of more than 10% of correct treatment ( calculated based on body weight; 3 patients ) or to withdrawal by family ( 1 patient ) or to treatment cessation after 1 dose due to intolerance ( 1 patient ) . The adherence to treatment varied slightly between the facilities and also amongst the patient groups - lowest adherence was in the pregnant women group ( nifurtimox 79% & eflornithine 93% ) and in small children below 5 years of age ( nifurtimox 89% & eflornithine 86%; Table 2 ) . Concomitant medication during NECT therapy was common: 93% ( 585/629 ) of patients received in median 4 additional different drugs ( range 1–14 ) . All pregnant women ( 14/14 ) and 86% ( 30/35 ) of children below 5 years of age received concomitant treatment . The median length of hospitalisation was 16 days , and measured from the day of admission to the day of discharge , including days when pre-medication was given ( Table 2 ) . The length of the hospitalisation stay was similar for all patient groups , but varied between the facilities ( 12 days in Bandundu , 15 days in Dipumba , 16 days in Katanda , Ngandajika & Kwamouth , and 20 days in Yasa Bonga ) mainly due to differing routine practices at the facilities ( reduced pre-treatment days in Bandundu or prolonged observation days in Yasa Bonga ) . 98 . 4% ( 619/629 ) of patients were discharged alive ( 95%CI = [97 . 1%; 99 . 1%] , Table 2 ) . All children younger than 12 years of age were discharged alive ( 100/100 ) , as were breastfeeding women ( 33/33 ) . Due to a death of one pregnant woman , the survival in that group was low , at 92 . 9% ( 13/14 ) . The proportions of patients discharged alive were similar for all other adults ( 97 . 3%–98 . 8% ) , including for patients with history of previous HAT . At the time of discharge from the treatment facility , clinical characteristics had substantially improved ( Karnofsky index , the classification of patients performance as to their functional impairment raised from mean 70% to 86%; neurological signs decreased from 89% to 37% , bad general health state had reduced from 80% to 14% and lymphadenopathy from 54% to 22%; the patients who died during the treatment were excluded from this analysis; data not shown ) . Treatment emergent adverse events were reported from the first dose of study drug until discharge of the patient from the treatment facility . Five hundred and seventy-eight patients ( 92% ) suffered from at least one adverse event and 79 patients ( 13% ) from at least one severe adverse event ( CTCAE grades 3 to 5 including death cases ) . The overall safety profile and the nature of the most frequently observed adverse events and severe adverse events were similar for the study populations of interest , with a few exceptions ( Table 3 ) . Pregnant women tended to be affected more frequently by vomiting , asthenia and headache . On the other hand , it was the only group not developing any psychiatric disorder . Breastfeeding women showed double the incidence of agitation than others ( 12% ) . Children had less psychiatric events and reported less headaches , but had more fever and injection site reactions . Anorexia was more frequently , and fever less frequently reported in the population with previous HAT . 6 . 5% of the patients received a repeated or added dose of nifurtimox , often due to vomiting during the 30 minutes following drug intake . This tended to occur more frequently in small children ( 8 . 6% ) , pregnant ( 14 . 1% ) and breastfeeding ( 9 . 1% ) women and patients with previous HAT ( 11 . 9% ) . One patient ceased his NECT treatment due to recurrent convulsions occurring just after 1 eflornithine administration , without having had a known convulsion history . One patient had a temporary nifurtimox interruption of 16 hours due to coma . Thirty-two ( 5 . 1% ) patients had a serious adverse event ( SAE ) during hospitalisation . From those , 25 were considered as possibly or probably related to study drug ( Table 2 ) . Thirteen patients had an SAE affecting the nervous system including mood disorders , psychosis , convulsions , ataxia and coma , while 8 patients had an SAE that may have been induced by myelotoxicity , including infections and anaemia ( data not shown ) . All patients with an SAE who did not die recovered without sequelae . No SAE was reported for children of breastfeeding mothers during their hospitalisation ( Table 2 ) . During hospitalisation , 10 patients died of various causes ( infections , coma , anaemia , cardiogenic shock and non-specific diagnosis ) . Three patients died during treatment , whilst 7 died during the observation period . Eight of the 10 patients were already in a bad health state prior to treatment , as reflected by their Karnofsky Index at 60% or lower . Nine deaths were considered to be possibly ( 7/9 ) or probably ( 2/9 ) related to NECT . One death was considered unrelated .
Almost all - 98 . 4% ( 619/629 ) patients were discharged alive after treatment . The nature , frequency and intensity of adverse events were situated in the expected range as shown in previous studies [11] , [21] , [22] and considering the fatal outcome of the disease . NECT was sufficiently tolerated among all population sub-groups in the context of 2nd stage HAT treatment . Most patients saw their health improve during hospitalisation ( Karnofsky index raise from 70% before treatment to 86% after treatment and general health state improvement of 85% of the patients as judged by the Investigators , data not shown ) . Concomitant treatments used to manage adverse events and co-existing diseases were mainly antiparasitic , antiinfective , analgesic , antipyretic , antiemetic drugs and benzodiazepines . The evaluation of adverse events and causes of mortality was complex , as symptoms were often related and confused with the symptoms of the disease itself [23] , [24] or concomitant disease in a background of severely ill and often malnourished patients . Apart from nausea and vomiting , the symptoms observed during NECT treatment are similar to HAT symptoms described during anamnesis at enrolment and reported in the literature [23] . A clear distinction of causality between the disease itself , the co-morbidities and the study treatment is not possible in most cases . Similar in-hospital safety rates and profiles were observed among the different population sub-groups including the small children , pregnant ( considering the low number in this group ) or breastfeeding women and patients having previously relapsed . Implementing a clinical trial inevitably modifies field conditions , as study procedures , patient's safety recommendations , study forms and external expertise are being brought to the centres . Nevertheless , the study was carefully designed to minimise these modifying effects , while maintaining adequate quality standards . The facilities' hospitalisation conditions were maintained: for example food was not systematically provided , unless a patient was suffering from advanced malnutrition; concomitant medication was based on routine stock of drugs . However , for safety reasons , emergency kits containing drugs for treatment of potentially severe or life threatening events were provided to the centres before the inclusion of the first patient . The study was implemented in six treatment facilities in the DRC under conditions mimicking field reality as much as possible . The demographic characteristics of patient populations varied slightly between facilities , but nonetheless reflected the observed distribution of the HAT populations described in the literature [4] , [13] and therefore allows comparison with previous studies even if here additional groups have been added with similar baseline and in-hospitalisation safety profiles . The proportion of patients discharged alive after NECT treatment in field conditions was comparable to eflornithine monotherapy and to previous NECT trials in field conditions , varying from 94 . 1% to 99 . 3% , depending on the drug in use [4] , [11] , [13] , [14] , [21] , [22] . The fatality rate during hospitalisation did not exceed the projected values derived from literature and field reports ( 0 . 8–2 . 1%; [4] , [11] , [13] , [14] ) . The median length of hospitalisation for a patient was 16 days including premedication and observation period compared to 25–30 days for melarsoprol [3] or 20 days for eflornithine monotherapy ( personal communication PNLTHA DRC ) . The NECT therapy could be accomplished in 10 days for most patients . Its dosage schedule is simpler to apply than eflornithine monotherapy ( 56 versus 14 intravenous infusions [4] ) , requiring less nursing staff . Compared to the formerly used standard HAT treatments , NECT is safer than melarsoprol ( 2 . 7–34% melarsoprol case fatality rate [3] , [4] ) and similar to eflornithine monotherapy . The overall safety profile of NECT in the present study is similar to the safety data of NECT obtained during previous studies comparing different drug combinations [21] , Nifurtimox-Eflornithine case series [22] and the NECT phase III RCT comparing NECT to eflornithine monotherapy [11] . In this study , arrhythmias , musculoskeletal and connective tissue disorders , injection site reactions and headaches were reported less frequently compared to the NECT phase III RCT [11] . All other adverse events were similar in nature , intensity and frequency . These differences in adverse event reporting can be explained by the different design and context of both studies . Another limitation of the comparability is that adverse events reported during previous studies were coded according to different standard dictionaries ( i . e . the CTC ) . Potentially harmful neurological adverse events , such as convulsions ( 9% ) and coma ( 1% ) compatible with an encephalopathic syndrome were observed in 13 SAE cases ( 2 . 1% ) . However , as convulsions already occurred in 31 patients ( 5% ) prior to treatment ( Table 1 ) , it is difficult to assess if they were caused by the disease , were related to the treatment regimen or resulted from a combination of both . These cases of encephalopathic-like reactions are not directly comparable to previous eflornithine [4] , [13] , [14] or melarsoprol [3] , [25] , [26] trials , as the same definition of the encephalopathic syndrome [25] , [27] was not consistently used in all those trials . Switching from a controlled environment , such as during the NECT phase III RCT where patients were closely followed [11] to field conditions raised some concerns about infection risk at the injection site and infections in general . However , during the present trial , no evidence of increased infection was observed . As already expressed by Priotto [11] , vomiting remains a concern because of its frequency , especially during the first days of NECT therapy . Nifurtimox doses were repeated once or twice in 6 . 5% of the patients . The final effectiveness analysis will enable evaluation of the impact of vomiting on the cure rate . A close observation of nausea and vomiting is recommended in order to allow the timely administration of a second nifurtimox dose if necessary , as well as the prescription of an anti-emetic drug . The in-hospital safety and feasibility of NECT in field conditions have been shown to be satisfactory in this trial . Consequently , its use in remote , rural sleeping sickness treatment facilities in endemic countries seems justified . In field conditions and in a wider population , including children , the use of NECT displayed a similar tolerability profile to that previously described in more stringent clinical trial conditions [11] . However , wide implementation of NECT demands sufficient levels of human resources for IV infusion treatment and that financial and logistical barriers in the supply chain management are overcome . In addition , a common international , standardised pharmacovigilance system should be supported to further improve the collection of safety and efficacy data in specific populations ( pregnant/breastfeeding women and children ) and to monitor the emergence of rare severe adverse events . The in-hospital evolution of patients was similar to previously published NECT results . The effectiveness will be assessed at the end of the 24 months follow-up period .
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Sleeping sickness is a neglected tropical disease affecting people in Sub-Saharan Africa , most of them in poor , rural settings . If not treated , the disease usually progresses into a serious stage affecting the central nervous system , causing severe sleep disturbances , as well as other neurological and psychiatric disorders , coma , and death . Until recently , the only ways to treat 2nd stage gambiense sleeping sickness involved drugs that were either toxic ( such as the arsenic derivative melarsoprol ) or difficult to administer in resource-constrained settings ( e . g . eflornithine ) . A new treatment , nifurtimox-eflornithine combination therapy ( NECT ) , was developed and tested in clinical trials , and is now recommended as the treatment of choice for 2nd stage sleeping sickness . NECT is easier to administer than eflornithine , but more information needs to be gathered for its use in the field: logistical barriers must be overcome , health care staff must be well trained , and , importantly , the safety profile of the treatment in real-life conditions needs to be better evaluated . We report here the results of a study designed to gain better understanding of the clinical tolerability , feasibility and effectiveness of NECT in field conditions , and to gather data on its use in specific populations , such as children and pregnant women .
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[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"infectious",
"diseases",
"african",
"trypanosomiasis",
"neglected",
"tropical",
"diseases"
] |
2012
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In-Hospital Safety in Field Conditions of Nifurtimox Eflornithine Combination Therapy (NECT) for T. b. gambiense Sleeping Sickness
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The emergence of severe acute respiratory syndrome coronavirus ( SARS-CoV ) in 2002 and Middle East respiratory syndrome coronavirus ( MERS-CoV ) in 2012 has generated enormous interest in the biodiversity , genomics and cross-species transmission potential of coronaviruses , especially those from bats , the second most speciose order of mammals . Herein , we identified a novel coronavirus , provisionally designated Rousettus bat coronavirus GCCDC1 ( Ro-BatCoV GCCDC1 ) , in the rectal swab samples of Rousettus leschenaulti bats by using pan-coronavirus RT-PCR and next-generation sequencing . Although the virus is similar to Rousettus bat coronavirus HKU9 ( Ro-BatCoV HKU9 ) in genome characteristics , it is sufficiently distinct to be classified as a new species according to the criteria defined by the International Committee of Taxonomy of Viruses ( ICTV ) . More striking was that Ro-BatCoV GCCDC1 contained a unique gene integrated into the 3’-end of the genome that has no homologs in any known coronavirus , but which sequence and phylogeny analyses indicated most likely originated from the p10 gene of a bat orthoreovirus . Subgenomic mRNA and cellular-level observations demonstrated that the p10 gene is functional and induces the formation of cell syncytia . Therefore , here we report a putative heterologous inter-family recombination event between a single-stranded , positive-sense RNA virus and a double-stranded segmented RNA virus , providing insights into the fundamental mechanisms of viral evolution .
Coronaviruses are large , enveloped viruses with single-stranded , positive-sense , non-segmented RNA genomes [1] . Based on the current nomenclature of the International Committee of Taxonomy of Viruses ( ICTV ) , coronaviruses of the family Coronaviridae are now classified into four genera: alpha- , beta- , gamma- and deltacoronavirus [2 , 3] . Betacoronaviruses can be further subdivided into four phylogenetic groups [2] . Coronaviruses employ a unique mechanism of viral genome replication and RNA synthesis , resulting in high frequencies of both mutation and recombination [4] . Recombination appears to be particularly important in coronavirus evolution [5] , with a number of hotspots interspersed throughout the viral genome [6] . Recombination events at 3’-end of the genome might impact the replication ability of coronaviruses since there are a number of regulatory sequences and accessory genes in this region [5] . As coronaviruses were previously known to cause only mild respiratory illnesses in humans they were not a major concern of the public health community . However , the emergence of severe acute respiratory syndrome coronavirus ( SARS-CoV ) [7–9] and its high infectivity and fatality generated considerable interest in the biodiversity , genomics , evolution , natural hosts and potential inter-species transmission of coronaviruses [10] . To date , at least 90 types of coronavirus have been isolated or genome-identified from humans and a wide variety of animals , including domestic animals , wild birds and bats . Bats are particularly notable in this respect because they are known to harbor a diverse range of pathogens , and are known to be the reservoir hosts of both human coronavirus 229E [11] and SARS-CoV [12] , and are closely related to MERS-CoV [13 , 14] . As a consequence bats have been prioritized for the surveillance of emerging zoonotic diseases [15–17] . In the present study we report a novel coronavirus discovered from bat samples in China that has been tentatively named Rousettus bat coronavirus GCCDC1 ( Ro-BatCoV GCCDC1 ) . Multiple lines of evidence indicate that Ro-BatCoV GCCDC1 may have arisen from a recombination event between an ancestral coronavirus and a fusogenic orthoreovirus .
A total of 118 rectal swab samples from Rousettus leschenaulti bats sampled in Yunnan province China were screened for the presence of coronavirus RNA . Of these , 47 ( 40% ) samples were found to be coronavirus positive . The PCR products were sequenced and BLAST searches revealed the sequences to be authentic coronavirus genes , with the strongest similarity to Rousettus bat coronavirus HKU9 ( Ro-BatCoV HKU9 ) [18] , a member of the genus betacoronavirus ( group D ) . However , our attempts to isolate the virus from samples using a number of cell lines , including Vero E6 , BHK-21 , MDCK , A549 , HEp-2 , CaCo-2 and a bat cell line from Myotis kidney , were unsuccessful . The cell lines were inoculated with positive samples and three blind passages were performed for each sample . No cytopathic effect was observed in any passage , and there was an absence of viral replication from the culture supernatant and cell pellet of each passage . The viral genomic sequences present in two coronavirus positive samples ( numbers 346 and 356 ) were determined with next-generation sequencing ( NGS ) . Analysis using a partial ( 816-bp fragment ) sequence of the RNA-dependent RNA polymerase ( RdRp ) gene indicated that the newly identified virus was likely to be a novel coronavirus according to previously proposed criteria [19] . Therefore , this virus was tentatively designated as Rousettus bat coronavirus GCCDC1 ( Ro-BatCoV GCCDC1 ) . Gaps within the genome of Ro-BatCoV GCCDC1 were closed , and the complete genome sequence confirmed , using Sanger sequencing . Finally , the 5’- and 3’-ends of Ro-BatCoV GCCDC1 genome were obtained using 5’ and 3’ RACE ( Fig 1 ) . Excluding the polyadenylated tail at the 3’-terminus , the genome of Ro-BatCoV GCCDC1 was 30 , 129 nt in length with a G/C content of 45 . 4% . Comparative genomic sequence analysis indicated that Ro-BatCoV GCCDC1 was most closely related to Ro-BatCoV HKU9 strains [18] with 66 . 6% - 67 . 4% nucleotide identities . Similarly , Ro-BatCoV GCCDC1 displayed equivalent genomic characteristics to Ro-BatCoV HKU9 except for an inserted gene at 3’ end ( discussed in detail in the next section ) . The major open reading frames ( ORFs ) had the identical order , namely 5’- replicase ORF1ab—spike ( S ) —NS3—envelope ( E ) —membrane ( M ) —nucleocapsid ( N ) followed by the accessory genes encoding nonstructural proteins ( NSPs ) ( Fig 1 and Table 1 ) , although the N gene was truncated . Amino acid sequence analyses showed that the ORF1ab , S , NS3 , E , M and N proteins of Ro-BatCoV GCCDC1 shared higher identities with Ro-BatCoV HKU9 strains than those of other betacoronaviruses ( Table 1 ) . Also of note was that the 3’-end of Ro-BatCoV GCCDC1 genome , just downstream of N gene , possessed a much more complicated structure than those of other members in the genus . Clearly , there were four NSP-encoding ORFs . According to the convention , the second-to-fourth ORFs were temporarily named NS7a , NS7b and NS7c , respectively , since they shared 29% - 53% amino acid identities with the accessory genes of Ro-BatCoV HKU9 strains and other related bat coronaviruses ( S1 Table ) . Perhaps the most striking feature of Ro-BatCoV GCCDC1 genome was the presence of a small intact ORF with 276 bases embedded between the N and NS7a genes . Although this ORF that had no homology to any known coronavirus , the encoded protein exhibited 30% - 54 . 9% amino acid identity with the p10 protein encoded by the first ORF of segment S1 of avian and bat fusogenic orthoreoviruses [20] , which are double-stranded segmented RNA viruses belonging to the family Reoviridae . Therefore , this ORF was provisionally marked as p10 according to the molar weight of protein that it encodes ( Fig 1 ) . The putative leader and body transcription regulatory sequences ( TRSs ) of Ro-BatCoV GCCDC1 , and their genomic localizations , were predicted in accordance with consensus core sequences of the TRSs of betacoronaviruses ( Table 1 ) . The TRS core sequence , 5’-ACGAAC-3’ , was consistent with those of SARS-CoV , Ro-BatCoV HKU9 and other betacoronaviruses . From the location of leader TRS , the leader sequence of the genome was then identified , which spanned genome positions 1 ( G ) to 78 ( C ) ( Fig 1 and Table 1 ) . Notably , in the putative TRS of the p10 gene , there was one nucleobase difference with the consensus core sequence ( Table 1 ) . The putative mature nonstructural proteins ( NSPs ) in the ORF1ab encoding the replicase were calculated based on the cleavage and recognition pattern of the 3C-like proteinase ( 3CLpro ) and papain-like proteinase ( PLpro ) . Comprehensive information on the size and genomic locations of nsp1 to nsp16 and the putative cleavage sites of proteinases is presented in Table 2 . Previous studies indicated that the P1 position of 3CLpro specific cleavage site is exclusively occupied by a glutamine ( Q ) residue [22 , 23] . However , nucleobase 12642 in the Ro-BatCoV GCCDC1 genome was a T nucleotide , thereby changing glutamine ( Q ) to histidine ( H ) . More interestingly , there were no glutamine codons in the sequence ( from -273 to +192 ) around this site , as also observed in the corresponding site in the genomes of Ro-BatCoV HKU9 . Therefore , the LH|AG region may represent a potential alternative cleavage site of 3CLpro to cleave between NSP9 and NSP10 . A similar phenomenon may occur at the cleavage site between NSP10 and NSP12 of Ro-BatCoV GCCDC1 , where the CAG codon has mutated to CAC causing the conversion of Q to H in amino acid sequence ( Table 2 ) . Following the criteria for coronavirus species demarcation defined by the ICTV [1 , 13] , seven conserved replicase domains of Ro-BatCoV GCCDC1 were selected for analysis ( Table 3 ) . The amino acid identities of seven concatenated domains in Ro-BatCoV GCCDC1 revealed that they shared 84 . 4% - 84 . 8% identity with those of Ro-BatCoV HKU9 , which was below the 90% threshold used for species demarcation ( Table 3 ) . Hence , these data suggest that the newly identified Ro-BatCoV GCCDC1 represents a novel coronavirus species in the genus betacoronavirus . To determine the evolutionary position of Ro-BatCoV GCCDC1 , the RdRp , S , N and p10 proteins were subjected to phylogenetic analyses . Phylogenetic trees of the RdRp , S and N proteins illustrated that Ro-BatCoV GCCDC1 , Eidolon bat coronavirus/Kenya/KY24/2006 ( Ei-BatCoV Kenya ) , Rousettus bat coronavirus/Kenya/KY06/2006 ( Ro-BatCoV Kenya ) and Ro-BatCoV HKU9 strains all belong to group D of the genus betacoronavirus ( Fig 2 ) . Within this cluster the two Ro-BatCoV GCCDC1 strains ( 346 and 356 ) formed a distinct lineage that was a sister-group to Ro-BatCoV Kenya and the Ro-BatCoV HKU9 strains ( maximum bootstrap value of 100% ) . However , a strikingly different phylogenetic pattern was observed in the distinctive p10 protein ( Fig 3 ) , in which the Ro-BatCoV GCCDC1 sequences were clearly related to bat ( Pteropine ) originated orthoreoviruses . Although the branch leading to the Ro-BatCoV GCCDC1 sequences is long , these viruses are clearly more closely related to the bat-origin than avian-origin orthoreoviruses ( Fig 3 ) , matching the host species from which Ro-BatCoV GCCDC1 was isolated . To exclude the false amplification of DNA polymerase or the inaccurate assembly of NGS data , the NGS data was analyzed further . Read mapping determined that there were a set of reads that covered the upstream junction site ( i . e . the recombination break-point ) between N and p10 genes , and a downstream junction site between the p10 and NS7a genes ( S1 Fig with data in S1 File ) . In addition , the integrity and continuity of context sequence surrounding the p10 gene were confirmed with specific primers . Agarose gel electrophoresis showed that the PCR products were intact fragments of the expected length . The amplicons were cloned for sequencing . As shown in Fig 4A , the sequence obtained covers , without interruption , the partial N gene , the whole p10 gene and partial NS7a gene ( data in the S2 File ) . Hence , there is clear evidence that the recombination event that placed the reovirus p10 gene in the Ro-BatCoV GCCDC1 genome was genuine . Sequencing information confirmed that the TRS of p10 gene is located within the encoding sequence of N gene with the core sequence of 5’-ACAAAC-3’ , which exhibited a single nucleobase difference to the consensus core sequence ( 5’-ACGAAC-3’ ) ( Fig 4B ) . We also observed a 97 nucleobase sequence between the TRS and the p10 initiation codon ( Fig 4B ) , which was much longer than the intervening sequences of other genes with the exception for that between the leader TRS and ORF1ab ( Table 1 ) . As shown in Fig 4B and S2 Fig , sequence comparisons also revealed that the location of TRS in the p10 gene could be discriminated from those of other genes , which are adjacent and downstream to the N gene of Group D Betacoronavirus . Notably , the ORF of the Ro-BatCoV GCCDC1 N gene was disrupted by the insertion of the “exotic” p10 gene , causing the truncation of eight amino acids at the 3’-terminus and a two amino acid deletion ( Fig 5 ) . According to the information provided above , the relative locations of the putative leader and body TRS ( s ) were identified in the genome of Ro-batCoV GCCDC1 ( Fig 6A ) . Based on the TRSs and transcription mechanism of coronavirus , nine potential subgenomic mRNAs of Ro-BatCoV GCCDC1 , including S , NS3 , E , M , N , p10 , NS7a , NS7b and NS7c , were depicted ( Fig 6B ) . In addition to an identical 5’ leader sequence , each lower subgenomic mRNA shared the same 3’-end structure with the upper one to comprise a 3' co-terminal nested set with the genome . The presence of subgenomic mRNAs is strong evidence of coronavirus replication in the infected cells . To determine if the bat , which sample was collected from , was likely the natural host of Ro-BatCoV GCCDC1 , subgenomic mRNAs in the sample were probed with a comprehensive set of primers . The PCR products were confirmed on an agarose gel . As displayed in Fig 6C , the lowest band marked with a red arrow on each lane was the specific amplicon from each subgenomic mRNA as demonstrated in Fig 6B . However , additional amplified bands were also compatible with this inference . As shown as an example on the lane of the E gene , the upper band indicates that subgenomic mRNA NS3 was simultaneously amplified in this reaction . On each lane the lowest band was cloned for sequencing , while other bands were purified and sequenced directly . Since the specific amplicon of the subgenomic mRNA NS7c failed to be cloned into the vectors , the PCR product was used as template for a second round of nested PCR . The product was then confirmed as shown in the lane of NS7c-2 in Fig 6C , and the band was cloned for sequencing . The results ( Fig 6D and S3 Fig ) indicated that the core sequence of the leader and body TRS of each gene , the leader-body fusion sites , and the mode of generation of subgenomic mRNAs were consistent with the prediction and demonstration in Fig 6B , especially the p10 gene and its subgenomic mRNA . Therefore , the existence of subgenomic mRNA in the samples further proved that the p10 gene was an intact authentic gene in the genome of Ro-BatCoV GCCDC1 . Despite the orthoreovirus origin of p10 , this protein exhibited 8 amino acid differences ( including a 2 amino acid deletion ) among the 28 “absolutely conserved” amino acids described previously ( Fig 7 ) . Hence , it is necessary to investigate whether the p10 gene of Ro-BatCoV GCCDC1 could play the same role as its reovirus homologs . For this purpose , the p10 gene of Ro-BatCoV GCCDC1 was transiently expressed in BHK-21 cells as well as the p10 gene of Pulau virus , which was used as a positive control . Wright-Giemsa and immunofluorescence staining showed that both genes had the same function to induce the formation of cell syncytia ( Fig 8A and 8B and S4A Fig ) . Thus , the alteration of certain conserved amino acids did not impair the syncytiogenesis of p10 gene of Ro-BatCoV GCCDC1 . The p10 subgenomic mRNA identified in the samples confirmed that the p10 gene could be transcribed from the genome of Ro-BatCoV GCCDC1 . However , due to the failure of virus isolation ( despite a great effort ) , there is no effective way to judge whether the p10 gene could be expressed during the virus replication cycle . Therefore , an artificial plasmid was constructed containing the transcribed p10 subgenomic mRNA , which confirmed the functional expression of p10 ( Fig 8C ) . When the plasmid was transfected into BHK-21 cells , once again , cell syncytia were observed with Wright-Giemsa and immunofluorescence staining ( Fig 8D and S4B Fig ) . Thus , this indirect evidence suggests that the p10 gene functions during the replication cycle of Ro-BatCoV GCCDC1 . Immunofluorescence staining also showed that polyclonal antibodies of Ro-BatCoV GCCDC1 p10 protein reacted with the p10 protein of Pulau virus ( Fig 8B and Fig 8D ) . In addition , the cross-reactivity further proved that the p10 gene of Ro-BatCoV GCCDC1 might have the same origin as those of fusogenic orthoreoviruses . As the p10 protein of Ro-BatCoV GCCDC1 is the first report of FAST protein in an enveloped virus , the conserved amino acids of the p10 protein of Ro-BatCoV GCCDC1 were mutated to determine whether they play a vital role in cell-to-cell fusion and syncytium formation as those sites in the p10 protein of reoviruses ( S5A Fig ) [24 , 25] . Notably , no cell syncytia were observed for all mutant constructs of p10 which had substitutions in the previously defined key sites in the p10 protein of reoviruses ( S5B Fig ) . This indicates that the functionality of p10 in Ro-BatCoV GCCDC1 also depends on traditional conserved domains relevant for the function of the FAST protein [24 , 25] . To confirm the expression of p10 by the Ro-BatCoV GCCDC1 virus , we performed Western blotting ( WB ) to detect the presence of p10 protein in the bat feces and concentrated rectal swab specimens . The results revealed the expression of p10 by Ro-BatCoV GCCDC1 itself ( S6 Fig ) .
We have identified a novel coronavirus , Ro-BatCoV GCCDC1 , from Rousettus leschenaulti , that belongs to group D of the genus betacoronavirus and which is related to Ro-BatCoV HKU9 [18] . According to the criteria defined by ICTV [1] , Ro-BatCoV GCCDC1 is sufficiently divergent to represent a novel bat coronavirus . More striking was that Ro-BatCoV GCCDC1 contains a p10 protein located at the 3’-end of the genome that appears to have captured from a bat-origin orthoreovirus by heterologous recombination . Homologous recombination events frequently occur during the viral RNA replication of coronaviruses , and are important for their evolution [10 , 27–29] . However , it is also possible that coronaviruses are one of the few virus families that can experience heterologous recombination . For example , members of betacoronavirus group A possess an HE gene [30 , 31] which was seemingly derived from ancestral influenza C virus , a negative-stranded RNA virus with a segmented genome [30 , 31] , and which would represent another case of inter-family recombination , although it has also been proposed that the HE gene might be captured from host RNA [32] . Uncommon inter-family recombination events have also been reported in chicken infectious anemia virus [33] , bandicoot papillomatosis carcinomatosis virus type 1 [34] , and recombinant viruses between Marek’s disease virus , fowlpox virus , and various avian retroviruses [35 , 36] . In the current study , sequence , phylogenetic and functional analyses demonstrated that the p10 gene of Ro-BatCoV GCCDC1 was likely derived from an ancestral orthoreovirus , although that it occupies a divergent position in the phylogeny suggests that the direct ancestor of the recombination event has yet to be sampled . Hence , these data provide clear evidence for a putative inter-family recombination between a single-stranded , positive-sense RNA virus and a double-stranded segmented RNA virus . The mechanisms that underpin such inter-family heterologous recombination clear merit further investigation . The biggest difference between fusogenic and nonfusogenic orthoreoviruses is the presence/absence of a small protein encoded by the segment S1 of the genome , termed the fusion-associated small transmembrane ( FAST ) protein . The FAST proteins are the only known nonenveloped reovirus fusogens that can mediate cell-to-cell , but not virus-cell , membrane fusion to induce the formation of syncytia [20] , and which might promote the dissemination of virus among cells [37] . Thus , the FAST proteins are the pathogenic determinants of fusogenic orthoreoviruses . To date , the FAST family comprises six members including p10 proteins encoded by avian- and bat-origin orthoreoviruses , p13 , p14 and p15 encoded by broome virus , reptilian reovirus and bush viper reovirus and baboon orthoreovirus respectively , and p16 and p22 encoded by aquareoviruses . Intriguingly , a specific p10 gene was identified in Ro-BatCoV GCCDC1 , so that this is the first report of a FAST protein in an enveloped virus and hence could represent the seventh member of FAST family . Unfortunately , the isolation of Ro-BatCoV GCCDC1 failed on cell culture in the present study , so it is difficult to determine the role of p10 gene during the life cycle of Ro-BatCoV GCCDC1 . However , functional analysis showed that the coronavirus p10 gene could induce syncytium formation in the transfected cells , in the same manner as orthoreoviruses , which might be beneficial for cell-to-cell virus spread . It is therefore possible that the p10 protein enhances the transmission potential of Ro-BatCoV GCCDC1 . Previous studies of the potential recombination between coronavirus and influenza C virus revealed the pivotal role of the shared HE gene for the pathogenesis of betacoronavirus group A [38] . Interestingly , human coronavirus HKU1 , OC43 and bovine coronaviruses employ the HE protein to mediate receptor-destroying enzyme activity late in the infection cycle to facilitate viral progeny release and achieve efficient virus dissemination [39] . Compared to nonfusogenic orthoreoviurses , fusogenic orthoreoviruses can cause severe pneumonia when infecting humans [40 , 41] , further implying that p10 is an important pathogenic determinant . Thus , the recombination of the reovirus-originated p10 into the Ro-BatCoV GCCDC1 may enable the novel virus to disseminate and replicate rapidly in the host , in turn leading to severe infections . In recent years , several coronaviruses , notably SARS-CoV and MERS-CoV , have caused severe pneumonia among humans [42 , 43] . Because of the presence of human infected fusogenic orthoreoviruses such as Melaka virus ( MelV ) [44] , there is obviously some risk that cross-family recombination events such as that described here may generate a novel coronavirus with altered pathogenicity . Our study therefore highlights the importance of investigating the mechanisms that might enable possible recombination between human coronaviruses and orthoreoviruses . In the protein sequence of ORF1ab encoded replicase of Ro-BatCoV GCCDC1 , the regular P1 position at two 3CLpro cleavage sites , NSP9/NSP10 and NSP10/NSP12 , contains a Q to H mutation which may impair the proteolytic efficacy and the release of NSP9 , NSP10 and NSP12 . As NSP12 is a typical RNA polymerase and the core of replication-transcription complexes ( RTC ) and NSP10 usually serves as a molecular switch that can interact with multiple NSPs to form complexes , the replication ability of Ro-BatCoV GCCDC1 might be suppressed by the decrease of release of these vital elements . It is also interesting to note that a similar situation may be observed at the NSP13/NSP14 cleavage sites of replicase polyprotein of human coronavirus HKU1 and human coronavirus NL63 . Clearly , further investigation will need to focus on the isolation of the virus , construction of infectious clones , and the virulence and replication ability of Ro-BatCoV GCCDC1 influenced by knockout of p10 gene and/or reverse mutation of cleavage sites . Phylogenetically distinct virus species or lineages have been reported co-circulating in certain bat populations [45 , 46] . Under this situation , co-infections of single host cells—the necessary requisite for recombination—are possible . By careful sequence analysis we show that the heterologous recombination event placed the p10 gene in Ro-BatCoV GCCDC1 is genuine . Previous studies showed the existence of p10-harboring orthoreoviruses in bat populations [40 , 47–49] , such that co-infections with bat coronaviruses and hence recombination events are clearly possible . In addition , a previous study reported that mammalian orthoreovirus , a type of nonfusogenic orthoreovirus , was isolated from a SARS-CoV patient along during the in 2003 outbreak [50] . We believe that future studies should investigate co-infections in specific bat cell lines using a coronavirus similar to Ro-BatCoV GCCDC1 or Ro-BatCoV HKU9 and the relevant orthoreoviruses , from which it will be possible to reveal more of the underlying basis of heterologous coronavirus recombination .
The protocol in this study was approved by the Committee on the Ethics of Animal Care and Use of the Chinese Center for Disease Control and Prevention ( Permit 20140509015 ) . The study was conducted in accordance with the Guide for the Care and Use of Wild Mammals in Research of the People's Republic of China . African green monkey kidney cells ( Vero E6 ) , human epithelial colorectal adenocarcinoma cells ( CaCo-2 ) , human epithelial type 2 HeLa derivative cells ( HEp-2 ) and human lung carcinoma cells ( A549 ) were purchased from the China Center for Type Culture Collection , while baby hamster kidney ( BHK-21 ) and Madin-Darby canine kidney ( MDCK ) cells were obtained from the Cell Resource Center of the Shanghai Institute for Biological Sciences , Chinese Academy of Sciences . The immortalized kidney cell line of Myotis Davidii ( IKMD ) was a generous gift of Dr . Zhengli Shi , Wuhan Institute of Virology , Chinese Academy of Sciences . Cells were grown in Eagle’s minimum essential medium ( EMEM ) ( A549 , BHK-21 ) or in Dulbecco’s modified EMEM ( DMEM ) ( Vero E6 , CaCo-2 , HEp-2 , MDCK , IBIE and IKMD ) supplemented with 10% or 20% ( CaCo-2 ) fetal bovine serum in a humidified chamber containing 5% CO2 at 37°C . All the bats analyzed here were captured at a roosting site with the assistance of villagers and staff of local the CDC office in Xishuangbanna , Yunnan Province , China . The rectal swab samples were collected and placed in the cryotube with viral transport medium ( VTM ) containing Earle's balanced salt solution ( Invitrogen , United States ) , 5% bovine albumin , 50 , 000 μg/ml vancomycin , 50 , 000 μg/ml amikacin , 10 , 000 units/ml nystatin [51] . All samples were immediately stored in liquid nitrogen and then transported with dry ice to our laboratory in Beijing and stored in the ultra-low temperature freezer until used for RNA extraction . Total RNA was extracted from 100 μL of VTM suspension of each swab with the RNeasy Mini Kit ( Qiagen , Germany ) according to the manufacturer's protocol . The RNA was eluted in 60 μL AVE buffer , of which 8 μL RNA was used as the template for RT-PCR immediately , or stored at −80°C until use . Total RNA extracted from the rectal swab suspension was screened for the presence of coronavirus RNA using pan-coronavirus RT-PCR with universal degenerate primers . The primers were designed from a highly conserved region of the RdRp ( primer sequences are presented in S4 Table ) . After the reverse transcription and synthesization of cDNA with SuperScript III Reverse Transcriptase ( Invitrogen , United States ) , a semi-nested PCR was performed . The expected amplicons of two rounds were 299 bp ( using primers panCoVs-OF and panCoVs-OR ) and 228 bp ( using primers panCoVs-IF and panCoVs-OR ) in length , respectively . All positive results were repeated and confirmed with fresh RNA extracts from the original bat rectal swab suspensions . Purified DNA amplicons ( both rounds ) were sequenced bi-directionally with pan-coronavirus sequencing primers ( S4 Table ) on an ABI Prism 3730 automated capillary sequencer ( Applied Biosystems , United States ) . Fresh RNA was extracted from sample numbers 346 and 356 which were confirmed as coronavirus positive . The RNA were subjected to Next Generation Sequencing ( NGS ) using the Ion Proton platform . The original NGS data were filtered , refined and mapped to the reference sequence of Ro-BatCoV HKU9 ( GenBank accession number NC_009021 ) using SOAP ( Short Oligonucleotide Alignment Program ) [52] . Any remaining gaps in the genome were closed by PCR amplification of these regions with specific primers and then sequenced . Complete genome sequences were confirmed with Sanger sequencing on the fragments amplified with a set of primers that covered the whole genome . The 5’- and 3’-RACE analyses were performed with 5’- and 3’- Full RACE Kit ( Takara , Japan ) according to the manufacturer’s instructions . As the amplification of 5’-end of the genome of Ro-BatCoV GCCDC1 strain 346 was unsuccessful , we focused our genome analyses on the complete genome of Ro-BatCoV GCCDC1 strain 356 . This genome was compared to those of eight complete genomes of Ro-BatCoV HKU9 ( GenBank accession numbers NC_009021 , EF065514 , EF065515 , EF065516 , HM211098 , HM211099 , HM211100 and HM211101 , respectively ) to annotate the 1ab , S , NS3 , E , M and N ORFs , respectively . As the origin of the ORFs at the 3’-end of the genome were uncertain they were also blasted ( tblastx ) against the GenBank database . The amino acid sequence of ORF1ab was aligned with the reference sequences of SARS-CoV , human coronavirus HKU1 , infectious bronchitis virus , turkey coronavirus , bovine coronavirus , mouse hepatitis virus and porcine epidemic diarrhea virus ( GenBank accession numbers NC_004718 , NC_006577 , NC_001451 , NC_010800 , NC_003045 , NC_001846 and NC_003436 , respectively ) to determine the cleavage and recognition patterns of the C-like proteinase and papain-like proteinase of the 16 nonstructural proteins . In addition , the sequences of the 5’ untranslated region ( 5’-UTR ) and 3’ untranslated region ( 3’-UTR ) were defined , and the leader sequence , the leader and body TRSs were illustrated , based on comparison with SARS-CoV . To eliminate the possibility of false amplification of DNA polymerase or inaccurate assembly of NGS data , the raw NGS data were further scrutinized and reads extracted for mapping to check the continuity of the p10 sequence , especially the upstream junction site between N and p10 genes and the downstream junction site between the p10 and NS7a genes . In addition , two sets of specific primers were designed to confirm the integrity and continuity of sequence surrounding the p10 gene ( primer sequences shown in S5 Table ) . The amplicons were subsequently cloned into the pMD18-T vector and recombinant plasmids were subjected to Sanger sequencing . To determine the phylogenetic position of the newly identified coronavirus among the known diversity of coronaviruses , the amino acid sequences of the RdRp , S , and N proteins were used for phylogenetic analyses ( GenBank accession numbers shown in S2 Table ) . In the case of the imported p10 gene , homologous sequences of orthoreoviruses were utilized as the background data set in the phylogenetic analysis ( GenBank accession numbers listed in S3 Table ) . All amino acid sequences were aligned using MUSCLE [53] , and all poorly or ambiguously aligned regions were removed using GBlocks [54] . Because of the short length of the p10 and N amino acid sequence alignments , more relaxed GBlocks parameters were used in these cases . In all cases phylogenetic trees of amino acid sequence alignments were inferred using the maximum likelihood method available in the PhyML package [55] , with bootstrap values estimated from 1 , 000 replicate trees . Each tree was inferred using the LG model of amino acid substitution with values of the gamma shape parameter inferred using ProtTest [56] . Finally , all phylogenetic trees were displayed and annotated with FigTree . Samples positive for coronavirus were cultured in Vero E6 , BHK-21 , MDCK , A549 , HEp-2 , CaCo-2 cells , as well as in an immortalized kidney cell line of Myotis Davidii . The cell lines were inoculated with positive samples and three blind passages were performed for each sample . The culture supernatant and cell pellet of each passage were harvested . The detection of viral replication was conducted using specific primers targeting the conserved region of RdRp . Nested subgenomic mRNAs are generated during the replication cycle of coronaviruses . Hence , the identification of subgenomic mRNAs in the samples provides strong evidence for the replication of coronavirus . To analyze the possibility of replication in the newly identified bat coronavirus , primers were designed to determine the presence of viral subgenomic mRNAs in the coronavirus-positive bat rectal swab samples . Forward primers were designed targeting the leader sequence at the 5’-end of the complete genome and the putative subgenomic mRNAs , while reverse primers were designed within the ORFs or downstream of the corresponding gene ( primer sequences are shown in S6 Table ) . Specific amplicons , that matched the expected length , were purified and then cloned into the pMD-18T vector for sequencing , while the additional suspected bands on the agarose gels were excised , purified , and then subjected to direct sequencing . Since the specific amplicon of subgenomic mRNA NS7c failed to be cloned into the vectors , the PCR product was used as a template for a second round of nested PCR . The product was then confirmed with agarose gel electrophoresis and the band was cloned for sequencing . The protein family of the putative p10 protein was analyzed using PFAM [57] and InterProScan [58] . Prediction of transmembrane domains was performed using TMHMM [59] , TMpred and PredictProtein [60] . Peptides corresponding to the ectodomain ( from amino acid positions 2–37 ) and the cytoplasmic domain ( the last 33 amino acids ) ( peptide sequences are described in S7 Table ) of the putative p10 protein were synthesized ( Xuheyuan Biological Technology Co . , LTD , Beijing , China ) . After conjugation with keyhole limpet hemocyanin ( KLM ) , the synthesized peptides were used to immunize mice for antibody production . The mice ( five mice per peptide ) were injected intramuscularly at their hind legs with 20 μg of the conjugated peptide mixed with adjuvant , followed by boosts until 14 days with the same conjugated peptides . Seven days after the boosts , the mice were killed and their blood collected to isolate sera . Antibody titers were determined using enzyme-linked immunosorbent assay ( ELISA ) . In the cells infected with avian- or bat-origin fusogenic orthoreoviruses , the formation of cell syncytia depends on a p10 protein , which is encoded by the first ORF in segment S1 of the reovirus genome . It was previously demonstrated that amino acid residues of p10 proteins could be sorted into absolutely , highly , moderately and non-conserved amino acids [26] . Sequence and phylogenetic analyses indicated that the p10 gene of Ro-BatCoV GCCDC1 most likely originated in an orthoreovirus . Comparative sequence analysis revealed that although the majority of key amino acids and motifs of the Ro-BatCoV GCCDC1 p10 protein were conserved , there were 8 amino acids differences ( including 2 deletions ) among the 28 so-called ‘absolutely conserved’ amino acids that characterize members of the FAST family ( Fig 7 ) . Hence , it is necessary to explore the potential role of the p10 gene during the life cycle of Ro-BatCoV GCCDC1 . To determine whether the putative p10 protein could play the same role as homologous proteins of avian and bat orthoreoviruses , the p10 gene was cloned into the pCAGGS vector and the recombinant plasmid ( Fig 8A ) was then transfected into BHK-21 cells using Polyethylimine ( PEI , Polysciences Inc . ) according to the manufacturer’s protocol . At the appropriate time post-transfection , cell-to-cell fusion was observed for the syncytium formation under the light microscope using Wright-Giemsa staining and an indirect immunofluorescence assay employing the polyclonal antibodies prepared above . The p10 gene of Pulau virus , a bat orthoreovirus [49] , was also cloned into the pCAGGS vector to serve as a positive control . Cells transfected with an empty pCAGGS vector were used as a mock control . The next step is to confirm that p10 can be transcribed or translated during the replication cycle of Ro-BatCoV GCCDC1 . We confirmed that the p10 gene could be transcribed from the genome during the replication cycle of Ro-BatCoV GCCDC1 , with the p10 subgenomic mRNA representing a distinct signal . Further , we cloned the deduced p10 subgenome into a pcDNA3 . 0-derived vector to construct an artificial plasmid ( Fig 8C ) , which could be transcribed out of an mRNA that is consistent with the p10 subgenomic mRNA in the infected cells of the host . The recombinant plasmid was transfected into BHK-21 cells and cell syncytia were observed as described above . The recombinant plasmid of Pulau virus p10 gene was still served as positive control . Cells transfected with empty pcDNA3 . 0 vector were used as mock control . As the p10 protein of Ro-BatCoV GCCDC1 is the first reported in an enveloped virus , we first tried to define the key amino acids for the p10 protein in the Ro-BatCoV GCCDC1 as described previously for p10 protein of reoviruses [24 , 25] . For syncytial indexing of six mutant constructs , each well of BHK-21 monolayer cells in a 6-well plate were transfected with 2 μg of plasmid DNA using Polyethylimine ( PEI , Polysciences Inc . ) and incubated for 5 h before replacing the transfection mixture with DMEM growth media ( Invitrogen ) supplemented with 10% fetal bovine serum ( GIBCO ) . Transfected cells were paraformaldehyde-fixed and stained with Wright-Giemsa at the indicated times , and syncytia were observed and pictures were taken at ×100 magnification on an Olympus IX51FL+DP70 microscope . The original specimens of bat rectal swabs and feces were used to test the expression of p10 . BHK-21 cells with transient expression plasmid of p10 gene ( pCAGGS-p10 ) were used as a positive control . Briefly , bat specimens and BHK-21 cell lysates were subjected to SDS-PAGE and transferred to a PVDF membrane . The membranes were blocked with a 5% non-fat dry milk solution and incubated with p10 antibody overnight at 4°C followed by peroxidase-conjugated affinipure goat anti-mouse IgG ( H+L ) ( Zhongshan Goldenbridge , Beijing ) . After washing with TBS-T buffer , the membrane was treated with ImmobilonTM Western Chemiluminescent HRP Substrate ( Millipore , Billerica ) and pictures were taken with Chemiluminescence System MicroChemi 4 . 2 ( DNR Bio-Imaging Systems Ltd , USA ) . The complete genome sequences of Ro-BatCoV GCCDC1 strains 346 and 356 have been deposited in the GenBank database and assigned accession numbers KU762337 and KU762338 , respectively . We also deposited the sequences of the p10 genes from the rectal swabs of 24 bats in GenBank . All these accession numbers are listed in S8 Table .
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Recombination is commonly reported in coronaviruses , and is an important mechanism by which these viruses generate genetic diversity . To date , however , most such recombination events involve homologous sequences among related viruses . We discovered a novel bat coronavirus that possesses a divergent but functional p10 gene that likely originated from , or shared the ancestry with , an ancestral non-enveloped orthoreovirus , thereby representing the outcome of heterologous recombination . We report herein a fusion-associated small transmembrane ( FAST ) protein encoded in an enveloped virus that arose through a putative inter-family recombination between a single-stranded , positive-sense RNA virus and a double-stranded segmented RNA virus . These findings shed important new light on the mechanisms of viral evolution and particularly the importance and scope of heterologous recombination .
|
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2016
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A Bat-Derived Putative Cross-Family Recombinant Coronavirus with a Reovirus Gene
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Thousands of regions in gametes have opposing methylation profiles that are largely resolved during the post-fertilization epigenetic reprogramming . However some specific sequences associated with imprinted loci survive this demethylation process . Here we present the data describing the fate of germline-derived methylation in humans . With the exception of a few known paternally methylated germline differentially methylated regions ( DMRs ) associated with known imprinted domains , we demonstrate that sperm-derived methylation is reprogrammed by the blastocyst stage of development . In contrast a large number of oocyte-derived methylation differences survive to the blastocyst stage and uniquely persist as transiently methylated DMRs only in the placenta . Furthermore , we demonstrate that this phenomenon is exclusive to primates , since no placenta-specific maternal methylation was observed in mouse . Utilizing single cell RNA-seq datasets from human preimplantation embryos we show that following embryonic genome activation the maternally methylated transient DMRs can orchestrate imprinted expression . However despite showing widespread imprinted expression of genes in placenta , allele-specific transcriptional profiling revealed that not all placenta-specific DMRs coordinate imprinted expression and that this maternal methylation may be absent in a minority of samples , suggestive of polymorphic imprinted methylation .
In mammals , DNA methylation of CpG dinucleotides has been shown to play critical roles in many developmental processes including cellular differentiation , X chromosome inactivation and genomic imprinting . DNA methylation patterns are initially established by the de novo DNA methyltransferase DNMT3A [1] , with the methylation profile faithfully maintained during DNA replication by the maintenance methyltransferase DNMT1-UHRF1 complex [2] . It has recently been shown that the gametes from both mouse and humans possess large intervals of opposing methylation [3–7] . Within a few hours after fertilization , a wave of global epigenetic reprogramming ensures that methylation at the blastocyst stage is at their lowest level , erasing the majority of this gametic epigenetic information [3 , 5 , 7] . However , some specific sequences survive this demethylation process , specifically those located within imprinted regions and certain repeat subtypes . Imprinted genes are only transcribed from one parental allele leading to parent-of-origin specific expression , with allelic expression directly controlled by allelic methylation [8] . To date all imprinted domains contain at least one differentially methylated region ( DMR ) that acquires methylation during gametogenesis ( germline DMR , or gDMR ) , and maintained throughout development . Some imprinted loci also contain DMRs that become allelically methylated in the embryonic diploid genome ( somatic DMRs , or sDMR ) which are under the hierarchical influence of gDMRs [4 , 9 , 10] . Recently , transiently methylated germline DMRs ( tDMRs ) have been identified in mice that are indistinguishable from ubiquitous imprinted gDMRs in gametes and preimplantation embryos [11] . The maternally methylated tDMRs described in mouse subsequently gain methylation on their paternal alleles at implantation , having first survived the post-fertilization demethylation process . This reprogramming to a totipotent state starts in the male pronucleus with TET3-mediated conversion of 5-methylcytosine ( 5mC ) to 5-hydroxymethylcytosine ( 5hmC ) [12] with subsequent replication-dependent dilution of methylation of both maternal and paternal genomes occurring during the first 2 days of human development [5 , 7 , 13] . Unlike mice , in humans it is currently unknown how many germline differences survive embryonic reprogramming and persist in humans , either as ubiquitous imprinted gDMRs or tDMRs . However initial screens suggest that oocyte-derived tDMRs may be present to the blastocyst stage [7 , 14] . Here , we present the data describing the fate of germline-derived methylation in humans . Using publically available methyl-seq datasets from gametes , preimplantation embryos , placenta and somatic tissues , we identify 53 , 549 methylation differences between gametes , the majority being methylated in the sperm and not in oocytes . With the exception of a few paternally methylated gDMRs associated within known imprinted domains , we demonstrate that sperm-derived methylation is reprogrammed by the blastocyst stage . In contrast a large number of oocyte-derived methylation differences survive to the blastocyst stage , persisting as maternally methylated DMRs in the placenta only , expanding the number of placenta-specific DMRs reported using high-density array based screens [10 , 15–17] . Furthermore , we demonstrate that this phenomenon is exclusive to humans and non-human primates since no placenta-specific maternal methylation was observed in other mammalian species . Utilizing single cell RNA-seq datasets from human preimplantation embryos [18] we show that following embryonic genome activation the maternally methylated gDMRs orchestrate imprinted expression in preimplantation embryos . However , despite showing imprinted expression of many genes , transcriptional profiling revealed that not all placenta-specific maternally methylated DMRs coordinate imprinted expression suggesting differential reading of this epigenetic mark during human embryonic development .
Transient maternally inherited monoallelic methylation has been previously observed in mouse . To identify candidate loci in humans we searched for regions that are differentially methylated between sperm and oocytes . Using defined criteria ( see methods ) we identified 5 , 438 oocytes and 48 , 111 sperm-derived DMRs . A high proportion of regions methylated in sperm and hypomethylated in oocytes were intergenic or map to repeat elements , consistent with previous observations [5] . In contrast , oocyte-specific DMRs were more uniformly distributed throughout the genome , often overlapping promoter CpG islands . Eighty percent of the oocyte-derived DMRs ( n = 4 , 352 ) remain partially methylated at the blastocyst stage , which is consistent with methylation dynamics during the progression of cleavage stage embryos to blastocysts [6 , 7] with very few sperm-derived DMRs surviving to the blastocyst stage ( 1% , n = 517 ) ( Fig 1A and 1B ) . This reprogramming is particularly evident when the size of the gDMRs surviving to the blastocyst stage is taken into consideration . In total ~7 Mb of the human genome encompasses oocyte-derived gDMRs of which 74% is hemimethylated in preimplantation embyros , whereas ~2 . 7 Mb is covered by sperm-derived gDMRs of which only 11% is hemimethylated at the same developmental stage . Therefore , maternal gDMRs are lost after the blastocyst stage whereas the methylation at paternal gDMRs is largely removed during preimplantation stages , possible occurring before the first cleavage division arguing against a simple replication-dependent demethylation of the maternal genome during preimplantation development . Numerous studies have shown that gDMR that persist uniformly in somatic tissues act as imprinting control regions . To date 49 ubiquitous imprinted DMRs have been identified in humans using high-density methylation arrays [10] . To determine if additional imprinted DMRs are present in the human genome , we determined the methylation profile of the oocyte and sperm-derived gDMRs that are present with preserved methylation in methyl-seq datasets in blastocysts , placenta and 14 different somatic tissues . We observe only one sperm-derived region mapping to a known paternally methylated DMR in > 12 tissues , the H19 gDMR on chromosome 11 . The only additional known paternally methylated DMR originating from sperm in humans , the IG-DMR on chromosome 14 , was differently methylated between gametes but was partially methylated in blastocysts and five somatic tissues only . Using the same criteria we observe 60 oocyte-derived DMRs in >12 tissues , including 25 known maternally methylated imprinted DMRs ( S1 Table ) . Of the remaining intervals not associated with known imprinted gDMRs , we confirm FANCC and SVOPL as being novel ubiquitous imprints ( S1 Fig; Fig 1C and 1D ) . Using allele-specific RT–PCR that incorporated a coding SNP within exon 5 , we observed maternal expression of SVOPL in placenta and monoallelic expression in brain and leukocytes ( Fig 1E ) . Unfortunately we could not identify any informative samples to allow for the allelic expression of FANCC to be ascertained . To determine if germline-derived DMRs are maintained in a tissue-specific fashion we screened for loci partially methylated in only one tissue ( Fig 2A and 2B ) . This analysis revealed that 551 of the partially methylated regions in blastocysts inheriting methylation from the oocyte survived only in the placenta , whereas only 38 regions inheriting methylation from sperm were identified in this extra-embryonic tissue ( S2 Table ) . Since standard bisulphite conversion based technologies cannot distinguish between 5mC and 5hmC , we utilized methylation-sensitive genotyping assays that can distinguish these two forms based on the addition of a glucose moiety to yield glucosyl-5-hydroxymethylcytosine . This , combined with allele-specific bisulphite PCR , revealed no novel paternally methylated placenta-specific gDMRs since all candidates were mosaically methylated , but maternal placenta-specific gDMRs were abundant and specifically associated with 5mC ( S2 , S3 and S4 Figs; S2 and S3 Tables ) . The fate of 5mC at maternal placenta-specific gDMRs in somatic tissues was largely influenced by sequence content . The confirmed placenta-specific maternal methylation regions were almost always high CG content intervals robustly unmethylated in somatic tissues , whereas hypermethylated loci in somatic tissues were often false positives being partially methylated regardless of the parental allele , with the exceptions of four loci which we confirm as maternally methylated ( TMEM247 , GPR1-AS1 , ZFAT , and C19MC ) ( S5 and S6 Figs ) [19 , 20] . This reflects the general methylation status of the placenta , which is relatively hypomethylated across the genome , including repeat elements [21 , 22] . For example the GRID2 gene is associated with two maternally methylated gDMRs with different genomic content . The promoter CpG island is robustly methylated on the maternal allele in placenta and is unmethylated in somatic tissues , whereas an intergenic region within intron 3 , consisting of an Alu/SINE repeat , is a gDMR with a mosaic methylation profile in placenta that is fully methylated in all somatic tissues ( Fig 2C and 2D ) . We observe robust maternal methylation associated with multiple members of two large gene families , the fibroblast growth factors ( FGF8 , FGF12 and FGF14 ) and calcium channel , voltage-dependent channel subunits ( CACNA1A , CACNA1C , CACNA1E and CACNA1I ) as well as several gene involved in epigenetic regulation ( JMJD1C and DNMT1 ) and microRNA processing ( LIN28B and EIF2C1 ) ( S3 and S4 Figs ) . These results therefore reveal that placenta-specific gDMRs are much more abundant in the human genome than previously reported . Using nested-multiplex bisulphite PCR , we confirmed the methylation profiles of four ubiquitous imprinted gDMRs ( H19 , MCTS2 , FANCC , SVOPL ) and 13 placenta-specific gDMRs in sperm and blastocysts micosurgically separated into inner cell mass ( ICM ) and trophectoderm ( TE ) ( Fig 3 and S4 Fig ) . The R3HCC1 loci on chromosome 8 exemplified the fate of opposing germline methylation difference as this gene has adjacent oocyte and sperm-derived gDMRs . Using bisulphite PCR we show that the maternally methylated gDMR is observed in ICM/TE and term placenta , whereas the paternally methylated gDMR , which was not identified in our initial genome-wide screen since it does not reach our screening criteria having < 25 CpGs , resolves to a mosaic methylated state at the blastocysts stage ( Fig 3C and 3D ) . We have previously shown that 14 orthologs of maternally methylated placenta-specific DMRs are devoid of methylation in the mouse placenta [10] . Using methyl-seq datasets from mouse placenta with bisulphite PCR confirmation we show that no human placenta-specific DMRs are conserved in mice ( S7A and S7B Fig ) . Similarly the mouse orthologous regions corresponding to the SVOPL and FANCC DMRs also lack allelic methylation and are biallelically expressed in multiple tissues ( S7B Fig ) . Several studies have shown that maternally methylated gDMRs mark different loci in mouse compared to humans [3 , 5] , suggesting that the mouse genome may possess a unique set of placenta-specific DMRs inherited from the female germline . We therefore determined the fate of oocyte-derived gDMRs in hybrid mouse placenta . Consistent with our previous observation , no maternal gDMRs persist as placenta-specific DMRs , reinforcing that this phenomenon is not observed in mice ( S7C Fig ) . Recently methyl-seq datasets have been produced from different mammalian species , including rhesus macaque , horse , cow and dog [23] . Similar to mouse , the orthologues of the vast majority of human placenta-specific gDMRs do not have a methylation profile consistent with imprinting in non-primate species ( S7A Fig ) . Using bisulphite PCR on DNA derived from rhesus placenta , we confirm evolutionary conservation of 63% placenta-specific DMRs as well as those associated with the ubiquitously imprinted MCTS2 , GRB10 and L3MBTL1 genes ( S7D Fig ) . The main biological significance of promoter methylation is thought to be transcriptional repression of tissue-specific genes , with methylation levels negatively correlated with expression following genome activation at the 8 cell stage [6] . To determine if maternal-specific methylation at placenta-specific gDMRs dictates paternal expression we performed allele-specific RT-PCR in placenta . Paternal expression was confirmed for nine genes including AGO1 , USP4 , SH3BP2 , FAM149A , MOCS1 , R3HCC1 , JMJD1C , PAK1 and PAPLN-AS ( Fig 4A; S4 Table ) . Curiously however , we observe that not all informative placenta samples exhibited monoallelic expression despite maintaining robust maternal methylation ( Fig 5A , S4 and S8 Figs; S4 Table ) . We also observe paternal expression of a ~10 kb non-coding ( nc ) RNA overlapping a placenta-specific gDMR located 12 kb 3’ to TET3 ( Fig 4B–4D ) . To determine if this ncRNA influences expression in cis , we performed allelic RT-PCR for TET3 . We observe biallelic expression of TET3 suggesting that the neighboring ncRNA does not possess enhancer or repressive function in term placenta ( Fig 4D ) . In total this bringing the total number of confirmed placenta-specific paternally expressed genes to more than 30 [8 , 16] . Polymorphic imprinting has been described for only a few loci in humans , including the IGF2R [24 , 25] and nc886/vtRNA2-1 [26 , 27] , with the latter consistent with being a metastable epiallele . To determine if the placenta-specific gDMRs that we identified show variable methylation on the maternal allele , we performed pyrosequencing to quantify a larger cohort of normal placenta samples from uncomplicated pregnancies . We identified hypomethylated samples for 12 of the regions ( Fig 5B ) , with the most affected loci being LIN28B and AGBL3 . For samples with informative polymorphisms this lack of methylation is associated with biallelic expression ( Fig 5C ) , an observation consistent with some placenta-specific maternal gDMRs being a stochastic polymorphic trait [17] . It has previously been reported that a significant proportion of transcripts are monoallelically expressed in cleavage embryo [17] indicating that maternally methylated placenta-specific gDMRs may regulate allelic expression at this earlier developmental time point . To ascertain if the placenta-specific gDMRs orchestrate imprinted expression , we determined allelic expression in publically available single cell embryo RNA-seq datasets for which paternal genotypes were available [18] . Gene expression profiles were analyzed in individual embryos to determine the progression of expression levels and their allelic origin . To compare embryos at different stages it is important to take into consideration two events , embryonic genome activation and oocyte-derived transcript degradation . Zygotic genome activation ( ZGA ) occurs soon after fertilization ( pre-major ZGA ) and processed in successive waves of activation with the major changes reported at the 4–8 cell stage [28] . Maternal transcript stores in the oocyte cytoplasm are diminished after fertilization by a combination of degradation and recruitment to the polysome and translated prior to ZGA [29] . Transcripts highly abundant at the pronuclear stage and decreasing as developmental proceeds will not be expressed from the embryonic genome and will appear maternally derived . Embryonically transcribed genes that maintain high expression levels from the pronuclear stages would appear maternally expressed before 8-cell stage , switching to imprinted paternal expression with RNA synthesis from the unmethylated allele if the gDMRs are functional . Some instances of biallelic expression maybe wrongly classified since embryonic paternal expression and oocyte-derived transcripts may co-exist until late cleavage stage . Finally , genes that are activated during cleavage embryo development , but not originally expressed in the zygote are predicted to be from the paternal allele . Therefore functional paternal expression can only be categorized after genome activation ( Fig 6A ) . Using these criteria we screened all transcripts near the oocyte-derived gDMRs for imprinting and observed , as proof of principal , the paternal expression of ZHX3 in 8-cell and morula and confirm preferential paternal expression arising from a maternally methylated promoter in multiple term placenta biopsies ( Fig 6B–6E ) . In addition to the reprogramming that occurs immediately after fertilizations from which imprints are protected , reprogramming in primordial germ cells ( PGCs ) of the developing fetus includes all ubiquitous imprints ensuring the transmission of genetic information with the correct epigenetic profile in the gametes [30] . Recently , the methylomes of human PGCs of both sexes have been generated , which confirm that human PGCs at 7–9 weeks gestation are hypomethylated similar to those in the mouse at embryonic day 13 . 5 [31 , 32] . Using these datasets , we confirm that placenta-specific DMRs are devoid of methylation in both male and female PGCs at 10 weeks gestation and are indistinguishable from ubiquitous gDMR imprints ( S5 Table ) . Similar to the ubiquitous gDMR imprints , the majority of placenta-specific gDMRs ( 78% ) are frequently associated with CpG-rich sequences with an intragenic location with evidence of a transcriptional event initiating from upstream promoters ( S5 Table ) . This intragenic location has been shown to be important in facilitating the acquisition of methylation during female germline development [33 , 34] .
In this study DNA methylation in human gametes , embryos , placenta and multiple somatic tissues were used to identify gDMRs that may act as imprints . Using high-density methylation arrays , our group and others have recently identified ~150 maternally methylated DMRs in placenta [10 , 15–17 , 35] , for which we confirm the majority are bona fide germline difference in methylation . A comparison of the oocyte-derived DMRs reported by Smith and colleagues revealed largely overlapping datasets in blastocysts [7] . Using different bioinformatics criteria , 25 continuously CpGs rather than 100bp tiles , our analysis identified ~64% of previously identified loci , with missing regions possible due to inferior sequence coverage of reduced representation bisulphite sequencing or the size of the windows analyzed . Furthermore , using methyl-seq datasets , we identify an additional 551 loci that could represent placenta-specific gDMRs , however only 11% had high informative polymorphisms to allow for allelic discrimination . With the exception of only four regions , these placenta-specific gDMRs are associated with CpG islands or promoter intervals devoid of methylation in somatic tissues . Those regions fulfilling our criteria of partially methylation and hypermethylated in other tissues may simply reflect the relatively hypomethylated nature of the placenta genome that had previously hindered us from performing imprinted DMR analyses in placenta methyl-seq datasets [10] . Recently , Schroeder and colleagues described that the placenta genome has unique partially methylated domains ( PMDs ) that are larger ( >100 kb ) and have lower levels of DNA methylation than the rest of the genome , which are stable throughout gestation [21 , 36] . The placenta-specific gDMRs we describe are much smaller than PMDs having an average size of 2 . 2 kb with only two ( CACNA1I and ZNF385D ) mapping to PMDs . While allelic DNA methylation at ubiquitous gDMR imprints is associated with monoallelic expression , our analysis reveals that only half of all placenta-specific gDMRs orchestrate paternal expression suggesting that despite being maternally methylated , the maternal alleles may not be associated with a compact chromatin state or decorated with repressive histone modifications sufficient to influence transcription . A recent genome-wide screen using diandric and digynic triploid conceptions and RRBS datasets also identified placenta-specific gDMRs , many overlapping with the loci we identify [17] . However , these authors did not perform any allelic expression analyses for their candidates and so the functional relevance of this tissue-specific methylation was not addressed . Furthermore this study revealed epigenetic stochasticity for many of the placenta-specific DMRs described , similar to what we also observe for many of the regions we quantified using pyrosequencing ( Fig 5 ) . However it remains to be determined whether lack of methylation at these loci reflects a random selection of cells not maintaining methylation after embryonic reprogramming or alternatively , exposes loci that fail to establish methylation in the female germline in a polymorphic fashion . We show that allelic methylation is present in the inner cell mass and trophectoderm of human blastocysts , revealing that 5mC is selectively protected from embryonic reprogramming and that it maintained following the first differentiation step . Furthermore , our data suggest that an additional small wave of targeted demethylation exists following implantation in cells specified for the somatic lineages that is absent during placenta differentiation . Very few studies have assessed allelic expression of imprinted genes in human embryos with only paternal expression of IGF2 , SNRPN and MEST being previously reported [37–39] . We show that the placenta-specific gDMRs can influence allelic expression immediately following embryonic genome activation as highlighted by ZHX3 . Unfortunately no additional paternally expressed genes were identified in the embryo datasets due to the lack of informative polymorphisms . Extrapolating this observations means that there are potentially thousands more transiently imprinted genes in the blastocysts associated with the loci which get that reprogrammed after implantation which may have a physiological role in embryonic development . By directly assessing methylation in placenta-derived DNA from different mammalian species we observe that oocyte-derived gDMRs in placenta are largely restricted to primates , being most abundant in humans . These observations are inconsistent with recent reports that oocyte-derived methylation regulates trophoblast development in the mouse [40] . However , this study did not assess allelic methylation per se , but inferred it from various Dnmt3a/Dnmt3b knockout crosses . The developmental phenotype observed could be due to the deregulation of only a few genes such as the maternally expressed Ascl2 ( previously known as Mash2 ) that is regulated in cis by the maternally methylated ubiquitous KvDMR1[41 , 42] . Furthermore strand-specific bisulphite PCR of several of the proposed genes responsible for this developmental phenotype failed to identify methylation specifically on the maternal allele in mouse hybrid placenta ( S9 Fig ) . There are no unifying explanations of how imprinted genes evolved , but there are several theories hypothesized that underscore the importance of the placenta . The most popular theory is associated with the parental conflict and nutrient supply and demand hypothesis [43 , 44] . However with the recent identification of developmentally important genes , including the FGFs that regulate trophoblast survival and placental angiogenesis [45] , and key epigenetic regulators , such as JMJD1C which is involved in regulating early preimplantation development of bovine embryos [46] , we favor the hypothesis that maternal silencing is a mechanism to prevent ovarian teratomas that arise from parthenogenetically activated oocytes [47 , 48] . Our study has shown that oocyte-derived methylation can uniquely be maintained as DMRs in the extra-embryonic lineages , with many placenta-specific DMRs coordinating paternal expression following embryonic genome activation . Our data corroborates the observations that these placenta-specific gDMRs can be polymorphic , with a minority of samples being unmethylated [17] . It remains to be seen if the lack of these placenta-specific DMRs influences pregnancy outcomes and whether they are involved in implantation and preimplantation embryo viability .
Ethical approval for the use of human placenta samples was granted by the Institutional Review Boards at the National Center for Child Health and Development ( project 234 ) , Hospital St Joan De Deu Ethics Committee ( 35/07 ) and Bellvitge Institute for Biomedical Research ( PR006/08 ) . The use of surplus human embryos for this study was evaluated and approved by the scientific and ethic committee of the Instituto Valenciano de Infertilidad ( IVI ) ( 1310-FIVI-131-CS ) , Bellvitge Institute for Biomedical Research Ethics Committee ( PR292/14 ) , the National Committtee for Human Reproduction ( CNRHA ) and the Regional Health Counsel of Valencia . Mouse work was approved by the Institutional Review Board Committees at the National Center for Child Health and Development ( approval number A2010-002 ) . A single placenta sample from rhesus macaque was obtained from the breeding colony of the Biomedical Primate Research Center , Rijswijk , Netherlands using protocols approved by the Committee on the Ethics of Animal Tissue Collection at BPRC ( Permit # 730 ) . The EUPRIM-Net Bio-Bank is conducted and supervised by the scientific government board along all lines of EU regulations and in harmonization with Directive 2010/63/EU on the Protection of Animals Used for Scientific Purposes . A cohort of 72 human term placenta biopsies ( gestational age 35–41 weeks gestation , average 37 weeks ) from uncomplicated pregnancies with their corresponding maternal blood samples were collected at Hospital St Joan De Deu ( Barcelona , Spain ) and the National Center for Child Health and Development ( Tokyo , Japan ) . Written informed consent was obtained from all participants . All placenta biopsies were collected from the fetal side around the cord insertion site . The placenta-derived DNA samples were free of maternal DNA contamination based on microsatellite repeat analysis . Both DNA and RNA extractions and cDNA synthesis were carried out as previously described [22] . Three surplus human blastocysts were recruited at the Fundación Instituto Valenciano de Infertilidad ( FIVI ) in Valencia . The blastocysts were thawed using the Cryotop method following manufacturer’s instructions [49] and incubated in CCM medium ( Vitrolife , Göteborg , Sweden ) for 6–12 hours before microdissection in order to allow their full expansion and the inner cell mass ( ICM ) and trophectoderm ( TE ) , that were subsequently separated by micromanipulation using laser technology ( OCTAX , Herborn , Germany ) . The separated ICMs and TEs were individually placed in PCR tubes containing 2 . 5 μL of PBS and immediately snap frozen at -80°C until processing . Wild type mouse embryos and placentas were produced by crossing C57BL/6 with Mus musculus molosinus or Mus musculus castaneous mice . Animal husbandry and breeding were conducted according to the institutional guidelines for the care and the use of laboratory animals . A single placenta sample from rhesus macaque ( animal 95023 ) was obtained from the breeding colony of the Biomedical Primate Research Center , Rijswijk , following a C-section procedure . We analysed twenty-eight publicly available methylomes obtain from GEO or NBDC repositories . Two datasets were derived from human oocytes ( JGAS00000000006 ) , 5 from human sperm ( JGAS00000000006 and GSE30340 ) , 3 from brain ( GSM913595 , GSM916050 , GSM1134680 ) 3 from CD4+ lymphocytes ( GSE31263 ) , 2 from liver ( GSM916049 , GSM1134681 ) and individual datasets from preimplantation embryos ( JGAS00000000006 ) , placenta ( GSM1134682 ) , muscle ( GSM1010986 ) , CD34+ cells ( GSM916052 ) , sigmoid colon ( GSM983645 ) , lung ( GSM983647 ) , aorta ( GSM983648 ) , esophagus ( GSM983649 ) , small intestine ( GSM983646 ) , pancreas ( GSM983651 ) , spleen ( GSM983652 ) , adrenal ( GSM1120325 ) and adipose tissue ( GSM1010983 ) . Methylation calls were mapped to the hg19 genome . CpG methylation values were calculated using reads from both strands as ( methylated / ( methylated + unmethylmated ) . Only CpGs covered by at least 5 reads were considered for the analysis . For samples with duplicates , the average of methylation was used except for oocyte samples that present a low coverage . For this sample the methylated and unmethylated calls of the two experiments were sum to calculate the methylation ratio . Using the cut off of 5 reads per CpG , the coverage of all experiment vary from 89 . 6% up to 96 . 9% of all the CpGs , except for the oocyte methylomes that cover 54 . 8% of CpGs sites . The methylomes for oocyte and sperm were screen with a sliding windows approach to identify methylated and umethylated intervals . Windows were defined as 25 consecutive CpGs and was only considered if the methylation levels was present for at least 10 CpG sites . This windows was classified methylated if mean25CpGs—1SD25CpGs > 0 . 75 and unmethylated if mean25CpGs + 1SD25CpGs < 0 . 25 . Overlapping windows with the same classification were merge and allowed us to identify 40025 unmethylated ( Us ) and 177787 methylated ( Ms ) region in sperm and 118853 unmethylated ( Uo ) and 102858 methylated ( Mo ) regions in oocyte . A germline DMR was identify when opposite methylated regions in sperm and oocyte overlap for more than 25 CpGs and the position defined by the overlapping difference between methylated regions in sperm and oocyte . Intermediately methylated region in blastocysts , placenta and somatic tissues were identify using the sliding windows approach with the following criteria 0 . 2 < mean25CpGs +/- 1 . 5SD25CpGs < 0 . 8 . Consecutive windows on each sample were fused to generate only a single region . A gDMR was considered to be conserved in preimplantation embryo if the gDMR overlap with a partially methylated region in the blastocyst dataset . To identify the gDMR that persist in somatic tissues , all partially methylated region obtain in the 15 tissues were merge and the number of samples partially methylated for each region is attribute to each region . Only regions > 500 bp were considered to generate the partial methylation region in tissues . To be considered as a ubiquitous gDMR , the partially methylated regions have to persist in the blastocyst and in at least 12 somatic tissues . Placenta-specific gDMR were identified when the partially methylated region is conserved at blastocyst stage but is not observed in additional tissues methylomes . All positional annotations ( CpG islands , repeats and gene locations , etc ) were obtained from UCSC web browser and genome build hg19 . We used the methyl-seq datasets from GSE63330 that contains placenta methylation information from rhesus macaque , dog , horse , cow and mouse [23] . The orthologous genomic intervals associated with the 551 human oocyte-derived gDMR that maintained an intermediate methylation profiles throughout embryonic reprogramming and in placenta were extracted using the UCSC LiftOver function . The abundance and genotypes of highly informative exonic SNPs within the transcripts flanking the gDMRs that maintained an intermediate methylation profile in blastocysts were called using Tophat v1 . 4 . 0 [50] ( for the alignment ) and Samtools v1 . 2 [51] ( for the filtering and allelic count ) in two published single cell RNA-seq datasets for preimplantation embryos ( GSE44183 [18]; GSE36552 [28] ) . For the purpose of this study the data from individual cells were merged to reconstruct each embryo . In the case of the GSE44183 dataset the embryonic genotypes were compared to the accompanying paternal exome-seq data from the sperm donor’s blood sample . Genotypes of potential SNPs identified in the UCSC hg19 browser were obtained by PCR and direct sequencing . Sequence traces were interrogated using Sequencher v4 . 6 ( Gene Codes Corporation , MI ) to distinguish heterozygous and homozygous samples . Heterozygous sample sets were analyzed for either allelic expression using RT-PCR , methylation-sensitive genotyping or bisulphite PCR , incorporating the polymorphism within the final PCR amplicon so that parental alleles could be distinguished ( for primer sequences see S6 Table ) . 5hmC- 5 μg of heterozygous placenta DNA was subject to DNA Glucosylation using the Epimark kit ( New England Biolabs ) and the DNA subject to digestion with 100 units of Msp1 for a minimum of 8 hours at 37°C . The DNA was subject to proteinase K digestion prior to PCR . 5mC- Approximately 1 μg of heterozygous placenta DNA was digested with 10 units of HpaII restriction endonuclease for 6 hours at 37°C . The digested DNA was subject to ethanol precipitation and resuspended in a final volume of 20 μl TE . Approximately 50 ng of digested DNA was used in each amplification reaction using Bioline Taq polymerase for 35–40 cycles ( for primer sequences see S6 Table ) . The resulting amplicons were sequenced and the sequences traces compared to those obtained for the corresponding undigested DNA template . For standard bisulphite conversion approximately 1 μg DNA was subjected to sodium bisulphite treatment and purified using the EZ DNA methylation-Gold kit ( ZYMO , Orange , CA ) . Approximately 2 ul of bisulphite converted DNA was used in each amplification reaction using Immolase Taq polymerase ( Bioline ) at 45 cycles and the resulting PCR product cloned into pGEM-T easy vector ( Promega ) for subsequent subcloning and sequencing ( for primer sequence see S6 Table ) . Surgically separated ICM and TE biopsies were subject to bisulphite conversion using the EZ DNA Methylation-Direct kit ( ZYMO , Orange , CA ) . We employed a multiplex nest PCR approach to maximize data generation . Two sets of primers were designed to each locus and robustly optimized in placenta-derived bisulphite DNA to ensure efficient amplification of both methylated and unmethylated strands at a single annealing temperature without contamination or the formation of primer dimer . All subsequent outer primers ( for ~20 separate loci ) were co-amplified in the first reaction using Immolase Taq polymerase ( Bioline ) for 45 cycles . Second round of amplifications specific to each region , also 45 cycles , utilized locus-specific inner primers using 1ul of first round PCR as template . All second round nested PCR products were subcloned into pGEM-T easy vector for direct sequencing ( for primer sequence see S6 Table ) . Approximately 50 ng of bisulphite converted DNA was used for pyrosequencing following previously described protocols [22] . Standard bisulphite PCR was used to amplify the imprinted DMRs with the exception that one primer was biotinylated ( for primer sequences see S6 Table ) . For sequencing , forward primers were designed to the complementary strand . The pyrosequencing reaction was carried out on a PyroMark Q96 instrument . The peak heights were determined using Pyro Q-CpG1 . 0 . 9 software ( Biotage ) .
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Differences in gamete DNA methylation is subject to genome-wide reprogramming during preimplantation development to establish an embryo with an epigenetic state compatible with totipotency . DNA sequences associated with imprinted differentially methylated regions ( DMRs ) are largely protected from this process , retaining their parent-of-origin epigenetic marks . By comparing the methylation profiles of human oocytes , sperm , blastocysts and various somatic tissues including placenta , we observe hundreds of CpG island sequences that maintain methylation on their maternal allele in blastocysts and placenta indicative of incomplete reprogramming . In some cases this maternal methylation influence transcription of nearby genes , revealing transient imprinting in embryos after genome-activation and in placenta . Strikingly , these placenta-specific DMRs are polymorphic between placenta samples with a minority of samples being robustly unmethylated on both alleles .
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2016
|
Human Oocyte-Derived Methylation Differences Persist in the Placenta Revealing Widespread Transient Imprinting
|
Cyclic dimeric GMP ( c-di-GMP ) is a bacterial second messenger that modulates many biological processes . Although its role in bacterial pathogenesis during mammalian infection has been documented , the role of c-di-GMP in a pathogen's life cycle within a vector host is less understood . The enzootic cycle of the Lyme disease pathogen Borrelia burgdorferi involves both a mammalian host and an Ixodes tick vector . The B . burgdorferi genome encodes a single copy of the diguanylate cyclase gene ( rrp1 ) , which is responsible for c-di-GMP synthesis . To determine the role of c-di-GMP in the life cycle of B . burgdorferi , an Rrp1-deficient B . burgdorferi strain was generated . The rrp1 mutant remains infectious in the mammalian host but cannot survive in the tick vector . Microarray analyses revealed that expression of a four-gene operon involved in glycerol transport and metabolism , bb0240-bb0243 , was significantly downregulated by abrogation of Rrp1 . In vitro , the rrp1 mutant is impaired in growth in the media containing glycerol as the carbon source ( BSK-glycerol ) . To determine the contribution of the glycerol metabolic pathway to the rrp1 mutant phenotype , a glp mutant , in which the entire bb0240-bb0243 operon is not expressed , was generated . Similar to the rrp1 mutant , the glp mutant has a growth defect in BSK-glycerol medium . In vivo , the glp mutant is also infectious in mice but has reduced survival in ticks . Constitutive expression of the bb0240-bb0243 operon in the rrp1 mutant fully rescues the growth defect in BSK-glycerol medium and partially restores survival of the rrp1 mutant in ticks . Thus , c-di-GMP appears to govern a catabolic switch in B . burgdorferi and plays a vital role in the tick part of the spirochetal enzootic cycle . This work provides the first evidence that c-di-GMP is essential for a pathogen's survival in its vector host .
Bis- ( 3′-5′ ) -cyclic dimeric guanosine monophosphate ( c-di-GMP ) , discovered by Benziman and colleagues in the mid-80s [1] , is now widely recognized as a ubiquitous second messenger that modulates many aspects of biological processes in bacteria ( for reviews , see [2] , [3] , [4] ) . C-di-GMP is synthesized by diguanylate cyclases ( DGCs ) , a group of GGDEF domain-containing proteins , and is broken down by phosphodiesterases ( PDEs ) that contain a conserved EAL or HD-GYP domain [5] , [6] , [7] , [8] , [9] , [10] , [11] . GGDEF , EAL and HD-GYP domains are among the most abundant domains encoded in bacterial genomes [5] , [12] . Numerous studies on c-di-GMP signaling pathways in the Proteobacteria revealed that c-di-GMP controls the transition between planktonic and biofilm lifestyles by stimulating the biosynthesis of adhesins and exopolysaccharide matrix substances in biofilms while inhibiting various forms of motility [13] , [14] , [15] , [16] , [17] , [18] , [19] . Several classes of c-di-GMP receptor/effector proteins have been identified [20] . Despite tremendous progress , the role of c-di-GMP in bacterial pathogenesis and the mechanisms of action of c-di-GMP remain poorly understood [4] , [21] , [22] . Further , very little is known about the function of c-di-GMP beyond Proteobacteria . Borrelia burgdorferi is a spirochete that causes Lyme disease , the most prevalent vector-borne infection in the United States [23] . As an obligate pathogen , B . burgdorferi has a reduced genome that contains a limited number of genes that are known to be involved in signal transduction and gene regulation [24] , [25] . For instance , the genome only has two sets of two-component signal transduction systems: Hk1-Rrp1 ( BB0420-BB0419 ) and Hk2-Rrp2 ( BB0764-BB0763 ) , in addition to the chemotaxis CheA-CheY system . On the other hand , the enzootic life cycle of B . burgdorferi is complex . It involves two markedly different hosts , an arthropod vector and a small mammal . This unique lifestyle requires B . burgdorferi to utilize its limited signaling capabilities for adapting to dramatic changes in host environments during its natural cycle . In this regard , the Hk2-Rrp2 two-component signaling pathway has been shown to modulate differential expression of numerous surface lipoprotein genes and plays an essential role for spirochetal transmission and mammalian infection [26] , [27] , [28] , [29] , [30] . Little is known about the function of the second two-component system present in B . burgdorferi , Hk1-Rrp1 . The response regulator Rrp1 contains an N-terminal response regulator receiver domain and a C-terminal GGDEF domain [8] . Ryjenkov et al . demonstrated that recombinant Rrp1 has DGC activity that strictly depends on the phosphorylation status of Rrp1 [8] . The complete enzootic cycle of B . burgdorferi and the pathogenesis of the disease can be largely reproduced in the laboratory [31] . Rrp1 is the only GGDEF-domain protein in B . burgdorferi , making this organism attractive for uncovering the role of c-di-GMP-mediated signaling in bacterial pathogenesis [5] , [8] . Two recent studies have shed light on the potential role that c-di-GMP plays in the life cycle of B . burgdorferi . Rogers et al . showed that rrp1 is significantly upregulated upon tick feeding [32] . They also generated an rrp1 mutant in the non-infectious clone B31 5A13 . The mutant showed altered expression of more than 140 genes ( 8% of the genome ) whose functions covered almost all functional categories , including cell envelope biosynthesis , transport , metabolism , chemotaxis , and flagellar biosynthesis [32] . The rrp1 mutant also showed reduced growth at room temperature and increased serum sensitivity [32] . Another study focused on BB0363 , the only EAL-domain protein encoded in the B . burgdorferi genome [33] . Recombinant BB0363 was shown to have c-di-GMP phosphodiesterase activity . The bb0363 mutant , which likely has high intracellular levels of c-di-GMP , was found to be defective in motility in vitro [33] . In vivo , the bb0363 mutant was able to survive in ticks but failed to establish infection in mice , suggesting that high levels of c-di-GMP are detrimental for spirochetes to replicate in a mammalian host . However , whether c-di-GMP is required for any stage of the infectious cycle of B . burgdorferi remains undetermined . In this study , we generated an rrp1 mutant in the infectious clone of B . burgdorferi , B31 5A4NP1 . We show that Rrp1 is dispensable for mammalian infection but is essential for spirochetal survival in the tick vector . We further show that the Rrp1 requirement is , in part , due to its control over the expression of glycerol transport and metabolism in B . burgdorferi .
To determine the role of c-di-GMP in B . burgdorferi pathogenesis , we constructed an rrp1 mutant in the infectious B . burgdorferi strain 5A4NP1 ( See Table 1 for a list of strains used in this study ) . This was accomplished by replacing the wild-type chromosomal rrp1 copy with a disrupted gene via homologous recombination ( Fig . 1A ) . A similar approach was used to repair the wild-type rrp1 gene by replacing the mutated copy with a wild-type rrp1 ( Fig . 1A ) . The genotypes of the rrp1 mutant and the repaired strain ( rrp1com ) were confirmed by PCR ( Fig . 1B ) and by immunoblot analyses ( Fig 1C ) . To determine the role of c-di-GMP in mammalian infection , we needle-inoculated groups of mice with various B . burgdorferi strains ( 105 spirochetes/mouse ) . Two-weeks post inoculation , ear punch biopsies were cultured in BSKII medium for the presence of spirochetes . Similar to wild-type spirochetes , the rrp1 mutant was readily detected in either immunocompetent ( C3H/HeN ) or immunocompromised ( C3H-SCID ) mouse strains ( Table 2 ) . No major difference in ID50 values between wild-type and the rrp1 mutant ( Table 3 ) . Further analysis of histopathology revealed that the rrp1 mutant elicited Lyme arthritis similar to that induced by wild-type B . burgdorferi . ( Supplemental Fig . S1 ) . This result indicates that abrogation of c-di-GMP synthesis does not affect the ability of B . burgdorferi to infect mice . We conclude that c-di-GMP is dispensable for mammalian infection . This is in contrast with an avirulent phenotype observed in B . burgdorferi lacking the c-di-GMP phosphodiesterase BB0363 [33] . To examine the rrp1 mutant's phenotype in the tick cycle , groups of pathogen-free Ixodes scapularis larvae were fed on C3H/SCID mice that were needle-infected with the wild-type , rrp1mut or rrp1com strains two weeks after infection . Engorged larvae were collected after repletion and tick contents were subjected to immunofluorescence assay ( IFA ) . In contrast to the wild-type and rrp1com strains that were readily detectable in fed larvae , virtually no rrp1 mutant spirochetes were observed ( Fig . 2A ) . Further quantitative PCR analysis revealed that there were significantly lower numbers of the rrp1 mutant than that of wild-type or rrp1com strains in ticks ( Fig . 2B ) . The inability to detect the rrp1 mutant in tick midguts after feeding could be due either to a defect in tick midgut survival or a defect in migration from the mouse to the tick . To test these two possibilities , we used microinjection to directly place spirochetes into midguts of nymphal ticks [34] , [35] . These artificially infected ticks then fed on naïve mice . Detached ticks were collected and subjected to IFA analysis . As shown in Fig . 3 , the wild-type and rrp1com strains were readily detectable in ticks , whereas the rrp1 mutant remained undetected . To confirm that the rrp1 mutant is defective in the ability to survive in ticks , engorged larvae that were fed on infected mice from the experiments described above were allowed to molt to nymphs in an environmental chamber . Unfed nymphs were then fed on naïve mice . Ticks that were infected with either the wild-type or rrp1com strains could readily infect naïve mice , whereas ticks infected with the rrp1 mutant could not ( Table 2 ) . Similarly , ticks that were artificially infected with the rrp1 mutant were also unable to infect C3H/SCID mice ( Table 2 ) . These results support the notion that the rrp1 mutant is unable to survive in the tick vector . To investigate the molecular mechanisms underlying the requirement of c-di-GMP for spirochete survival in ticks , we sought to identify genes whose expression was affected by the deletion of rrp1 . To do so , we performed two independent microarray analyses: one comparing transcriptional profiles of the wild-type and rrp1 mutant and the other comparing transcriptional profiles of the rrp1 mutant and the rrp1com strain . The comparison of the transcriptomes of the wild-type and rrp1 mutant revealed 120 genes whose expressions were up- or down-regulated by Rrp1 ( cut-off >3-fold ) ( Text S1 ) . Among these , 39 genes whose dependence on Rrp1 could be confirmed by the comparison of the transcriptomes of the rrp1 and rrp1com strains ( cut-off >3-fold ) ( Table 4 ) . We considered these genes to be the most reliable candidates for Rrp1-dependent regulation . Genes regulated by Rrp1 are distributed throughout the genome and extra-chromosomal segments of B . burgdorferi ( Table 4 , Locus numbers start with BB and a letter are extra-chromosomal genes [24] ) . Among these genes , an intriguing target of Rrp1 regulation was an apparent glp operon encoding glycerol transport/metabolism genes , bb0240-bb0243 [24] , [25] , [36] , [37] . The first gene of the operon , bb0240 , encodes a putative glycerol uptake facilitator ( GlpF ) , followed by a putative glycerol kinase gene ( bb0241 , glpK ) , a small putative hypothetical gene ( bb0242 ) , and a putative glycerol-3-phosphate dehydrogenase gene ( bb0243 , glpA/glpD ) . Glycerol can be utilized in energy production as a biosynthetic precursor to membrane lipids or lipoproteins [24] , [25] , [36] , [37] . qRT-PCR analysis confirmed that induction of bb0240-bb0243 was indeed under the control of Rrp1 ( Fig . 4A ) . We hypothesized that if bb0240-bb0243 is involved in glycerol transport and metabolism , the rrp1 mutant may have a defect in the utilization of glycerol as a carbon source . To test this hypothesis , the wild-type , rrp1 and rrp1com strains were cultivated in either standard BSKII medium or in a modified BSKII medium where glucose was replaced with glycerol ( BSK-glycerol , which was prepared from glucose free CMRL ) [37] . The rrp1 mutant was not impaired in growth in the standard BSKII medium at either 35°C ( Fig . 4B ) or 23°C ( Fig . 4D ) . However , when grown in the BSK-glycerol medium , the rrp1 mutant failed to reach the cell density of the wild-type or rrp1com ( Fig . 4C and 4E ) . Thus , glycerol transport and metabolism appeared to be particularly important at later time points in the growth curve . BSK medium is a complex medium containing many undefined components including rabbit serum and BSA as well as other potential carbon source such as pyruvate . Presence or absence of pyruvate did not significantly affect the growth of either wild-type or the rrp1 mutant in BSK-II or BSK-glycerol medium ( data not shown ) . BSK-glycerol medium also contains 0 . 1 g/L of glucose , determined by D-Glucose Kit ( Roche Applied Science , Indianapolis , IN ) , which may contribute to the initial growth of the rrp1 mutant in BSK-glycerol medium ( the standard BSK-II medium contains 6 g/L of glucose ) . Nevertheless , these experiments verified the involvement of Rrp1 in glycerol transport/metabolism . We further tested the possibility that expression of rrp1 is also influenced by glycerol . RNA was extracted from wild-type B . burgdorferi grown in either standard BSKII or BSKII-glycerol medium . The extracted RNAs were subjected to qRT-PCR analysis . Growth in the BSKII-glycerol medium did not significantly alter expression of Rrp2-dependent genes such as rpoS and ospC . However , the transcript level of rrp1 as well as the glycerol metabolic genes bb0240-bb0243 were dramatically upregulated when grown in BSKII-glycerol medium ( Fig . 5A ) . However , Rrp1 protein level is much less influenced by this growth condition ( 1 . 7 fold ) ( Fig . 5B ) . Nevertheless , this observation suggests that glycerol may potentially enhance rrp1 expression . Because Rrp1 was required for full induction of the glycerol operon and for maximal growth in the BSKII-glycerol medium , we hypothesized that defective glycerol metabolism by the rrp1 mutant could contribute to the mutant's inability to survive in ticks . If so , a mutant defective in glycerol metabolism would be expected to have a phenotype similar to that of the rrp1 mutant . To test this hypothesis , we constructed a glp mutant by deleting a portion of the first gene bb0240 and its upstream promoter of the bb0240-bb0243 operon ( Fig . 6A ) . qRT-PCR analysis confirmed that the glp mutant lacks bb0240 bb0241 , bb0242 , and bb0243 mRNA ( Fig . 6B ) . Expression of bb0240-bb0243 was restored when the mutated bb0240 gene and the promoter region was replaced by the wild-type copy of bb0240 at the native location ( designated as glpcom . Fig . 6A and 6B ) . We first examined the growth phenotype of the glp mutant in vitro . The mutant had no detectable growth defect when grown in standard BSKII medium ( Fig . 6C ) . However , similar to the rrp1 mutant , the glp mutant could not reach the same cell density as the parent wild-type strain when grown in the BSK-glycerol medium ( Fig . 6D ) . This defect resulted from abrogation of bb0240-0243 expression , as the growth defect was readily restored upon restoration of bb0240-bb0243 expression in glpcom ( Fig . 6A & 6D ) . This result is consistent with the prediction that the growth defect of the rrp1 mutant in the BSK-glycerol medium is due to the loss of expression of bb0240-bb0243 . We then examined the phenotype of the glp mutant in vivo . The wild-type , glp mutant or glpcom spirochetes ( 105 spirochetes/mouse ) , were intradermally inoculated into C3H/HeN mice . Two weeks after inoculation , ear punch biopsies from all mice were culture-positive for spirochetes , suggesting that BB0240-BB0243 are not required for mammalian infection ( Table 5 ) . Further determination of the ID50 values showed that the glp mutant has a slight infectivity deficit relative to wild-type B . burgdorferi , with 1-log-unit increase in the ID50 ( Table 3 ) . To examine the role of bb0240-0243 in the tick-mouse cycle , pathogen-free unfed larvae were placed on infected mice . Fed larvae were collected and allowed to molt to nymphs . Unfed nymphs then fed on groups of naïve C3H/HeN mice . Ticks at various stages were collected for IFA and/or qRT-PCR analyses . We observed that although detectable in ticks , the glp mutant had reduced spirochetal loads compared to the wild-type or glpcom strains ( Fig . 7A & 7B , only results from nymphs were shown ) . These data suggest that , similar to Rrp1 , the glycerol transport/metabolic pathway is required for the optimal colonization of B . burgdorferi in ticks and that the loss of bb0240-bb0243 expression in the rrp1 mutant contributes to its poor survival in ticks . Mice two weeks post tick feeding were also examined for the presence of spirochetes in various tissue samples ( skin , heart , and joint ) . Unlike the rrp1 mutant that failed to infect mice via tick bites , the glp mutant was capable of completing the tick-mouse cycle and subsequently infecting naïve mice upon tick feeding ( Table 5 ) , despite its reduced survival in ticks . Note that both the glp mutant and glpcom strains showed partially reduced infectivity via tick bites , indicating that this reduction of infectivity is not due to the loss of bb0240-bb0243 ( Table 5 ) . These results indicate that loss of bb0240-bb0243 expression of the rrp1 mutant could not fully account for the inability of the rrp1 mutant to complete its enzootic cycle and that Rrp1 controls additional factor ( s ) involved in the spirochetal life cycle in ticks . To further investigate the role of glycerol transport and metabolism during tick infection , we constitutively expressed the bb0240-bb0243 operon in the rrp1 mutant using an independent flaB promoter ( Fig . 8A ) . The resulting strain , designated as rrp1mut/flaBp-glp , expressed bb0240-bb0243 in an Rrp1-independent fashion ( Fig . 8B ) and fully rescued the growth defect of the rrp1 mutant in the BSK-glycerol medium ( Fig . 4B & 4C ) . This observation provides additional genetic evidence that the growth defect of the rrp1 mutant is due to impaired glycerol transport/metabolism . To compare the phenotypes in ticks , the wild-type , rrp1 mutant , or rrp1/flaBp-glp strains were needle-infected into naïve mice . Unfed larvae were allowed to feed on these infected mice . qPCR analyses on fed larvae showed that the rrp1mut/flaBp-glp spirochetes had a 4- to 5-fold increase in spirochetal load compared to the load of the rrp1 mutant ( Fig . 8C & 8D ) . This increase suggests that restoration of expression of glycerol transport/metabolism can improve survival of the rrp1 mutant in ticks . However , the spirochete load of rrp1/flaBp-glp was still drastically lower than that of wild-type spirochetes ( Fig . 8C & 8D ) . To determine if the rrp1/flaBp-glp spirochetes are able to migrate to mice , fed larvae were collected and allowed to molt to nymphs . Infected nymphs were then used to infect naïve C3H/SCID mice . The result showed that , similar to the rrp1 mutant , the rrp1mut/flaBp-glp strain was incapable of completing the tick-mouse cycle to infect naïve mice ( Table 6 ) . These data further support the conclusions that while glycerol transport/metabolism is important during tick residence , additional Rrp1-dependent factor ( s ) are involved in the tick-mouse cycle of B . burgdorferi .
During the transmission process between mammals and ticks , B . burgdorferi dramatically alters the expression of many genes that are essential for spirochete survival in either host ( for reviews , see [31] , [38] ) . In the past few years , we and others have shown that one of the B . burgdorferi two-component signaling systems , Hk2-Rrp2 , functions as a key signaling pathway that governs expression of genes necessary for mammalian host infection [26] , [27] , [29] , [30] , [39] . In this study , we provide genetic evidence that the other two-component system , Hk1-Rrp1 , is dispensable for mammalian infection , yet plays a vital role in the tick , in part , by controlling expression of the glycerol transport/metabolic pathway of B . burgdorferi . Rrp1 is a diguanylate cyclase responsible for synthesis of the second messenger c-di-GMP [8] , [32] . The importance of c-di-GMP to bacterial pathogenesis has been well documented [4] , [21] , [22] . In many cases , the impact of c-di-GMP on pathogenesis is due to its effect on biofilm formation or motility [40] , [41] , [42] , [43] . An interesting example that is related to this study involves another vector-borne pathogen , Yersinia pestis . Similar to the phenotype of the rrp1 mutant in ticks that we have described herein , disruption of hmsT , a gene encoding diguanylate cyclase in Y . pestis , reduces the transmission of plague bacteria from fleas to mammals [44] , [45] , [46] , [47] . However , the mechanisms of influencing transmission by c-di-GMP in these two pathogens seem to be different . Inactivation of hmsT results in a defect in biofilm formation but not in replication of Y . pestis in fleas , which is important for the spread of Y . pestis from fleas to mammals . Currently there is no evidence that B . burgdorferi forms biofilms . The B . burgdorferi genome encodes a luxS gene responsible for the autoinducer AI2 synthesis , which is necessary for biofilm formation in some bacteria [48] , [49] , [50] . However , inactivation of luxS does not affect the life cycle of B . burgdorferi in either ticks or mice [51] , [52] . Therefore , the mechanism of action of c-di-GMP in the enzootic cycle of B . burgdorferi is different from that of Y . pestis . In addition to affecting biofilm formation and motility , c-di-GMP modulates many other activities that may not be related to multicellular behavior such as cell division , phage resistance , heavy metal resistance , etc . [2] , [3] , [4] . With regards to bacterial pathogenesis , c-di-GMP has been reported to affect the processes of adhesion , invasion , and toxin production by modulating the production or activities of virulence factors [21] , [22] , [53] , [54] , [55] . However , modulation of bacterial infection by the control of glycerol metabolism is observed here for the first time . In this study , we provide the following lines of evidence supporting the notion that c-di-GMP controls glycerol transport and metabolism in B . burgdorferi , which in turn is important for its survival in ticks . 1 ) Expression of bb0240-bb0243 is significantly downregulated by abrogation of Rrp1 ( Table 4 & Fig . 4A ) . 2 ) Both the rrp1 mutant and the glp mutant show growth defects in BSK-glycerol medium ( Fig . 4C & Fig . 6D ) . 3 ) The glp mutant has reduced survival in ticks ( Fig . 7 ) . 4 ) Restoration of bb0240-bb0243 expression in the rrp1 mutant rescues the growth defect in vitro and enhances the survival of the rrp1 mutant in ticks . What roles does glycerol transport/metabolism play in B . burgdorferi physiology ? As an obligate pathogen , B . burgdorferi has a reduced genome and lacks many metabolic pathways such as the TCA cycle and those for synthesis of amino acids , nucleotides , and fatty acids [24] , [25] , [36] . B . burgdorferi does encode proteins for the utilization of several sugars in addition to glucose [24] , [25] , [36] , [37] . Notably , a complete pathway for transport and utilization of glycerol ( BB0240-BB0243 ) is preserved . Bioinformatics analysis suggests that upon uptake of glycerol ( by glycerol uptake facilitator BB0240 , GlpF ) , glycerol is converted to glycerol-3-P by glycerol kinase ( BB0241 , GlpK ) [36] , [37] . Glycerol-3-P can either feed into lipid/lipoprotein biosynthesis or enter the ATP-generating stage of glycolysis via conversion to glyceraldehyde 3-phosphate by glycerol-3-P dehydrogenase ( BB0243 , GlpA/GlpD ) and triosephosphate isomerase ( BB0561 ) [36] , [37] . In other words , the glycerol and glucose pathways interconnect , and glycerol can be an important carbon and energy source at times when glucose becomes limited . This notion is supported by a previous study [37] as well as the in vitro growth data from this study ( Fig . 4B & 4C ) . Based on the observation that the glycerol pathway-defective glp mutant replicates normally in mice but has reduced growth in ticks , we postulate that B . burgdorferi utilizes different carbon/energy sources within each host environment . During mammalian infection when glucose is readily available ( 0 . 1–0 . 2% in mouse blood ) [56] , B . burgdorferi utilizes glucose as the main carbon and energy source . Thus , inactivation of the glp operon does not dramatically affect spirochete replication in mammals . When spirochetes enter the tick vector , initially the glp mutant may be able to replicate with the presence of glucose from blood . Then , glucose may become limiting , while glycerol , on the other hand , may be available in ticks . This notion is consistent with the fact that the glp mutant remains capable of surviving in ticks but with reduced spirochetal numbers ( Fig . 7 ) . It is noteworthy that many insects including ticks produce glycerol as an anti-freezing molecule [57] . Therefore , activation of the glycerol transport and metabolism via Rrp1 could ensure optimal growth of B . burgdorferi in the tick vector . Further , growth on glycerol appears to provide a positive feedback on rrp1 gene expression ( Fig . 5 ) . How does c-di-GMP control the expression of bb0240-bb0243 ? One of the characterized mechanisms employed by c-di-GMP to influence gene regulation is through a unique riboswitch RNA structure . It was shown that c-di-GMP can directly bind to a riboswitch located in the 5′ UTR region of target genes and can influence gene transcription and/or translation [58] . We did not find c-di-GMP-specific riboswitches upstream of bb0240 . C-di-GMP can also modulate gene expression by affecting expression or activity of transcription factors [59] , [60] , [61] , [62] , [63] . Some of these transcription factors bind c-di-GMP directly [64] , [65] . Interestingly , c-di-GMP controls DNA binding of a subgroup of CRP ( cAMP receptor protein ) transcription factors that activate genes involved in utilization of alternative carbon and energy sources ( other than glucose ) . For example , Clp , a CRP homolog from Xanthomonas campestris binds c-di-GMP and regulates virulence gene expression [65] , [66] , [67] . In Vibrio cholerae , it was shown that cAMP-CRP controls expression of a DGC that , in turn , governs the production of c-di-GMP and biofilm formation [68] . Bioinformatic analysis did not identify any CRP homologue encoded in the B . burgdorferi genome . Recently , it was reported that another transcriptional regulator in B . burgdorferi , BosR , also affects glp expression [69] , [70] . Thus , it is possible that c-di-GMP may influence glp via BosR . Nevertheless , elucidating the mechanism of how Rrp1 controls expression of the glycerol pathway in B . burgdorferi will shed light on the interplay between c-di-GMP and carbon utilization networks . Work on Rrp1 from this study and previous studies [8] , [32] strongly supports the notion that c-di-GMP is essential for spirochetal adaptation in the tick vector but is not required for mammalian infection . In fact , c-di-GMP is not only dispensable , shutting down the synthesis of c-di-GMP is necessary for B . burgdorferi to successfully establish infection in the mammalian host . This was recently demonstrated by Sultan et al . , when they showed that the B . burgdorferi mutant missing c-di-GMP phosphodiesterase ( BB0363 ) failed to infect mice [33] . This is consistent with an emerging theme that uncontrolled production of c-di-GMP is detrimental to the acute phase of bacterial infection [3] , [21] , [22] . Thus , a tight regulation of the synthesis of c-di-GMP is important for Borrelia adaptation in both the tick vector and the mammalian host . What are the downstream effectors of c-di-GMP in B . burgdorferi ? The bb0363 mutant showed a defect in motility , suggesting that flagellar proteins or gene transcription of B . burgdorferi may be direct targets of c-di-GMP , as shown in other bacteria [15] , [17] , [68] , [71] . The rrp1 mutant did not have an apparent defect in motility , suggesting that c-di-GMP controls other bacterial factor ( s ) that are important to spirochetal survival in ticks . Note that although c-di-GMP may regulate transcription of flagellar genes [71] , our microarray analysis indicates that flaB expression is not affected by rrp1 deletion and thus using the flaB as the reference gene in this study remains valid . In addition , expression of previously identified genes important for spirochetal survival in ticks , including ospA/B , bptA , dps , bb0365 and lp6 . 6 [34] , [72] , [73] , [74] , [75] , [76] , were not affected by Rrp1 ( Table 4 ) . Although glycerol transport/metabolism is important to the optimal growth of B . burgdorferi in ticks , independent expression of the glycerol transport/metabolism genes in the rrp1 mutant does not fully rescue spirochete survival in ticks , and the rrp1/flaB-glp spirochetes remain incapable of completing its entire enzootic cycle ( Table 6 ) . Thus , c-di-GMP likely controls yet-to-be-identified factor ( s ) that contribute to B . burgdorferi proliferation in ticks . In this regard , relatively few c-di-GMP targets have been identified in other bacteria to date . The best characterized c-di-GMP targets are PilZ domain-containing proteins , such as cellulose synthase subunit BcsA in Gluconacetobacter xylinus and motility regulatory protein YcgR in Escherichia coli [77] , [78] . The B . burgdorferi genome encodes one PliZ protein , PlzA ( BB0733 ) [79] . Interestingly , Freedman et al . showed that plzA expression is upregulated during tick feeding , suggesting a potential role of PlzA in the tick vector [79] . Whether PlzA plays a role in the enzootic cycle of B . burgdorferi remains to be determined . Microarray analyses from this study and previous studies by Roger et al [32] suggest that expressions of several membrane-associated proteins including Rev , Mlps , and Erps are influenced by Rrp1 . Whether these proteins/lipoproteins contribute to B . burgdorferi survival in ticks needs to be further determined . In addition , there are some significant differences between these two microarray results . Roger et al . showed that Rrp1 influences expression of more than 140 genes , most of which are chromosome-encoded core genes [32] . Our study reveals only few chromosome-encoded genes whose expression was affected by rrp1 deletion and such effect could be further restored in rrp1com . One difference between the two studies is the strain used . In this study , an infectious strain B31 5A4NP1 that contains all endogenous plasmids was used , whereas Rogers et al . , a non-infectious strain B31 5A13 that lost lp25 was used [32] . In addition , differences in media used for cultivation of B . burgdorferi might also contribute to differences of the results ( we used BSK-II whereas Roger et al . used commercially purchased BSK-H complete medium [32] ) . Another factor that may contribute to this discrepancy is that many genes revealed by WT/rrp1 microarray analysis could not be confirmed by rrp1com/rrp1 analysis . In fact , there are only 39 genes whose dependence on Rrp1 could be confirmed by rrp1com/rrp1 microarray analysis . We do not fully understand what might contribute to this phenomenon , but it may reflect the complexity of B . burgdorferi plasmid contents and gene regulation . Nevertheless , since the expression of rrp1 as well as the in vitro growth defect and the tick survival defect of the rrp1 mutant were fully restored in rrp1com , the difference between the microarray results of WT/rrp1 and rrp1com/rrp1 is not due to Rrp1 and does not affect the overall conclusion of the work presented in the manuscript . The difference of microarray results observed herein also raises caution on microarray analysis of B . burgdorferi gene expression and reinforces the importance of performing complementation experiments for identification of genes that are truly affected by inactivation of the target gene . What signal activates Rrp1 during tick feeding ? As a two-component response regulator , the diguanylate cyclase activity of Rrp1 is dependent on phosphorylation [8] . The predicted cognate histidine kinase for Rrp1 is Hk1 . Bioinformatics analysis suggests that Hk1 contains a periplasm-located sensor domain homologous to the family 3 periplasmic substrate-binding proteins ( SBP_3 ) [80] . Proteins in this family often bind to amino acids or opine molecules [80] , suggesting that B . burgdorferi may sense such a molecule and activates the c-di-GMP signaling pathway to achieve successful adaptation of the harsh environments of feeding ticks . In summary , the findings on Hk1-Rrp1 and Hk2-Rrp2 two-component systems suggest a seemingly simple signal transduction model in B . burgdorferi . Through evolution , B . burgdorferi reduced its genome and only kept these two sets of two-component systems for the adaptation to each of the two hosts encountered in its entire enzootic life cycle . When spirochetes migrate from ticks to the mammalian host , the Hk2-Rrp2 pathway is activated during tick feeding , leading to the production of OspC , DbpA/B , BBK32 BBA64 and many factors that are important for B . burgdorferi to establish infection in the mammalian host [81] , [82] , [83] , [84] , [85] , [86] . Prior and/or after spirochetes enter the tick gut from mammals , the Hk1-Rrp1 pathway becomes activated , leading to activation of the glycerol pathway and other yet-to-be identified factors to ensure that spirochetes can successfully adapt and replicate in the tick vector .
All animal experimentation 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 of using ticks and mice was approved by the Committee on the Ethics of Animal Experiments and the Institutional Animal Care and Use Committee of Indiana University ( Permit Number: 2976 ) . All surgery was performed under sodium pentobarbital anesthesia , and all efforts were made to minimize suffering . Low–passage , virulent B . burgdorferi strain 5A4NP1 ( Table 1 ) ( a gift from Drs . H . Kawabata and S . Norris , University of Texas Health Science Center at Houston ) was derived from wild-type strain B31 by inserting a kanamycin-resistance marker into the restriction modification gene bbe02 on plasmid lp25 [87] . Borreliae were cultivated in Barbour-Stoenner-Kelly ( BSK-II ) medium [88] supplemented with 6% normal rabbit serum ( Pel Freez Biologicals , Rogers , AR ) at 35°C with 5% CO2 . BSK-glycerol medium was prepared as previously reported by von Lackum and Stevenson , by replacing glucose with an equal amount of glycerol ( 0 . 6% ) and regular CMRL 1066 with glucose-free CMRL [37] . Relevant antibiotics were added to the cultures with the following final concentrations: 200 µg/ml for kanamycin , 100 µg/ml for streptomycin , 50 µg/ml for gentamicin , and 50 ng/ml for erythromycin . The constructed suicide vectors were maintained in E . coli strain TOP10 . The rrp1 ( bb0419 ) mutant was created by allelic exchange in 5A4NP1 by transforming a suicide vector pXY307 ( Fig . 1A ) . To construct pXY307 , a 2663bp sequence from B . burgdorferi chromosome DNA between the coordinates 428794 and 431456 was PCR cloned into pGEM-T ( Promega , Madison , WI ) using primers pri-Rrp1-40 and pri-Rrp1-41 ( Text S1 ) . Then an antibiotic marker flaBp-aadA was inserted into the XbaI site within the rrp1 gene . The protocol used for transformation was described previously [34] , [89] . Numerous streptomycin- and kanamycin-resistant transformants were obtained and the loss of Rrp1 was confirmed by immunoblotting analyses . Endogenous plasmid profiles were determined as previously described [90] , [91] . One of the rrp1 mutant clones that had plasmid profiles identical to the parental strain ( 5A4NP1 ) was chosen for further study . For cis-complementation of the rrp1 mutant , a suicide vector , pMH38 , was constructed ( Fig . 1A ) . pMH38 contains an ermC antibiotic marker flanked by 1 ) a PCR fragment of rrp1 and part of the hk1 ( bb0420 ) region ( using primers priRrp1-F2-PstI-3 and priRrp1-F2-XhoI-5 , Text S1 ) and 2 ) a PCR fragment of the bb0418 region ( with primers priRrp1-F1-SpeI-5 and priRrp1-F1-BamHI-3 ) . pMH38 DNA was transformed into the rrp1 mutant . Erythromycin- and kanamycin-resistant transformants were subjected to immunoblot analyses to confirm the restoration of rrp1 expression . A successfully complimented clone ( rrp1com ) with endogenous plasmid profiles identical to the parental strain was then chosen for further study . To construct a suicide vector for inactivation of bb0240-bb0243 , regions of DNA corresponding to 1 . 9 kb upstream of bb0240 and 1 . 9 kb downstream of bb0240 ( including part of bb0240 ) were PCR amplified from B31-A3 genomic DNA . The resulting DNA fragments were then cloned upstream and downstream of a gentamicin-resistant marker ( aacC ) within the pCR-XL-TOPO cloning vector , resulting in suicide vector pMH85R ( Fig . 6A ) . The construct was confirmed by sequencing . The plasmid DNA was transformed into B . burgdorferi B31 strain 5A4NP1 , resulting in a mutant with a disrupted bb0240 and its promoter region by an aacC marker . Since bb0240-bb0243 constitute an operon , the loss of bb0240 , bb0241 , bb0242 , and bb0243 expression was confirmed by RT-PCR analysis . One of the bb0240-bb0243 mutant clones ( designated as the glp mutant ) that had all the endogenous plasmids ( identical to the parental strain 5A4NP1 ) was chosen for further study . However , this clone subsequently lost lp28-4 during storage , which may contribute to the reduced infectivity in mice with tick infestation ( Table 5 ) . For cis-complementation of the glp mutant , the fragment containing the aacC marker and the disrupted bb0240 in pMH85R was replaced with an aadA marker linked to a wild-type copy of bb0240 , to generate the suicide vector pMH89 ( Fig . 6A ) . pMH89 DNA was then transformed into the glp mutant . Restoration of bb0240-bb0243 expression in the streptomycin/kanamycin-resistant transformants were confirmed by RT-PCR analysis . A positive clone ( designated as glpcom ) with a plasmid profile identical to the parental strain was selected for further study . A flaB promoter and bb0240 fusion fragment was constructed using a two-step PCR method . First , a flaB promoter fragment was PCR amplified from B31 genomic DNA with primers 240P7Aat2 and 240P8 ( Text S1 ) . Second , a promoter-less bb0240 fragment was PCR amplified with primers 240P3B and 240P4 . These two overlapping fragments were mixed together and subjected to RCR reaction with 5 cycles . The mixture was then served as template for PCR amplification of the fused flaBp-bb0240 fragment with primers 240P7Aat2 and 240P4 . The flaBp-bb0240 fragment was cloned into a cloning vector , pSCB-kan/amp , to generate plasmid pMH86 . A 1 . 6 kbp fragment upstream of bb0240 ( starting from 205 bp upstream of the bb0240 ORF ) was PCR amplified with primers 240P9Sal1 and 240P10Aat2 . This fragment was then cloned into pMH86 upstream of the flaB promoter to generate plasmid pMH87 . Lastly , a gentamicin-resistant marker , aacC , was inserted into pMH8 upstream of the flaB promoter to generate the suicide vector pMH88R ( Fig . 8A ) . pMH88R DNA was transformed into the rrp1 mutant , and gentamicin/streptomycin/kanamycin-resistant clones were selected and subjected to PCR analysis to confirm the replacement of the native bb0240 with flaB-bb0240 in the rrp1 mutant . Constitutive expression of bb0240-bb0243 in these clones was also determined by quantitative RT-PCR analysis ( Fig . 8B ) . Plasmid profiles were then performed , and a clone having a plasmid profile identical to the parental strain was selected for further study . This strain is designated as rrp1/flpB-glp . Four-week-old C3H/HeN mice ( Harlan , Indianapolis , IN ) were subcutaneously inoculated with 1×105 spirochetes . Ear punch biopsies were collected 14 days after inoculation , and mice were sacrificed by CO2 asphyxiation at 21 days post-inoculation . To culture B . burgdorferi , ear punch tissue samples were transferred to 2 ml of the BSK-II medium ( Sigma-Aldrich , St . Louis , MO ) containing an antibiotic mixture of fosfomycin ( 2 mg/ml ) , rifampin ( 5 mg/ml ) , and amphotericin B ( 250 µg/ml ) ( Sigma-Aldrich ) . All cultures were maintained at 34°C and examined for the presence of spirochetes every 5 to 7 days by dark-field microscopy beginning 5 days after inoculation . A single growth-positive culture was used as the criterion for infection of each mouse . The colony of Ixodes scapularis originated from females obtained from Bridgeport , Connecticut , and was maintained in the Tick-Borne Disease Activity Laboratory at the Centers for Disease Control and Prevention , Ft . Collins , Colorado . The tick-mouse experiments were conducted in the Vector-Borne Diseases Laboratory at Indiana University School of Medicine , Indianapolis , IN . Unfed , larvae were fed on groups of mice ( C3H/HeN , three mice/group , 100–150 larvae/mouse ) that were needle-infected with either 5A4NP1 or various mutant spirochetes . Ticks were allowed to feed to repletion ( 3–5 days ) and then collected within 24 hrs . A portion of fed larvae were subjected to IFA or qPCR analysis ( see below ) . The remaining fed larvae were maintained in the tick incubator and allowed to molt to the nymphal stage ( about 5 weeks ) . One month after molting , unfed nymphs were then allowed to feed on naïve mice ( 10 ticks per mouse ) . Fully engorged nymphal ticks were collected within 24 hrs of repletion and subjected to IFA or qPCR analyses . Two weeks after tick feeding , mouse tissues were collected and tested for infection by cultivation for positive growth of spirochetes in BSK-H medium , as described above . To generating artificially infected ticks with B . burgdorferi , a previously described microinjection method was used [26] , [34] , [35] . Briefly , 0 . 1 µl of B . burgdorferi culture with a concentration of 108 spirochetes per ml was injected into the rectal aperture of unfed nymphal ticks by using a femtojet microinjector system ( Eppendorf AG ) . After microinjection , ticks were placed on naïve C3H/HeN mice ( 10 ticks per mouse ) , allowed to feed to repletion ( 4–5 days ) , and then collected for IFA or qPCR analysis . Spirochetes were harvested by centrifugation at 7 , 000×g and washed three times with PBS ( pH 7 . 4 ) at 4°C . Pellets were resuspended in SDS buffer containing 50 mM Tris-HCl ( pH 8 . 0 ) , 0 . 3% sodium dodecyl sulfate ( SDS ) and 10 mM dithiothreitol ( DTT ) . Total protein lysates ( 5×107 cells per lane ) were separated by 12 . 5% SDS-polyacrylamide gel electrophoresis ( PAGE ) and transferred to nitrocellulose membranes ( GE-Healthcare , Milwaukee , WI ) . Protein bands were detected using a 1∶20 dilution of monoclonal antibody against Rrp1 or FlaB , and a 1∶1000 anti-mouse IgG-peroxidase-conjugate secondary antibody ( Jackson ImmunoResearch Laboratories , West Grove , PA ) , followed by development with 4-chloro-1-naphthol as the substrate . Monoclonal antibody against FlaB , 8H3-33 , has been described previously [92] , [93] . Anti-Rrp1 monoclonal antibody was generated by immunizing BALB/c mice with the full-length fusion protein according to previously published protocols [92] . RNA samples were extracted from B . burgdorferi cultures using the RNeasy mini kit ( Qiagen , Valencia , CA ) according to the manufacturer's protocols . Three independent culture samples were used for each strain . Digestion of contaminating genomic DNA in the RNA samples was performed using RNase-free DNase I ( Promega ) , and removal of DNA was confirmed by PCR amplification for the B . burgdorferi flaB gene . The cDNA was synthesized using the SuperScript III reverse transcriptase with random primers ( Invitrogen , Carlsbad , CA ) . To quantify the transcript levels of interested genes , an absolute quantitation method was used by creating a standard curve in qPCR assay by following the manufacturer's protocol ( Strategene , La Jolla , CA ) . Briefly , a cloning vector containing the flaB gene serves as standard template . A series of ten-fold dilution ( 100 to 107 copies/µl ) of the standard template was prepared and qPCR was performed to generate a standard curve by plotting the initial template quantity against the Ct values for the standards . The quantity of the targeted genes and flaB in cDNA samples were calculated by comparing their Ct values of the Standard Curve plot . Both standards and samples were performed in triplicate on an ABI 7000 Sequence Detection System using GREEN PCR Master Mix ( ABI , Pleasanton , CA ) . Levels of target gene transcript were reported as per 1000 copies of flaB transcripts . IFA was performed as described previously [26] . Briefly , the entire contents of a fed tick were smeared and fixed on a silylated microscope slide ( CEL Associates , Pearland , TX ) . The slides were incubated with BacTrace fluorescein isothiocyanate-conjugated goat anti-B . burgdorferi antibody ( Kirkegaard and Perry Laboratories Gaithersburg , MD ) at 37°C . Samples were observed using an Olympus BX50 fluorescence microscope . Ten ticks from each group were examined by IFA . DNA was isolated from engorged larvae ( pools of 3 larvae per sample ) , and replete nymphs ( one nymph per sample ) using the DNeasy Blood & Tissue Kit B ( QIAGEN , CA ) according to the manufacturer's instructions . Spirochete burdens within infected ticks were assessed by qPCR with primer pairs of qflaB-F/R for the B . burgdorferi flaB gene and qTactin-F/R for the tick actin gene ( Text S1 ) . Calculations of relative DNA copy number ( represented by flaB ) were normalized with the copy numbers of the tick actin gene . Wild-type , the rrp1 mutant and rrp1com strains were cultivated in BSK-II at 35°C and harvested at the mid-logarithmic growth . RNA was extracted from three biological replicates using Trizol reagent ( Invitrogen , Carlsbad , CA ) according to the manufacturer's protocol . Digestion of contaminating genomic DNA in the RNA samples was performed using RNase-free DNase I ( GenHunter Technology , Nashville , TN ) , and removal of DNA was confirmed by PCR amplification using primers specific for the B . burgdorferi flaB gene . RNA quality was determined using the Agilent Bioanalyzer 2100 ( Agilent Technologies , Santa Clara , CA ) . 70-mer oligonucleotides arrays of B . burgdorferi were prepared as previously reported [26] , [29] , [94] . cDNA synthesis , sample labeling , hybridization , and data analysis were also described previously [26] . A cutoff value of a 3-fold change was used for selecting candidate genes . Statistical analyses were performed using the one and two-sample significance test ( p<0 . 05 ) in the Acuity program . The array data has been deposited at http://www . ncbi . nlm . nih . gov/geo/ ( accession number GSE26968 ) . To determine the statistical significance of differences observed in qRT-PCR , qPCR , and growth curves , values were compared using an unpaired t test . The P values are indicated in each figure .
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The Lyme disease pathogen Borrelia burgdorferi has two sets of two-component systems , Hk1-Rrp1 and Hk2-Rrp2 . The Hk2-Rrp2 signaling system has been shown to modulate differential expression of numerous surface lipoprotein genes and to play an essential role in spirochete transformation from a tick colonizer to a mammalian host-adapted state . In this study , we show that Rrp1 , the only diguanylate cyclase in B . burgdorferi , is not required for mammalian infection but is essential for spirochete survival in the tick vector . We identify over 39 genes whose expression is influenced by this c-di-GMP signaling system . We further demonstrate that one set of the Rrp1-dependent genes , the glp operon for glycerol transport and metabolism , plays an important role in the spirochete adaptation to tick environment and partially accounts for the essentiality of c-di-GMP for B . burgdorferi survival in ticks .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"infectious",
"diseases",
"biology",
"microbiology"
] |
2011
|
Cyclic di-GMP is Essential for the Survival of the Lyme Disease Spirochete in Ticks
|
Retinal prosthesis technologies require that the visual system downstream of the retinal circuitry be capable of transmitting and elaborating visual signals . We studied the capability of plastic remodeling in late blind subjects implanted with the Argus II Retinal Prosthesis with psychophysics and functional MRI ( fMRI ) . After surgery , six out of seven retinitis pigmentosa ( RP ) blind subjects were able to detect high-contrast stimuli using the prosthetic implant . However , direction discrimination to contrast modulated stimuli remained at chance level in all of them . No subject showed any improvement of contrast sensitivity in either eye when not using the Argus II . Before the implant , the Blood Oxygenation Level Dependent ( BOLD ) activity in V1 and the lateral geniculate nucleus ( LGN ) was very weak or absent . Surprisingly , after prolonged use of Argus II , BOLD responses to visual input were enhanced . This is , to our knowledge , the first study tracking the neural changes of visual areas in patients after retinal implant , revealing a capacity to respond to restored visual input even after years of deprivation .
A wide range of approaches towards sight restoration are currently being developed , opening new possibilities for blind people with retinal pathologies . These possibilities range from optogenetics techniques [1] through gene therapy [2] to cortical and retinal prosthesis [3 , 4] . The first retinal prosthesis to enter clinical trials was a 5000-electrode microphotodiode chip ( Artificial Silicon Retina [ASR] , subretinal approach; Optobionics , Glen Ellyn , Ill , 2000 , see [5–7] ) , and it produced successful results . At present , two devices have been approved as commercial products with clinical trials: the subretinal visual implants Alpha IMS ( Retina Implant AG , Reutlingen , Germany , see [8 , 9] ) , which induce excitation of the internal plexiform layer , and the Argus II implants ( Second Sight , Sylmar , CA , see [10–12] ) , which induce excitation of the ganglion cell axons with an epiretinal implant , requiring simpler and less invasive surgery . Most of the retinal pathologies develop late in life , and little is known about the capacity of the adult visual system to process restored visual input after many years of deprivation . Fine et al . [13] described the patient MM , who had his vision restored 40 years after becoming blind at the age of three , when the critical period of vision in human is still open [14] . The study shows that only very limited plasticity was preserved , especially in V1 , and that was strictly dependent on the short visual experience during childhood . Cross-modal plasticity [15–18] , known to occur also in late blind subjects [19] , may raise an additional obstacle to reactivate cortical responses to the restored input [20 , 21] . The success of a cochlear implant in restoring auditory function correlates well with the level of inactivity of acoustic primary cortices , as assessed by positron emission tomography study [22] . Similarly , the degree of vision loss in retinitis pigmentosa ( RP ) patients correlates with primary visual cortical responses to tactile stimuli [23 , 24] . Interestingly , in one case of restored vision ( Boston Keratoprostesis , see [25] ) , responses to motion stimuli were enhanced in extra-striate occipital areas seven months after visual restoration , and this was accompanied by a decreased recruitment of acoustic processing [26] . All these data suggest that , after the original input is restored , the brain needs time to promote a response to the original sensation . This idea is consistent with the results described by Cunningham et al . [23] on two subjects with Argus II Retinal Prosthesis: the first subject ( who had the implant only for six weeks before the scanning ) showed extensive tactile-evoked responses in V1 , whereas in the second subject ( who had the implant for 15 weeks ) , it was largely decreased . After extensive training , patients with Argus II implants learn to perform a few easy behavioral tasks , such as moving independently in space , locating a large bright square on a screen , and reading large , 100%-contrast characters [10 , 27–31] . Interestingly , after this learning , the Goldmann visual field perimetry of the operated eye improved in all subjects tested , also well outside the retinotopic region covered by the implant [27] . In one patient , there was also a substantial improvement of the unoperated eye [32] . These findings are in line with the earlier evidence obtained with the subretinal implant ASR [5–7] . These patients were tested for many years after the operation and showed a great improvement of visual acuity and also of the visual field perimetry , assessed by computerized methods , compared with the unoperated eye . Interestingly , the visual field also improved in regions that were not directly stimulated by the implant . Two effects may mediate the improved visibility revealed by these studies: the learning of the artificial visual signals may reopen central visual plasticity , and this need not be retinotopically specific , or release of a peripheral trophic factor may be induced by the injection of current by the implant when in use , and this may diffuse at the not-activated site of the retina [33] . Indeed , Sabel et al . [34] demonstrated an improvement in static perimetry and in visual acuity associated with alpha-band changes in the electroencephalogram ( EEG ) after only 40 min of noninvasive alternating current stimulation ( ACS ) delivered transorbitally . At present , the origin of the increased visibility after Argus II , ASR , or Alpha IMS implants is not clear . In particular , it is not clear whether it is mediated by local retinal mechanisms or by releasing vetoed mechanisms at a central level , such as the thalamus or cortex . In this experiment , we aim to study whether these changes can be traced by measuring Blood Oxygenation Level Dependent ( BOLD ) visual cortical responses during the course of training with Argus II Retinal Prosthesis . The results show that the thalamus and the cortical responses are enhanced by the use of the implant , pointing to great neuronal plasticity of the adult blind brain .
The surgery was successfully performed in all RP subjects ( Fig 1 ) within similar operating time ( see Table 1 ) . No subject had adverse reactions or complications , except for S7 , who reported a choroidal detachment two months after the last assessment , which was resolved in two surgery revisions . Six months after surgery , S3 developed a localized retinoschisis behind the implant , which was kept under observation but not treated , because it did not affect the implant effective stimulation . The position of the implant was similar in all patients and presumably excited a similar amount of fibers ( see Fig 1 ) . Given the recruitment criteria of bare light perception ( see Methods ) , before surgery , subjects’ performance for monocular vision was nearly at chance for the motion direction discrimination of contrast modulated gratings ( Fig 2C and 2D , gray and black columns ) . After surgery , no improvement was measured in the motion direction discrimination task either for the implanted eye with the Argus II system switched on or off ( Fig 2C , red and green columns ) or for the nonimplanted eye ( Fig 2D , blue column ) . The performance for this task was very similar for patients , eyes , and time from the surgery , as shown by the sensitivity measures reported in S1A Fig . Also , contrast sensitivity measured in a detection task in two-interval , two-alternative force choice was nearly at chance level ( Fig 2C , green dot ) and constant over time ( S1B Fig ) . Consistently , with the lack of cataract , the two phakic patients ( S3 and S7 ) had no change in contrast sensitivity before and after surgery that involved also lens removal ( see Methods ) . All subjects except S7 learned quickly to use the Argus II device , and some simple performance , like the spatial localization of a square of maximum luminance , improved over the testing period ( see S2 Fig ) when using the implant . Surprisingly , when testing the implanted eye with the Argus II system switched on ( Fig 2C , red dot ) , subjects' performance reached 90% accuracy ( t ( 6 ) = 6 . 3; p < 0 . 001 ) even in more subtle tasks , like the detection of contrast-modulated grating not associated with luminance variation . This improvement in detection performance with the Argus II system switched on correlates significantly ( rho = 0 . 95 , p < 0 . 05 ) with the time from the surgery , suggesting that the effect results from perceptual learning of the artificial incoming signals ( Fig 2E ) . We recruited four RP patients to measure the BOLD response before and after surgery in response to a sequence of flashes of lights ( 1 Hz ) . However , one of these patients did not comply with the procedure of the Argus II training and failed to follow the training routine . For this reason , his data are treated separately and are not included in the functional MRI ( fMRI ) group analysis . In normal sighted subjects , the flashes of lights massively activate all of the occipital , temporal , and parietal visual brain ( p < 0 . 005 , corresponding to False Discovery Rate [FDR] q-value of <0 . 05; Fig 3A ) . To our surprise , and despite the absence of visual evoked potential ( VEP ) responses to stronger flashed stimuli than those used in the fMRI experiment , we observed some BOLD responses before surgery in all RP subjects ( Fig 3B , average p < 0 . 01 , uncorrected ) . Although the patients never reported seeing the weak flashes ( 60 cd/m2 ) used to elicit BOLD response , spared activation was observed in the calcarine sulcus , the lateral occipital sulcus , and the mediotemporal sulcus . In all four patients , we performed the fMRI scan again after surgery using the same stimuli with the Argus II system switched off . The activation increased in V1 in the group of three patients who complied with the training . Also , in the LGN , the responses became statistically significant ( Fig 3C for the right hemisphere [RH] ipsilateral to the implant , p < 0 . 01 , uncorrected ) . To quantify the increase in activation , we measured the average beta values in the anatomically defined regions of interest ( ROIs ) positioned along the calcarine sulcus and LGN in all four patients . This technique allows detection of changes that may occur at different cortical positions across subjects , as it is to be expected , given the great variability of the perimetry between subjects . The beta values at the level of the LGN ( Fig 4A and 4B ) , both contralateral and ipsilateral to the implant , increased in amplitude after the surgery in the three subjects tested ( S4 , S5 , and S6 ) , reaching statistical significance ( p < 0 . 005 ) . A similar increase was measured in the V1 region ( Fig 4C and 4D ) , selected anatomically to encompass the first 20 degrees of visual space representation . Interestingly , a significant response improvement in the LGN and V1 was not observed in subject S7 , despite the removal of the lens during the surgery that should have clarity of the ocular media . This subject did not follow the training procedure or use the Argus II daily; he showed no improvement in detection with the Argus II system switched on ( triangle in Fig 2E ) . The result of this subject is clearly at odds with those of the other subjects . The repeated measure ANOVA performed on the data of the three subjects who used the implant shows a significant increase in BOLD activity after surgery ( main effect of time , F ( 1 , 2 ) = 26 . 8 , p < 0 . 05; estimated marginal means: before the surgery = 0 . 18 ± 0 . 072 , after the surgery = 0 . 516 ± 0 . 070 ) . We did not observe any significant main effect of the ROIs ( F ( 3 , 6 ) = 1 . 55 , p = 0 . 29 ) , indicating that the BOLD increase did not differ between ROIs , nor was it a significant effect of interaction between ROIs and time ( F ( 3 , 6 ) = 0 . 64 , p = 0 . 60 ) . The availability of an RP subject who had a successful surgery but did not use the device provides the rare possibility to disentangle the physical effects of implant and wireless operation from the use of the device as a visual aid in eliciting plasticity . To assess whether the increase in BOLD activity in the LGN and V1 might have a functional relevance , we correlated the BOLD increase with the performance scored in the detection task when testing the implanted eye with the Argus II system switched on ( Fig 4E and 4F ) . The data show that the subjects who performed the detection task with greater sensitivity ( with Argus II on ) also have higher BOLD responses in the LGN and V1 . To assess the significance of this effect , we performed a two-way repeated measure ANOVA on BOLD changes , with the factors visual sensitivity and ROIs ( hemisphere being a within factor ) , and obtained a significant effect for the visual sensitivity factor ( F ( 3 . 1 ) = 45 , p = 0 . 005 ) . The effect of ROIs and the interaction between ROIs and sensitivity were not significant . We also performed a direct BOLD contrast between the activation before ( blue ) and after ( red ) surgery in the three individual subjects ( Fig 5 ) , mapping the results on the average brain where average retinotopy acquired by our laboratory has been projected ( see Methods ) . After surgery , the activity improved in the primary visual cortex and in the mediotemporal and occipitotemporal sulcus . The improvement is statistically significant ( p < 0 . 05 ) but did not survive FDR correction . However , the anatomical localization of the increased activity voxels is not random , but clustered within the primary and secondary visual cortex , including V1 , V2 , and V3 in the peripheral visual field representation . This corroborates the ROI ANOVA result , suggesting an increase in BOLD activity in these areas after surgery . In addition , given that the retinotopic position of the improvement will vary across subjects , a group analysis is not the optimal technique to detect the possible BOLD changes , and the ROI analysis is more appropriate . Similarly , many higher associative visual areas ( like lateral occipital [LO] , V3AB , V7 , and MT+ ) showed higher signal before the surgery , suggesting a suppression of these area by the new visual activation ( see Discussion ) . To rule out the possibility that these results were due to magnetic field interference between the magnetic gradients and the implant ( which has local coils and wireless hardware ) , we performed additional scans , decreasing the number of slices for the subject S4 ( S3 Fig ) and changing the slice orientation or the visual display for the subject S6 ( S4 Fig ) . No difference was observed in the left and right LGN ( t ( 116 ) = 0 . 068 , p = 0 . 9; t ( 116 ) = 0 . 20 , p = 0 . 8 ) , nor in the left and right calcarine sulcus ( t ( 116 ) = 0 . 51 , p = 0 . 6; t ( 116 ) = 0 . 49 , p = 0 . 6 ) . Similarly , no differences in beta values were observed in S6 ( S4 Fig ) , nor in left and right LGN ( t ( 116 ) = -0 . 1 , p = 0 . 8; t ( 116 ) = -0 . 6 , p = 0 . 5 ) , nor in the left and right calcarine sulcus ( t ( 116 ) = 1 . 04 , p = 0 . 3; t ( 116 ) = 0 . 6 , p = 0 . 5 ) .
Here , we have demonstrated that the adult visual brain retains a degree of plasticity and is able to reorganize its response to process new and abnormal incoming inputs after many years of deprivation in adulthood . The boost in BOLD response takes a long time and intensive training to appear , being stronger in those subjects who used the prosthetic device more intensely and for a longer time . Our data show that the training with Argus II transfers to more subtle improvements than previously observed [30] , including the detection of sinusoidal gratings that the patients never saw before and , importantly , that are not associated with overall mean luminance changes . We observed no improvement for the nonimplanted eye and no improvement for motion direction discrimination , even with the Argus II system on . It has been suggested [35] that the perceptual experience produced by the implant might be distorted due to the axonal stimulation of the ganglion cell axons that travel under the implant , introducing motion smear . This may help to explain the lack of improvement for horizontal motion discrimination observed here . Recent studies have revealed that the adult V1 cortex retains plasticity with deprivation [36 , 37] , even after a brief period of hours of monocular alteration [38] . The BOLD boost that we observed here reinforces this evidence and shows that the plasticity is retained even after years of deprivation . Other sensory modalities usually recruit the occipital cortex after periods of deprivation [15] . This can take place even in normal sighted subjects with training [39] and also in RP patients with loss of vision at adult age [19] . Interestingly , in the RP patients , as in the congenitally blind , the recruitment of the primary cortex is particularly strong with language and memory areas , as demonstrated by the increase in functional connectivity between the occipital cortex , frontal cortex [40 , 41] , and Broca area [41] , rather than with other sensory cortex . The need for extensive and prolonged training , suggested by our data , is probably necessary to allow the remodeling of these spurious multisensory activations . A previous study with cochlear implant [22] on prelingually deaf children demonstrated that the degree of cross-modal plasticity ( marked by glucose hypometabolism ) predicts the auditory temporal cortex’s capability to respond to auditory stimuli of the cochlear implant and the overall success of the functional use of the implant . Prolonged periods of deafness induced stronger cross-modal reorganization of the acoustic cortex and hampered recovery after the cochlear implant . These results are in close agreement with the present results , establishing that the functional response of the appropriate modality response is a good indicator of the use of the implant and of the plastic cortical response . All our patients had only light-dark perception , which may explain the small residual BOLD activation observed in the presurgical scan . Interestingly , none of the four patients reported perceiving the flashing stimulus , probably because of the low intensity of the flashes . However , some activity was clearly elicited , suggesting that some functional connection between retinal input and the cortex is still present in these RP patients despite the lack of subjective report and the lack of VEP responses . This also indicates that the cross-modal plasticity in V1 demonstrated by many studies [15] did not veto the cortex from responding to the flashes . Overall , our data suggest that if a patient had enough visual experience before blindness and has some residual light perception , once new vision information—even artificial and aberrant—is relayed to the brain , the primary visual cortex can reactivate to endorse a plastic cortical response . The more prolonged the exposure to the artificial vision , the stronger the response of V1 to the artificial visual input . Interestingly , two other patients implanted with Argus II [23] have been reported to show a reduction of tactile cross-sensory plasticity in V1 after use of the implant , suggesting that the first step of the plastic response of the primary visual cortex is the weakening of the cross-modal responses and , later on , the restoration of the original visual modality . These two patients did not show any response to visual stimuli , but they were tested only a few months after surgery , reinforcing the present data that a longer time is need to promote plasticity . We observed an increase of BOLD activity in the V1 , V2 , and V3 corticies , but not in MT+ , LO , nor in associative cortices such as intraparietal , superior temporal , and precuneous cortices . This suggests that higher associative areas that have stronger cross-modal plasticity [18 , 42–46] may have hampered the visual responses . The lack of a global activation of all the visual cortical circuits may also explain the lack of perception in these patients . It would be interesting to follow these patients for a longer time , implementing a more intense training regimen to verify the hypothesis that eventually these associative cortices may also respond to light , and then , perhaps , perception could also be partially rescued . The spared plasticity of V1 and low tier associative cortices was not expected , given the prevailing evidence of low plasticity to visual stimuli observed in the two subjects who regained vision at adult age [13 , 26] . However , both of these studies followed the progression of congenitally blind patients or patients who became blind during the critical period . The data on the subject MM [13] are very clear in stating that even after more than ten years of restored vision , the visual response did not change [47] . Interestingly , the only plastic reorganization was observed in area MT+ , specialized for motion processing [16 , 18] . Development of motion-selective mechanisms is nearly complete at three years , the time of the blindness onset of this patient [14 , 48] . Even more surprisingly , we observed plastic changes even in the thalamus . LGN plasticity , even during development , is usually believed to be very limited [49] . It is surprising that the effect that we observed here for the LGN is nearly as strong as for V1 . One possibility it is that it is mediated by an attentional modulation towards a stimulus known to modulate activity in LGN [50] . However , this is unlikely , because no subject reported seeing any stimulus during the scan despite the BOLD activity . The lack of perception was also confirmed by the total absence of any electroretinogram ( ERG ) or VEP responses . Another possibility it is that the plastic changes originate at the retinal level , mediated by some local and unknown trophic effect . The first study to use a subretinal implant observed a clear recovery of visual acuity and perimetry in six out of ten patients over a period of seven years from the surgery [5–7] . The recovery was not confined to the stimulated retinal position , being observed very far from it , but it was confined to the same eye . This suggests that it might be mediated by a retinal trophic effect induced by the surgery itself , by the local injection of current , or even by the overall improvement in eye health after surgery . Ciavatta et al . [33] observed a significant elevation in fibroblast growth factor-2 ( Fgf2 ) expression in rats following implantation of an active micro-photodiode array compared with rats with a minimally active array or sham surgery . These authors suggested that subretinal electrical stimulation by the active array induced selective Fgf2 expression , producing a neuroprotective effect on the retina . Also , an expansion of the visual field perimetry [27] has been observed in some Argus II patients , consistent with the diffusion of retinal trophic factors . However , in one patient , there was also a strong improvement of the temporal retina of the fellow eye as well of the operated eye . The improvement in both eyes declined as the patient used the device less . This latter result is hard to explain in terms of release of a retinal trophic factor and also in terms of electrical stimulation , given that the electrical field produced by the wireless coils attached to the Argus II is very low at the distance of the fellow eye . Interestingly , the improvement occurred for homologue regions of the retina , which might suggest that it is the neuronal activity at higher levels in the visual pathways that might exert a trophic function . For example , the influence of the operated eye on the fellow eye may be mediated by an anterograde effect from a central station such as the LGN , where the projection of the fiber from the two eyes is closely interlayered , and some cross-talk of neuronal discharge may take place . The fact that patient S7 of the present study did not show any improvement in BOLD response does not support the view that plasticity arises from the surgery itself or by the overall improvement in eye health after surgery . This patient had the same successful surgery , the same visibility thresholds , and similar years of deprivation to the other patients . However , he was the only one who did not use the Argus II system . The lack of visual responses in this patient provides a strong control against possible artifacts between the device and the magnetic field , which might have influenced the increase of the BOLD response after surgery . Although at this stage it is very difficult to disentangle the origin of the observed recovery after surgery , our data indicate that the neuroplastic response is present at the early stage of processing . Whatever the mechanisms mediating the plasticity , it is worthwhile to stress a few important implications of our results . They show that the adult brain is able to reorganize itself to adapt to the new incoming visual stimulation even after years of blindness; this takes place only after extensive training and well before proper perception is achieved; the BOLD signal is more sensitive than a perceptual threshold or EEG measure to monitor these changes . The reason for the superiority of BOLD in monitoring these changes might be that signals from multiple sources , both bottom-up and top-down , are integrated within the BOLD response and over long periods . In addition , this also indicates that some activity related to the visual stimulus reaches the cortex in RP patients despite complete blindness . If so , this suggests that , in RP patients , the input signal may be actively suppressed at the cortical level , probably because it is too aberrant and temporally noisy to mediate a perceptual response .
All subjects signed the informed consent for prosthesis implant after being informed about the possible outcomes of the surgery . The protocol was approved by the Ethics Committee of Azienda Ospedaliera Pisana ( IRB IRB00010229 ) . The trial was and continues to be conducted in accordance with the Declaration of Helsinki and the national regulations for medical device clinical trials in Italy . The clinical trial is posted in a publicly accessible registry approved by the WHO or ICMJE on www . clinicaltrials . gov ( trial registration number NCT01490827 ) and adheres to the TREND guidelines for nonrandomized trials ( https://clinicaltrials . gov/ct2/show/NCT01490827 ? term=Argus&rank=5 ) . The MRI and the psychophysical protocol of this study were approved by the Ethics Committee of Fondazione Stella Maris ( protocol N 11/2012 , IRB00003240 ) , which has been superseded by the Regional Pediatric Ethical Board ( IRB 00009689 ) . All subjects signed informed consent to participate . This study was conducted on a group of seven blind patients ( males = 4 , females = 3 , age = 60 ± 6 years ) affected with RP with bare Light Perception , before and after ( 17 ± 7 month interval ) implantation with Argus II Retinal Prosthesis . All subjects were required to have some visual memory , no electro-retinographic response , and residual light perception , although bare . Exclusion criteria included the presence of other ocular disease that might interfere with device function or inhibit postoperative device visualization; history of cystic macular edema; pregnancy or desire to become pregnant; deafness; and uncontrolled systemic disease . The initial screening visit included a complete eye examination , retinal fundus photography , fluorescein angiography , optical coherence tomography ( OCT ) , Goldman full-field visual field testing , and ultrasound ( A-scan ) axial length measurement . Only patients with axial lengths between 20 . 5 and 26 . 0 mm were included . All patients had immeasurable monocular logMAR ( logarithm of the minimum angle of resolution ) visual acuity ( worse than 2 . 9 ) before surgery . All preoperative tests were performed with both eyes open and , in pseudophakic patients , several years after cataract surgeries . In order to avoid a further intervention of phacoemulsification a few months later , phakic patients underwent clear cornea phacoemulsification and were left aphakic , given that pars plana vitrectomy is a risk factor for cataract progression [51] . The two phakic patients had a normal lens with only an initial sclerosis that did not impair light transmission . No other eye diseases were present . Subject details are listed in Table 1 . The surgery and the rehabilitation were conducted as previously described [27] . Only one eye was implanted ( Fig 1 ) . During the use of the Argus II Retinal Prosthesis system , patients need to wear glasses equipped with a central mounted camera . This camera is connected to a video processing unit carried on the patient's body . The video processing unit converts images captured from the camera into electronic signals and sends it to a transmitter coil attached to the glasses . The transmitter coil sends the information wirelessly to a receiver coil that is sutured onto the sclera with a scleral band . A transcleral cable conveys the signals from the receiver coil to a 6 x 10 grid of electrodes array held on the retinal surface ( covering about 11 x 17 visual angle degrees ) that is abruptly refreshed at a low frequency ( below 20 Hz ) . When activated , each gold electrode emits pulses , which are thought to directly stimulate the retinal ganglion cells or their axons ( but see [35] ) . The patients usually learn to use the device after intensive training consisting in perceiving and localizing crude high-contrast forms and lights on a screen , and following a post-implant visual rehabilitation with the support of low vision therapists . ERGs were recorded differentially with gold-coated Mylar electrodes positioned in the lower fornix of each eye . The other eye was closed and served as a reference ( interocular PERG: [52] ) . We also recorded VEPs , with Ag-AgCl electrodes placed 2 cm above the inion ( active ) and at the vertex . The common ground for all recordings was on the forehead . PERG and VEP signals were amplified ( PERG 100 , 000-fold , VEP 50 , 000-fold ) , band-pass filtered between 1–100 Hz ( 6 dB/oct ) and averaged on-line by computer over at least 300 periods . The visual stimulus was a strong flash ( 500 mJ ) positioned at about 20 cm distance from the subject eye and delivered at a frequency of 0 . 5 Hz . We never measured any ERG or VEP reliable responses in any of the RP subjects , consistent with the diagnosis of total blind or bare light perception , both before and after surgery . The vision of the implanted and the not implanted eye was tested in psychophysical experiments in separate sessions both before and after surgery . When testing with the Argus II system switched on , the subjects' eyes were patched so that only the activity delivered by the implant could be used . Subjects sat in front of a large screen ( 86 x 55 degree , luminance 180 cd/m2 ) at a comfortable distance of 57 cm . The stimuli were contrast-modulated gratings , perfectly linearized by gamma correcting the monitor after collecting the photometric values at all luminance levels ( from 5 to 350 cd/m2 ) to avoid abrupt increment of luminance that could aid detection in the blind patients given the residual light perception . Visual functions were evaluated before and after the implant with a motion direction discrimination task and a detection task , with a single or double presentation two-alternative forced choice procedure . The stimuli were optimized to achieve the best performance for the blind patients or for the Argus II device . In the "Motion Direction Discrimination Task" ( Fig 2A ) , subjects were asked to indicate the direction of a drifting grating . A high-contrast ( 60% Michelson contrast ) grating , 40 degrees wide with spatial frequency of 0 . 0625 cpd , was abruptly presented for 1 or 0 . 5 s when testing the Argus II system switched on or off , respectively . The grating drifted rightward or leftward at 1 Hz for the Argus II system on or 4 Hz for the Argus II system off . The long presentation time and slow temporal frequency were chosen to be certain to be inside the sampling frequency and the transient delivery of the device . In the "Detection Task" ( Fig 2B ) the subjects were asked to specify in which of two intervals ( marked by tones ) was the stationary , low spatial frequency grating presented ( for 1 s ) . The intervals were marked acoustically by tones and separated by more than 5 s . No feedback was given to the subjects . Each subject performed three sessions of ten trials each per each eye and condition for the motion direction discrimination task and two sessions of ten trials each for the detection task . We allowed all the necessary time for the subject to reach a decision . Usually they took a very long time , in the order of seconds . This meant that the separation between trials was variable , but always longer than 1 s . For each subject , the average accuracy was calculated and transformed to detectability , d-prime . A one-sample t test against 0 . 5 was performed in each subject to assess the visual performance in the detection task after the surgery . We also performed the Square Localization test , used previously with Argus II patients [10 , 27] . On a touch screen display , a white square 7 . 3 cm wide was displayed on a black background ( 100% contrast ) at random positions . The patient , at 30 . 5 cm from the screen with free head movements , was asked to localize the center of the square on the screen with a reach-and-touch movement . The task was repeated 40 times , and the average difference between the square center and the patient’s touch , in centimeters , was automatically computed by the testing software . Four subjects ( S4 , S5 , S6 , S7 ) also underwent MRI examination in a 1 . 5 T GE scanner before and after surgery . The MRI compatibility and issues of the Argus II system retinal prosthesis have been previously tested [23 , 53] , and the system has been labeled as an MR conditional device ( http://www . mrisafety . com/SafetyInfov . asp ? SafetyInfoID=313 ) . Following the MRI recommended procedures , the scanning sessions lasted only 15 min after the surgery . The Argus II system was switched off at least 2 h before the scanning to allow discharge of the wireless coil current and remained off during the exam . Subjects were instructed to notify the MRI operator if pain or unusual sensation of heat was occurring during the scanning , giving particular attention to the orbital region . No subjects reported any uncomfortable feelings of this kind during the examination . The subjects’ eyes were monitored through an infrared camera to assess discomfort symptoms like excessive blinking or squeezing . MRI data were acquired using a GE 1 . 5 THD Neuro-optimized System ( General Electric Medical Systems ) fitted with 40 mT/m high-speed gradients . Each session included a whole brain set of anatomical images with T1-weighted contrast . T1-weighted scans were acquired using a FSPGR sequence with TR = 8 . 4 ms , TE = 3 . 9 ms , flip angle = 8° , and 1 mm3 isotropic resolution . Echo Planar Imaging Gradient Echo ( EPI-GRE ) sequences were used for the fMRI data acquisition ( TR = 3 , 000 ms , TE = 35 ms FOV = 192 x 192 mm , flip angle = 90° , matrix size of 64 x 64 , and slice thickness = 3 mm ) . Head movement was minimized by padding and tape . During fMRI scanning , 15 s of full-field ( 20° x 30° ) flashing stimuli ( maximum range 0 . 1 cd/m2 to 60 cd/m2 at 1 Hz: 500 ms on and 500 ms off ) alternated with 15 s rest periods of dark six times . Stimuli were displayed through liquid crystal goggles equipped with infrared eye-movement camera ( VisuaStim XGA Resonance Technology at a resolution of 800 × 600 voxels , subtending 30° × 22 . 5° at an apparent distance of 1 . 5 m , with mean luminance of 30 cd/m2 ) . Subjects were instructed to keep their eyes open , which were monitored and recorded in all subjects . After the MRI scan , we questioned the subjects about their perception during the scan . All of them reported not perceiving the flashes . Subjects performed one anatomical scan and two functional scans before and after the surgery . As reported on the MRI-safety website , the implant may create artifacts and MR signal drop . In line with Cunningham et al . [23] , we observed local artifacts of the implant during the EPI sequences that were strictly limited to the patient's implanted eye and did not extend to the cortical or subcortical regions . To quantify the local artifact , we calculated the signal-to-noise ratio ( SNR ) by dividing the mean pixel intensity value in an ROI by the standard deviation of an external ROI of the same extent ( in the air region outside the ghosting artifacts ) . We measured the SNR before ( SNRpre ) and after ( SNRpost ) surgery in the three ROIs: one centered on the eye , the second centered on the LGN ipsilateral to the implant , and the last on the ipsilateral primary visual cortex . The SNR of the ROI centered on the implanted eye was significantly different ( SNRpre = 17 ± 11; SNRpost = 1 . 5 ± 0 . 3 ) before and after surgery , whereas the one centered on the LGN ( SNRpre = 31 ± 11; SNRpost = 32 ± 11 ) and the one centered in V1 ( SNRpre = 100 ± 8; SNRpost = 100 ± 8 ) were not , confirming that the artifact did not affect the signal at these levels . To rule out further potential confounds due to interaction between the implant and the magnetic field gradients , we ran control scans in two subjects , varying the scanning condition and parameters . In one subject ( S4 ) , fMRI was performed by scanning with a different number of slices covering the brain ( 13 or 30 slices ) while keeping the TR constant . If the BOLD signal were driven by the interaction between implant and the changing gradients , we would have expected a different activation between these two conditions . In another subject ( S6 ) , fMRI was performed by scanning with axial or oblique slice orientation , hence including or not including the implant in the field of view ( FoV ) . Additionally , to also eliminate possible artifact induced by the goggles , we also delivered the flash via an optic fiber bundle , previously used by [54] . Imaging data were analyzed with Brain Voyager Qx ( version 2 . 8 Copyright 2001–2015 Rainer Goebel ) . Anatomical images were spatially normalized using the Talairach and Tournox atlas [55] to obtain standardized coordinates for the ROI . Functional data were preprocessed to compensate for systematic slice-dependent differences in acquisition time ( using cubic spline ) , three-dimensional motion correction ( using Trilinear/Sync interpolation realigning data to the first volume of the first scan ) , and temporal filtered ( high-pass filter GLM with Fourier basis set , including linear trend , with two cycles ) . No spatial smoothing was used . We analyzed the data with a multi-study , multi-subjects fixed effect GLM with one regressor corresponding to the flashing light blocks . The regressor was convolved with the canonical hemodynamic response function ( HRF ) . The BOLD modulation recorded during scans before and after the surgery was contrasted . Beta values were extracted from two anatomically defined regions of interest . The LGN was localized individually for each single subject with the help of an expert neuro-radiologist , and a 216-mm3 ROI was defined around it . The LGN was identified according to anatomical landmarks , as previously described in literature [56–59] , and direct measurements in control subjects in response to flashing lights . Anatomically , the LGN was localized on top of the apex of the lateral recess of the ambient cistern in between the optic radiation laterally and the posterior limb of the internal capsule anteromedially . On coronal view , the LGN was visible above the hippocampus . In control subjects , the anatomical localization of the LGN matched exactly with the area activated when subjects were exposed to flashing lights . The ROI on the calcarine sulcus was defined from the most occipital pole along the sulcus for about 3 cm , representing about 20 degrees of visual space [60 , 61] . To assess the significance of the ROI analysis , we performed a two-way repeated measure ANOVA , with two factors: "ROIs" of 4 levels ( LGN left hemisphere [LH] , LGN RH , V1 LH , V1 RH ) and "surgery" with 2 levels ( before and after ) . To identify the visual areas showing activation changes , we performed cortex-based alignment between the RP patients and a control-sighting subject who underwent standard retinotopic mapping . From all the subjects of our database with retinotopic identification of visual area , we chose the one that more closely aligns with the average location of the border of the visual cortical areas across subjects [54 , 62] . This procedure aligns the brains of a group using the gyral/sulcal folding pattern of the cortex and not the less precise alignment based on the anterior , posterior commissures and the six points defining the limit of the Talairach space . In order to identify the anatomical regions that were falling outside standard retinotopic boundaries , we performed the cortex-based alignment of the RP patients with the Brain Voyager atlas , providing parcellation maps .
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The majority of pathologies leading to blindness are related to diseases of the photoreceptors , such as retinitis pigmentosa ( RP ) . A wide variety of different methods are being developed in the attempt to partially restore vision in blind people , including a retinal prosthesis that uses electrical stimulation of the retina to elicit neural responses in the visual pathway . However , restoring appropriate function to the retina does not necessarily imply that the patients can see again , given that plasticity of the primary visual cortex retained by the adult brain is limited , especially after many years of blindness . Here , we followed the sight recovery process in adult patients with the retinal implant Argus II using changes in blood oxygen levels as a readout of neural activity . The recovery of vision depended on the amount of time and practice the subject experienced with the implant , implying that the reorganization process takes time to develop . Importantly , we observed that subjects who used the prosthetic implant the most were also the ones whose response along thalamic visual pathways and the primary cortex increased before and after sight restoration . Overall , these results suggest that there is residual plasticity in adult subjects and that the adult brain can learn to “see” using an artificial visual input .
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2016
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Visual BOLD Response in Late Blind Subjects with Argus II Retinal Prosthesis
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The phenotypic effect of a gene is normally described by the mean-difference between alternative genotypes . A gene may , however , also influence the phenotype by causing a difference in variance between genotypes . Here , we reanalyze a publicly available Arabidopsis thaliana dataset [1] and show that genetic variance heterogeneity appears to be as common as normal additive effects on a genomewide scale . The study also develops theory to estimate the contributions of variance differences between genotypes to the phenotypic variance , and this is used to show that individual loci can explain more than 20% of the phenotypic variance . Two well-studied systems , cellular control of molybdenum level by the ion-transporter MOT1 and flowering-time regulation by the FRI-FLC expression network , and a novel association for Leaf serration are used to illustrate the contribution of major individual loci , expression pathways , and gene-by-environment interactions to the genetic variance heterogeneity .
A central question in genetics is to understand how genetic polymorphisms in genes lead to trait variability in populations . Complex traits are determined both by genes and environmental factors . For these phenotypes , the genetic effects of allelic variability are most often described as shifts in the mean phenotype between individuals with different single- or multi-locus genotypes . These mean effects will result in both additive and non-additive genetic variance , but the main focus in most GWAS studies to date has been to detect additive effects of loci and consequently explain the contribution of individual genes to the narrow-sense heritability ( ) . Such analyses therefore miss not only the contributions of mean effects to the non-additive genetic variance , they also ignore other types of genetic effects that influence the phenotypic variance . One such rather unexplored level of genetic control , is that of the variance , i . e . how allelic variants of genes regulate the amount of phenotypic variability that individuals with a particular genotype can display . The topic of genetic variance control has been under investigation for many years in quantitative genetics , primarily motivated by its potential importance in evolutionary biology and agricultural selection programs . Both theoretical and empirical work has improved our understanding of how the genetic regulation of the environmental variance can contribute to observations of fluctuating asymmetry , canalization and genetic robustness [2] , [3] . More recent empirical work support the principal idea that genetic control over variation is an inherent feature of biological networks and genes are therefore expected to exhibit control over the environmental variance ( see e . g . [4] for a review ) . Further studies have also provided insights to how genetic variance-control contributes to e . g . capacitation [5] , [6] and maintenance of developmental homeostasis [7] . Already in the mid 1980s it was observed that it was possible to identify QTL with effects the variance , rather than the mean [8] . It is , however , only recently that the topic of mapping of variance-controlling loci contributing to e . g . environmental plasticity [7] , canalization [9] , developmental stability [10] and natural variation in stochastic noise [11] have started to receive more attention . Although these first reports illustrate the usefulness of this approach , we still know very little about how common the variance-controlling genes are in the genome and how large total contributions they make to trait variation in populations [3] , [12] . More studies are thus needed and several newly described statistical methods will facilitate detection of variance-controlling loci , and likely also GG and GE interactions [13] , in both future QTL [11] , [12] , [14] and GWAS [15] , [16] studies . In this study , we perform a variance-heterogeneity GWAS , or vGWAS for short , in a publicly available Arabidopsis thaliana dataset [1] to identify novel variance-controlling loci that illustrate the biological impact of genetic variance heterogeneity . Our study shows that clear signals from a vGWAS can be obtained using a relatively small , but well-designed , Arabidopsis thaliana population without requiring measurements of within-line variation . The study also includes an extension of the available quantitative genetics theory to estimate contributions of variance differences between genotypes to trait variation by individual loci . The vGWAS approach facilitates detection of loci that are involved in the genetic control of environmental variation ( as discussed above ) . It also allows mapping of loci where incomplete LD between the causal polymorphism and the tested marker , multiple functional alleles , gene-gene or gene-by-environment interactions leads to a heterogeneity in variance , rather than a mean difference , between the genotypes [13] , [14] .
We re-analyzed a publicly available Arabidopsis thaliana dataset [1] . The dataset contained 199 phenotyped ecotypes , for most of which 107 phenotypes were measured . The phenotypes were classified as either flowering ( ) , developmental ( ) , defense ( ) or ionomics ( ) traits . All accessions were genotyped using a 250K SNP chip , resulting in 216 , 130 SNPs that passed quality control for use in the GWAS ( http://arabidopsis . usc . edu ) . The original GWAS [1] reported signals in several annotated candidate genes across the genome and , in contrast to most results from human association studies , many common alleles were identified to be associated with the studied phenotypes , although the population stratification present in the dataset will affect the interpretation of the findings . The overlap between a-priori candidates and the detected association signals was argued to be a useful validation of the GWAS strategy in Arabidopsis thaliana . Here , we performed a vGWAS for the 83 , of the 107 , measured traits that were quantitative using a Brown-Forsythe test ( Table A1 in Text S1 ) . This test is based on an ANOVA of the absolute deviation from the median and test for population-wide between-genotype variance heterogeneity at each evaluated marker ( for more details see Methods ) and does not account for potential within genotype variance heterogeneity between repeated measurements in the same inbred line . The impact of population stratification was evaluated by comparing the distribution of the genome-wide -values observed in the vGWAS to their theoretical expectation . The inflation factor for the observed -values ( ) was calculated ( see Methods ) and found to vary for the traits ( ) . Although this differential inflation across traits might initially seem surprising , as the genomic relationship at the DNA level is identical in all analyses , the observation that the highest inflation-factors were observed for traits that are most likely to have been under selection for local adaption might explain why the analyses of those traits are most affected by population stratification . We decided to report vGWAS results for traits with high overall inflation of -values ( , ) in Text S1 only and not discuss them further in this report . For the other traits , conservative significance thresholds were obtained by using Bonferroni correction for multiple-testing and using genomic control ( GC ) to correct for genomic inflation ( , ) . SNPs with a minor allele frequency ( MAF ) less than 10% were removed . No correction for testing of multiple traits was used . This procedure filtered out many traits and signals , leaving two strongly evidenced variance-controlling loci ( Table 1 ) . The conservative strategy is not recommended in studies aiming at a comprehensive exploration of the genetic architecture of a complex trait; for example only two loci of all reported in the original GWAS analysis of this dataset [1] would have met these criteria . To compare the genome-wide distributions for the -values obtained in the vGWAS and the GWAS , we subjected the results for the Wilcoxon-based GWAS results to the same conservative significance testing strategy employed in the vGWAS . The GC- and Bonferroni corrected -values from the two analyses showed little correlation overall ( Figure 1a ) and no overlap among the genome-wide significant loci . Even at sub-GWAS levels of significance ( Figure 1a ) , there is little overlap among the loci detected in the two analyses . Using a sub genome wide significance-threshold of ( -value ) , there are approximately three times as many significant SNPs in the vGWAS as in the GWAS and only about 3 out of 1000 significant SNPs reach this level of significance in both analyses . This indicates that by using a vGWAS , one will identify a novel set of loci affecting primarily the variance heterogeneity and that neither the GWAS nor the vGWAS will identify the loci with intermediate effects on both the mean and the variance heterogeneity . The results , however , also indicate that there are also a number of loci that will not be significant in either of these analyses , but that might be significant when simultaneously considering the effects on the mean and the variance ( Figure 1a ) . Also , a large number of the loci that are significant in one analysis will also have effects on the other variance component , although not on a genome wide level . The potential importance of such earlier undiscovered effects for loci detected in the original GWAS [1] will be discussed in more detail later . To estimate the contribution of genetic variance heterogeneity between genotypes to the phenotypic variance , the following model can be used:where is the variance due to genetic effects on the mean , is the variance due to heterogeneity between genotypes and is the remaining environmental variance [3] , [17]–[20] . In the population analyzed here , where only the two homozygous genotypes exist , and consequently:The contribution of the genetic variance heterogeneity is:where and are the frequencies for the low- and high-variance alleles ( LAF and HAF ) and and are the differences in the mean/standard deviation between the two homozygous genotypes ( see the Methods and Text S1 for more information ) . This straight-forward single locus extension of available quantitative genetics theory facilitate mapping of individual variance-controlling loci and estimation of their contribution to the phenotypic variance . In Table 1 we give the estimates of and for the two most significant loci in the vGWAS . Using MOT1 as an example , , and are calculated as:so that we havewhich are the same as given in Table 1 ( ignoring small rounding errors ) . For some loci , the genetic variance heterogeneity can thus explain a considerably larger proportion of the phenotypic variance than the genetic effect on the mean . In Figure 1b we plot and for all the genome-wide evaluated loci across the 52 traits with inflation factor 1 . 5 . There is no overlap among the genome-wide significant loci and , as discussed above , there is little overlap even at sub genome-wide significance levels . Many loci thus have significant effects only on the mean ( significant and large and non-significant and small ) or the variance ( large and significant and small and non-significant ) . Figure 1a and Figure 1b , however , indicate that a number of loci make substantial contributions to the phenotypic variance if considering mean- and variance effects jointly . By mapping loci that display a variance heterogeneity between genotypes , and by also including in the decomposition of the phenotypic variance for the loci significant in the standard GWAS , it is possible to detect new loci , account for non-additive genetic variance and genetically dissect the environmental variance . In this way genetic effects that was previously part of the residual variation can be accounted for and more of the total phenotypic variance be explained ( Table 1; Figure 1b ) . Our proposed vGWAS strategy is based on the Brown-Forsythe test and we show empirically , and through simulations , that it is powerful while still controlling the false-positive rate: The power of the vGWAS is influenced by ( Figure A3 in Text S1 ) and by the low-variance allele frequency ( LAF ) . has its maximum at LAF , where and are the phenotypic standard deviations for the high- and low-variance genotypes , respectively ( see Methods and also Figure A2 in Text S1 ) . Given this , it is not surprising that the most significant variance-controlling loci in the vGWAS have high LAF ( 0 . 5 ) as well as large ( Figure 2 , Table 1 ) . The false positive rate ( FPR ) of the vGWAS is very low for any sample size and LAF , as shown by simulations ( Figure A3 and A4 in Text S1 ) , which supports the theoretical expectation of a low false positive rate for the Brown-Forsythe test in a vGWAS [16] and that GC is useful for filtering out false positive signals due to population confounding [21] . Atwellet al . [1] introduced a method for evaluating the enrichment of strong , but not necessarily genome-wide significant , signals for SNPs in candidate genes . An enrichment of such signals indicates that the analysis identifies true signals rather than random noise . Here , we extended this analysis by combining the rank-order lists from the Wilcoxon- and EMMA [22] , [23] GWAS analyses performed by Atwell et al . [1] with the results from our vGWAS . In this combined rank-order list , where for each trait the highest rank for the listed genes in the GWAS or vGWAS was included , the average rank of the candidate genes listed by Atwell et al . [1] improved considerably . For the traits with inflation factor 1 . 5 , the ranks of 31 ( 5 . 1% ) of the listed candidate genes were improved by introducing the vGWAS results and on average their rank increased by ( from to ; the complete results are available in Table 1–83 in Text S1 ) . The vGWAS signals are thus more frequent in regions of known candidate genes and the two most significant signals in our vGWAS both map to candidate genes listed by Atwell et al . [1] ( Table 1<~ ! /emph> ) . Several SNPs covering the only exon of the gene MOT1 were in the vGWAS found to be associated with the molybdenum concentration ( Figure 3 ) . MOT1 was top-ranked in the vGWAS while originally ranked 31 in the GWAS [1] ( Table 25 in Text S1 ) . The level of molybdenum in Arabidopsis is known to be regulated by the mitochondrial molybdenum transporter encoded by this gene [24] , [25] and here MOT1 explains of the phenotypic variance by its effect on the mean . The effect on the variance heterogeneity between genotypes was larger ( Table 1 ) and in total the locus explains of the phenotypic variance , i . e . 57% rather than 10% of the earlier reported broad sense heritability for this trait [25] . Our finding that MOT1 affects the variance heterogeneity in this population might initially seem surprising , as the original studies only report an effect on the mean . However , a closer inspection of the results by Baxter et al . ( Figure 1B ) [25] and Tomatsu et al . ( Figure 2C ) [24] , indicates that variance heterogeneity between genotypes was present also in earlier studies . Using the Baxter et al . [25] data ( http://www . ionomicshub . org ) , we re-estimated the differences in the mean ( 1 . 35 v . s . 0 . 22 ) and the standard deviation ( 0 . 59 v . s . 0 . 10 ) between Col-0 ( ) and Ler-0 ( ) and found that both the differences in mean and variance between the genotypes are signficant ( and respectively ) . Under the assumption that the difference between Col-0 and Ler-0 is only due to the effects of MOT1 , and can be estimated using the formulae above to be 58 . 7% and 11 . 0% , respectively . The lower effect on the mean and higher on the variance heterogeneity in the Atwell et al . dataset [1] is most likely due to the different experimental designs . The earlier studies were based on comparisons between two inbred lines selected to have a large mean difference in molybdenum levels , whereas the more recent study was population-based including lines with highly variable levels of molybdenum content . As the genetic background differs between lines in the population-based studies , effects of multiple alleles and genetic interactions are more likely in the population based data . Given that other genes contribute to the difference between the selected inbred lines , we might over-estimate the mean contribution and under-estimate the variance contribution in the data from Baxter et al . [25] and Tomatsu et al . [24] . Despite this , these datasets still show evidence of genetic variance heterogeneity . A novel locus affecting Leaf serration at 16°C was identified on chromosome 1 ( Table 1; Figure 4a ) . The genetic variance heterogeneity at this locus is due to a shift in the phenotypic distribution from normal to uniform ( Figure 4b ) . The locus is close to the suggested candidate gene ANAC13 [1] . Earlier studies have described similar effects on the phenotypic variance when disruptive mutations lead to a loss of control in a developmental pathway , leading to an unregulated system displaying a random ( uniform ) occurrence of the phenotype [26]–[28] . A closer inspection of the vGWAS evidenced region ( Figure 4 c , d ) , however , shows that the signal is very low in the coding region of ANAC13 and also that the coding region is in low LD with the SNPs that display the strongest association signals . This makes it less likely that the causative mutation leading to the observed effect on the phenotype is located in the coding region of this gene . Further studies of this Variation in Serration ( VS ) locus , including e . g . the regulatory regions of ANAC13 , are needed to identify the biological explanation for the observed effect . The two main variance-controlling loci detected in the vGWAS , MOT1 and VS , primarily affect the variance heterogeneity between genotypes in this dataset and only have small effects on the mean . When looking beyond these two loci to explore the total contribution of the sub-vGWAS significant loci to the phenotypic variance , many of these were found to also have effects on the mean ( Figure 1 ) . Also , a number of the loci detected in the GWAS were indicated to also affect the variance heterogeneity . To explore this observation further , we estimated the mean and variance controlling effects for the well-studied locus FRI ( Frigida ) that had the highest significance in the standard GWAS [1] . Genetic variability in this locus is known to influence its own mean expression level [1] and through effects on downstream loci influence flowering as well . Here , we found that this locus also had a significant effect on the genetic variance heterogeneity between the alternative FRI-genotypes ( , , and , ) for the trait FRI Expression . It is known that the expression of FRI influences flowering by inducing expression of Flowering Locus C ( FLC ) , which in turn delays flowering ( Figure 5 ) [29] , [30] . Here , we observe a variance heterogeneity between FRI genotypes that is not only present for FRI expression , but also for the other traits downstream in this pathway , i . e . FLC expression and several flowering traits ( Figure 5a; Table 2 ) . In biology , it is often observed that the phenotypic variance increases with the mean trait value . The mean shift is commonly thought to be of primary functional importance and the change in the variance a by-product of altering the mean . Adaption is , however , driven by selection of individuals based on their phenotype and consequently both the mean and the variance will affect this process . If the increase in the variance is not under genetic control , it will not be able to contribute to adaption and merely increase the noise in the phenotype and decrease the efficiency in selection . If the the heterogeneity in variance on the other hand is under genetic control , it might be selected for and potentially be of adaptive value . It is therefore of interest to understand the biological mechanisms leading to variance heterogeneity between genotypes and how such effects might impact the phenotype under selection . One example of where genetic control of the environmental variance might be of adaptive value is for variation in flowering time [31] . Under selection in a stable environment , the optimum time to flower will be relatively constant across years , suggesting a fitness advantage for alleles decreasing the variability in flowering time for its offspring . In a fluctuating environment , however , high-variance alleles are potentially more adaptive as offspring will flower over a broader time period , allowing a fraction of the offspring to reproduce every season . Here we observe a variance heterogeneity between FRI genotypes in the downstream phenotypes in the FRI-FLC pathway . Is there then also a functional propagation of the differential variance in FRI expression through the downstream pathway ? Or is this the result of a mere increase in the stochastic noise ? If there is a quantitative , rather than threshold , transmission of signals through the pathway , one could expect that the quantitative differences among individuals in FRI levels would result in quantitative differences also in FLC expression , resulting in a potentially FRI driven adaptive variation in flowering . Such a functional propagation through the pathway would result in a phenotypic correlation between the individuals for the phenotypes in the pathway , i . e . individuals for whom the levels of FRI deviate most from the mean would also be those where the deviations were the highest in FLC and flowering . The available data supports such a transmission of effects , as there are moderate to high correlations between the deviations from the trait median in the pathway ( Figure 5b ) . Furthermore , there is also a clear relationship between the trait values throughout the pathway for individual accessions ( Figure 5c ) , where FRI expression levels are strongly associated with high FLC expression and late flowering . Interestingly , other empirical data also indicate that variance heterogeneity in the FRI-FLC pathway might be of adaptive advantage . The low-variance ( loss of function ) FRI allele has appeared and remained multiple times in natural populations [32] without replacing the wild-type high-variance allele globally , suggesting that the alternative alleles have fitness advantages in different environments . FRI plays a central role in the vernalization response in Arabidopsis thaliana , where dominant alleles at this locus acts to confer late flowering , which is reverted to earliness by vernalization . Here , we find a gene-by-environment interaction effect between FRI and vernalization on both the mean and variance in flowering-time ( Figure 6 ) . FRI shifts the mean flowering time and the degree of variance heterogeneity both in the presence and absence of vernalization . The genetic effect of the wild-type FRI genotype on the variance heterogeneity is , however , much more dependent on the level of vernalization than the effect of the non-functional genotype . The observed genetic variance heterogeneity is not a mere general increase in the dispersion , but rather the appearance of very late flowering among a smaller number of accessions with the wildtype FRI genotype when there is less vernalization ( see also Figure 5c ) . In the absence of vernalization , a bi-modal phenotypic distribution appears , indicating an underlying strong interaction between the FRI-genotype , vernalization and at least one more locus or environmental factor .
We have validated the vGWAS strategy used in this study by simulations and shown that it controls the false-positive rate well . To avoid any potential strong influence of the population structure , we focus our discussion on results for traits with lower -value inflation ( 1 . 5 ) and also applied GC [15] . Our further analyses of the obtained results , including the thorough investigation of the most significant loci in the vGWAS and the enrichment analysis of a priori candidate genes indicate that the analysis provides results of biological significance . Further studies are needed to explore the extent of genetic variance heterogeneity in the genetic architecture of other populations and traits as well as to develop methods for accounting for more severe effects of population structure . Our results , however , strongly indicate that the vGWAS is a promising approach for analyzing genome-wide association data . Several earlier QTL studies have shown that it is possible to map loci that control the environmental variance of quantitative traits and identify plausible candidate loci for these effects [9]–[11] , [33] . These results are thus in line with what was shown in this report . Previous applications of vGWAS in human populations [15] , [16] , have , however , only found weak signals . The reason for this might be that human GWAS datasets normally contain noisy phenotypic measurements on many genotypes ( individuals ) , whereas this and other datasets from experimental populations contain phenotypic measurements with less non-systematic environmental noise on fewer genotypes . Also , as all inbred lines in this study were grown in the same environment for each phenotype measured , phenotypic plasticity had no effect on the single phenotypes in the study , which removes this as a potential cause for variance heterogeneity between genotypes [34] . The low non-systematic environmental noise , absence of effects from phenotypic plasticity and perhaps also an increased sensitivity of homozygous lines to environmental variation ( see [7] and references therein ) , thus makes the current design a better choice for mapping and exploration of genetic variance heterogeneity . We have illustrated the biological impact of genetic variance heterogeneity using three examples . MOT1 illustrates how an individual gene can explain a large fraction of the phenotypic variance by its genetic effect on the variance heterogeneity . The VS locus illustrates the potential of the vGWAS to identify loci underlying developmental stability , where the disruptions are likely to cause a random occurrence , rather than a directional shift , in the phenotype [26]–[28] . The FRI-FLC pathway is a well-studied system in Arabidopsis thaliana , and here we indicate that this pathway might not only regulate the average flowering time , but also the heterogeneity in flowering times . This through a potential propagation of genetic heterogeneity in gene-expression through the pathway and a gene-by-environment interaction leading to a differential variance heterogeneity in flowering times depending on the FRI genotype and the extent of vernalization . The dominant paradigm in current GWAS analyses is to identify additive loci through their effect on the mean difference between genotypes . The total contribution of the detected additive loci to the narrow-sense heritability is then estimated as the sum of their individual effects . The discrepancy between the estimates of the heritability for the studied trait in the population and the sum of the effects of the loci detected in the GWAS is often referred to as the “missing heritability” . As this discrepancy appears to be large , even when large populations are analyzed , there has been an intense discussion regarding the potential mechanisms underlying this . The observation has also increased the interest in exploring alternative approaches to analyze GWAS data . Identifying loci contributing to the genetic control of the environmental variation will allow us to better explain the genetic contribution to the phenotypic variation , but not the narrow-sense heritability . Some loci detected in the vGWAS might , however , be involved in gene-gene or gene-environment interactions , result from an incomplete LD between the causal polymorphism and the tested marker , as well as contain multiple functional alleles . In such situations , the loci might make contributions to the narrow-sense heritability that are difficult to detect using a standard GWAS [14]–[16] . By accounting for genetic variance heterogeneity in future analyses of GWAS data , we foresee that more genes that contribute to the phenotypic variation through non-additive genetic effects on the mean and genetic regulation of the environmental variation can be mapped and functionally dissected . Consequently , the vGWAS will allow genetic analysis to proceed beyond the current GWAS paradigm , dissect the genetic regulation of the environmental variance and potentially also detect loci contributing to the currently unexplained genetic variance . The discussion in the field of quantitative genetics regarding the potential importance of genetic heterogeneity between genotypes have historical roots [35] . The results reported here provides insight to the genome-wide effects of variance heterogeneity and show that the genome contain many loci that contribute to the phenotypic variance through a genetic control of the variance heterogeneity . Earlier studies on the genetic control of robustness in gene-expression indicates that it , at least to some extent , is under genetic control by individual loci with measurable effects [12] . Our finding that genetic variance heterogeneity might also be propagated in gene-expression pathways could have further functional implications for studies of the regulation of gene-expression . Studies are therefore needed to explore whether the extent of regulatory control over variance heterogeneity in expression pathways is of functional importance . If this regulation proves to be important , it adds a new dimension to the complexity in regulatory models . Such studies of the propagation of regulatory effects on the variability of expression could e . g . be performed by mapping of cis-regulated variance-controlling loci in genetical genomic studies followed by subsequent identification of downstream variance heterogeneity in known pathways , or by searching for co-expression on the level of variance in traditional microarray experiments . It will be interesting to see if this new way of dissecting the regulatory control in the transcriptome , using data that is already publicly available for many species , could provide a new handle on this topic .
In a single-locus additive model , the phenotypic variance is partitioned ashere is the additive variance , and is the residual variance . This model only accounts for effects of genes on the mean difference between genotypes . For a single locus , we instead suggest to dissect the phenotypic variance into the variance due to the mean shift between genotypes , , the variance due to the variance heterogeneity , , and the remaining residual variance , i . e . Since inbred lines are analyzed in this paper , there is no dominance , and consequently . We therefore have , where equality holds if and only if , i . e . captures a part of that is not stochastic noise , but actually contributions by genetics . Several alternative quantitative genetics models have been proposed for modeling the genetic effect on the environmental variance ( see e . g . [3] for a review ) . Here , we review and use the well-established quantitative genetics estimation equation for and also explicitly derive the proportion of due to variance heterogeneity , , for a single locus in this quantitative genetics framework . This is to clearly present and investigate the properties of these quantities when applied in a vGWAS context ( For details on the derivations , see Text S1 ) . / here denotes the high/low-variance allele ( HA/LA ) , respectively . Our quantitative derivation resembles the “standard deviation model” in [3] , which assumes an additive model for the standard deviation per genotype . is here the phenotypic variance explained by and identical to . From basic probability theory , we haveSimilarly , is measured as the variance of , The total variance of the phenotype iswhere , and is the mean environmental variance . is a part of , and the remaining residual variance isThe proportion of due to variance heterogeneity is thusWe investigated properties of the above quantities in detail ( see Text S1 ) , and it is worth noting that both the narrow sense heritability and are maximized whenwhich is ( see e . g . Figure 2 ) . Only when no variance heterogeneity exists , is maximized at . For testing variance-controlling SNPs in the vGWAS , we use the Brown-Forsythe ( Levene ) test . The Brown-Forsythe is a statistical test for the equality of group variances and is based on an ANOVA of the absolute deviation from the median . It has earlier been shown to be robust to deviations from normality of the phenotypic distribution in GWAS applications [16] . If the phenotypic value is for individual with genotype , where , and , the absolute deviations from the median of each genotype arewhere is the median of the phenotypic values of the individuals that have genotype . Performing a one-way ANOVA on , we have the ANOVA statisticwhere is the number of observations in group . This statistic follows an distribution with , degrees of freedom . Usually , is sufficiently large to approximate the statistic as a statistic with degrees of freedom . The nominal -values calculated using such -statistics are used in the vGWAS with a Bonferroni corrected significance threshold . In an ordinary GWAS , genomic control ( GC ) is used to shrink any existing inflation of the test scores ( -values ) . When testing for the single genetic effect in the GWAS , the null distribution of the test statistic for the nominal -values is with 1 degree of freedom . Since most of the SNPs are not expected to be associated with the trait , the sample distribution of the chi-squares across the genome should resemble the null distribution . If there is inflation , the chi-squares are adjusted using , i . e . the inflation factor estimated by comparing the distribution of the sample 's and distribution with 1 degree of freedom . As the sample size in this study is sufficient to approximate the -statistic of the Brown-Forsythe test using a statistic , the ordinary GC methods can be applied . Here , we regress the sample 's on the null 's with a zero-intercept and take the slope as an estimate of , which is the approach used in the current version of the GWAS analysis package GenABEL [36] . This approach was selected as it is expected to be more conservative than , or similar to , the alternative way of estimating using the ratio of the observed median of 's to the theoretical median of with 1 degree of freedom [21] .
|
The most well-studied effects of genes are those leading to different phenotypic means for alternative genotypes . A less well-explored type of genetic control is that resulting in a heterogeneity in variance between genotypes . Here , we reanalyze a publicly available Arabidopsis thaliana GWAS dataset to detect genetic effects on the variance heterogeneity , and our results indicate that the environmental variance is under extensive genetic control by a large number of variance-controlling loci across the genome . A straightforward extension of current quantitative genetics theory was derived to estimate the contribution of genetic variance heterogeneity to the phenotypic variance for loci detected in the vGWAS . This showed that some variance-controlling loci explained more than 20% of the phenotypic variance . Genetic variance heterogeneity was detected in various biological processes , including cellular control of ion levels in the plant and regulation of flowering . Our findings indicate that further studies of genetically determined variance heterogeneity are important to further understand the extent of its biological importance . Accounting for variance-controlling loci in complex trait genetic studies is a useful way to identify previously unexplained genetic variance , dissect the genetic control of environmental variance , and gain biological insight into the genetic regulation of complex traits .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
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2012
|
Inheritance Beyond Plain Heritability: Variance-Controlling Genes in Arabidopsis thaliana
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GATA transcription factors play critical roles in cellular differentiation and development . However , their roles in mature tissues are less understood . In C . elegans larvae , the transcription factor ELT-2 regulates terminal differentiation of the intestine . It is also expressed in the adult intestine , where it was suggested to maintain intestinal structure and function , and where it was additionally shown to contribute to infection resistance . To study the function of elt-2 in adults we characterized elt-2-dependent gene expression following its knock-down specifically in adults . Microarray analysis identified two ELT-2-regulated gene subsets: one , enriched for hydrolytic enzymes , pointed at regulation of constitutive digestive functions as a dominant role of adult elt-2; the second was enriched for immune genes that are induced in response to Pseudomonas aeruginosa infection . Focusing on the latter , we used genetic analyses coupled to survival assays and quantitative RT-PCR to interrogate the mechanism ( s ) through which elt-2 contributes to immunity . We show that elt-2 controls p38-dependent gene induction , cooperating with two p38-activated transcription factors , ATF-7 and SKN-1 . This demonstrates a mechanism through which the constitutively nuclear elt-2 can impact induced responses , and play a dominant role in C . elegans immunity .
Induction of local innate immune responses is the first reaction to an invading pathogen , and includes increased expression of antimicrobial effector peptides/proteins , as well as immune modulators . Regulation of these responses depends on signaling modules that are similar in their principles of action from plants to animals , suggesting convergent evolution [1] . Within the animal kingdom these signaling modules often use similar proteins , such as pattern recognition receptors , their downstream signaling cascades , and MAP kinase signaling pathways [2 , 3] . This conservation warrants the study of innate immune mechanisms in well-characterized invertebrate model organisms , such as Drosophila melanogaster and Caenorhabditis elegans , to better understand their vertebrate counterparts . Studies of C . elegans immunity have repeatedly converged on the p38 MAPK pathway as a pivotal module in orchestrating immune responses , very similar to its roles in vertebrate innate immune responses [4–7] . The core components of the C . elegans p38 pathway include the NSY-1 MAP3K , the SEK-1 MAP2K , and the PMK-1 MAPK . TIR-1/SARM was shown to serve as an upstream activator during infection [8 , 9] , and VHP-1/DUSP8 , as a negative regulator [10] . Downstream to the p38 pathway , several transcription factors have been shown to mediate effects on gene expression: ATF-7 , an ATF-2 ortholog , was shown to regulate immune gene expression in the intestine [11]; DAF-19/RFX , was shown to cooperate with ATF-7 in regulating genes involved in neuronal serotonin synthesis , but was also found to contribute to expression of intestinal immune genes [12]; SKN-1/Nrf , better known for regulating oxidative stress responses , was further found to contribute to resistance against bacterial pathogens [13–15] . In addition , ELT-3 was identified as a regulator of epidermal anti-fungal responses , a subset of which was also regulated by the p38 pathway [16] . ELT-3 is one of two C . elegans transcription factors of the GATA family with roles in epithelial development and differentiation , and additional roles in regulating immune responses . ELT-3 is important for epidermal differentiation and epidermis-specific gene expression [17] . The second GATA protein is ELT-2 , which is important for terminal development of the intestine and for intestine-specific gene expression [18 , 19] . Whereas ELT-2 was proposed to be the predominant regulator of all intestinal gene expression , experiments supporting this were performed only in embryos or L1 larvae , leaving the extent of its roles in the adult intestine unresolved [20 , 21] . We , and others , have shown that ELT-2 regulated specific anti-bacterial responses in the adult intestine [22–24] . Similar roles , both in endodermal development , as well as in adult immune regulation and protection , were described for the Drosophila GATA protein Serpent and for the vertebrate GATA6 [22 , 25] . Vertebrate GATA transcription factors comprise two homology groups: GATA1-3 are regulators of lymphocyte terminal differentiation and cytokine expression; GATA4-6 are regulators of mesodermal and endodermal differentiation ( in the heart , liver , lung , and pancreas ) , and are considered the orthologs of elt-2 [26 , 27] . In the adult endoderm , GATA4 and GATA6 were also shown to play key roles in the regulation of stress responses [28 , 29] . Importantly , MAPK signaling , including signals from the p38 pathway , regulates the activity of GATA4 during stress responses [30] . Thus , it is possible that ELT-2 is similarly regulated during infection . To better understand the roles of ELT-2 in the adult intestine , particularly its involvement in immune gene regulation , we characterized gene expression following elt-2 knock-down specifically in adults . This identified two gene subsets: one that was constitutively regulated by ELT-2 and included genes involved in digestive degradation of macromolecules; and a second , which was induced in response to infection , and included genes previously implicated in protection from pathogens . Members of the latter demonstrated co-regulation by ELT-2 and the p38 pathway . Subsequent genetic analyses identified genetic interactions between elt-2 and the p38 transcriptional mediator genes atf-7 and skn-1 in regulating C . elegans innate immune responses . Our results suggest a dominant role for elt-2 in the regulation of digestive and metabolic functions of the intestine , and the role of a master regulator for p38-dependent immune responses , cooperating with activated transcription factors to control induced responses .
To identify genes regulated by elt-2 , we compared gene expression profiles in animals fed with elt-2 RNAi during the first two days of adulthood ( RNAi-ad ) to those in control-treated animals , either following a twelve hour infection with Pseudomonas aeruginosa , or exposure to non-pathogenic E . coli ( Raw data can be downloaded from GEO , accession no . GSE63846 ) . Adult elt-2 knock-down has been shown to cause a marked decrease in ELT-2 protein levels persisting up to three days after worms were removed from RNAi plates [22] . Successful knock-down is also discernible by eye , as animals present a modest ‘clear’ phenotype , potentially due to reduced fat storage ( S1A Fig ) . Previous work found elt-2 ( RNAi-ad ) animals to be more susceptible to infection , but to have a normal lifespan on dead E . coli , suggesting that effects of post-developmental elt-2 knock-down are largely immune-specific [22] . Microarray analysis identified 429 transcripts , corresponding to 420 genes , which were differentially expressed in elt-2 ( RNAi ) animals compared to control-treated animals ( Fig 1A ) . Prominent clusters of co-regulated genes included a cluster of 187 genes with reduced expression following elt-2 knock-down ( ‘elt-2-regulated’ ) , suggesting contribution of elt-2 to constitutive expression ( Fig 1A and S2 Table ) ; a cluster of 96 genes , that were also suppressed following elt-2 knock-down , and additionally failed to be induced by infection in elt-2 ( RNAi ) animals ( ‘elt-2-induced’ ) ; and a cluster of 43 genes showing elevated expression following elt-2 knock-down , suggesting repression by the transcription factor ( ‘elt-2-repressed’ ) . qRT-PCR verified elt-2 regulation for three selected ‘elt-2-regulated’ , and seven ‘elt-2-induced’ genes ( S2A and S2B Fig ) . Additional measurements for ‘elt-2-induced’ genes in animals exposed to the pathogen for a longer duration ( 24 hours ) similarly showed no infection response in elt-2 ( RNAi ) animals , suggesting that impaired induction represented a complete failure rather than a delay ( S2C Fig ) . To identify potential direct ELT-2 targets in the three subsets , we searched gene promoters for the GATA motif core sequence , TGATAA [20 , 22] . GATA motifs are prevalent in the genome , as targets for various developmental and tissue-specific transcription factors . However , an examination of GATA motif distribution in upstream sequences of elt-2-dependent genes revealed a statistically-significant enrichment for GATA motifs in proximal promoter regions , in contrast to a uniform distribution in upstream regions of randomly-selected genes ( Fig 1B and S3 Table ) . Focusing on proximal promoter regions ( 500 bp ) to better differentiate between elt-2 targets and non-targets , GATA motifs were identified in 72% of the ‘elt-2 induced’ genes , 50% of ‘elt-2 regulated’ genes , and 47% of ‘elt-2 repressed’ , compared to 42% in upstream sequences genome-wide , demonstrating a significant enrichment for GATA motifs in promoters of elt-2-induced and elt-2-regulated genes , but not among ‘elt-2 repressed’ genes ( p = 5 . 6E-10 , 0 . 004 and 0 . 1 , respectively; hypergeometric distribution ) . Twelve of the GATA-containing genes were among those tested by qRT-PCR ( nine of the ‘elt-2-induced’ subset , and three of the ‘elt-2-regulated’ subset ) and indeed demonstrated elt-2-dependent expression ( S2 Fig ) . In addition to enrichment of GATA promoter motifs , 55% of the ‘elt-2 induced’ , and 32% of the ‘elt-2 regulated’ genes were genes previously reported to be preferentially expressed in the intestine [19 , 20 , 31] ( and Wormbase ) ; only 6/43 ( 14% ) of the ‘elt-2 repressed’ genes were intestinal , while 12/43 were genes shown to be preferentially-expressed in muscle tissue [32] . Together , these analyses suggest that a large fraction of the elt-2 regulated genes , in particular of the ‘elt-2-induced’ genes , are direct ELT-2 targets . Nevertheless , some ‘noise’ is included in these subsets in the form of genes that are indirectly affected by elt-2 knock-down . In the case of ‘elt-2-repressed’ genes it seems that most are affected indirectly , and probably outside of the intestine , suggesting a negligible contribution of elt-2 to direct gene repression . To learn about potential contributions of putative ELT-2 targets to worm physiology , we next examined their associated GO annotations . Among the ‘elt-2-regulated’ genes enrichment was found for genes involved in innate immunity and defense responses ( represented by 13 genes , p = 8 . 48E-05 , Bonferroni corrected ) , and for genes with hydrolase activity ( 37 genes , p = 0 . 038 , not corrected ) ( S4 Table , highlighted in yellow ) . The former are genes that were previously shown to respond to infection [22] , suggesting that they might have been inappropriately assigned as ‘elt-2 regulated’ due to a weak response or noisy measurements , and were more likely to be part of the ‘elt-2 induced’ subset . The more telling members of the ‘elt-2 regulated’ subset appeared to be the hydrolase genes , which mainly included proteases and lipases , and pointed at regulation of these enzymes as an important function of elt-2 in adults . Regulation of three of these enzymes by elt-2 was confirmed by qRT-PCR ( S2A Fig ) . While enrichment for genes annotated as hydrolases is not strictly statistically significant , this may be due to the noise in the ‘elt-2-regulated’ list . Supporting the central role of elt-2 in regulating hydrolytic enzymes in the adult intestine , the overlap between the ‘elt-2-regulated‘ gene list and a previously published list of genes specifically expressed in the adult intestine [20] consisted of fifteen genes , four of which are associated with immune defense functions , seven that encode hydrolytic enzymes , and four unknowns ( S4 Table ) . In embryos , elt-2 has been shown to contribute significantly to expression of genes encoding structural intestinal proteins [20 , 33] . However , in agreement with previous results , our microarray data did not reveal effects of elt-2 knock-down in adults on the expression of act-5 ( microvilli structure ) , let-413 ( adherens junctions ) , eps-8 ( apical morphogenesis ) , and ifb-2 ( intestinal-specific intermediate filament ) [22] . In addition , qRT-PCR analysis found no effect of elt-2 knock-down on the expression of non-hydrolytic genes previously shown to be expressed in the adult intestine: lmp-1 ( lysosomal membrane ) , mrp-5 ( membranal transport ) , and ubl-1 ( possibly involved in protein translation ) ( S3 Fig ) , whereas hydrolytic enzyme gene expression was reduced in the same RNA samples ( as shown in S2A Fig ) . Together , this indicated that elt-2 was necessary for specific functions in the adult intestine , but not for all . ELT-2 was previously shown to function synergistically with ELT-7—a co-expressed intestinal GATA transcription factor—in morphological gut differentiation and in larval gut-specific gene expression [21] . It is possible that redundancy between elt-2 and elt-7 masked additional contributions of elt-2 to intestinal gene expression . Nevertheless , the results presented highlight elt-2’s dominant contribution to hydrolytic gene expression . For the ‘elt-2-induced’ gene subset , all enriched ‘process’ GO annotations were related to defense and innate immune responses ( 22 genes , p = 1 . 5E-15 ) ( S4 Table ) . In addition , ten genes of this subset were annotated with carbohydrate binding , most of which are lectins , which are known to take part in C . elegans innate immune responses , and have been suggested to play roles in pathogen recognition [34] . These enriched annotations support the dominant role previously proposed for elt-2 in regulating intestinal innate immune responses . Interestingly , elt-7 is a member of the ‘elt-2-induced’ subset , suggesting participation in immune responses; however , previous work could not identify any significant contribution of elt-7 to immune protection [22] . ELT-2 acts as a regulator of intestinal development following activation of its expression . This expression is maintained in adults , possibly through autoregulation [35] . ELT-2 was previously shown to be constitutively nuclear [35] . Therefore , to take part in regulation of induced responses ( as demonstrated for ‘elt-2-induced’ genes ) its activity must be modulated by some signal transduction pathway ( s ) . A likely candidate is the p38 pathway , which is known to play an important role in regulating C . elegans immune responses [4] . Among genes previously described to be regulated downstream to the MAPKK gene sek-1 or the p38 MAPK gene pmk-1 [36] , and included in our filtered dataset , 38% ( 22/57 ) and 33% ( 13/39 ) , respectively , were also regulated by elt-2 ( p<4E-8 ) ( Fig 2A ) . This suggested that elt-2 co-regulated genes with the p38 pathway , potentially downstream to it . To examine this possibility , we knocked down elt-2 in adult sek-1 ( km4 ) mutants . While elt-2 knock-down significantly decreased resistance in wildtype animals , its effect on the already compromised resistance of sek-1mutants was marginal ( Fig 2B ) . The fact that overlap between p38 and elt-2 targets was only partial could reflect technical differences between the two studies , resulting in different coverage of the respective datasets; additionally , it may reflect partially aligned regulatory programs , with some contributions to gene expression that are independent of each other . The survival analysis , showing only marginal exacerbation of infection susceptibility of sek-1 mutants by elt-2 RNAi is more consistent with the first possibility . We next turned to gene expression , to further examine the relationship between the elt-2 and p38 regulatory modules . We began by examining the expression of a GFP reporter controlled by the promoter of F55G11 . 2 , an early immune response gene regulated by both elt-2 and the p38 pathway [22 , 36] . RNAi knock-down in adult worms demonstrated that both the p38 MAP3K gene nsy-1 , and more so elt-2 were necessary for basal expression from the F55G11 . 2 promoter ( Fig 2C ) . In response to P . aeruginosa , F55G11 . 2 induction was apparent within four hours in control-treated animals , but not in elt-2 knock-down animals . Disruption of nsy-1 also reduced immune induction , but not as much as elt-2 disruption . Similar results were observed in pmk-1 ( km25 ) mutants , corroborating the co-regulation of F55G11 . 2 by p38 signaling and elt-2 , and the dominant contribution of elt-2 to its expression ( S4 Fig ) . Using mutants carrying the pmk-1 ( km25 ) null allele , we expanded our analysis ( and increased its sensitivity ) by employing qRT-PCR to follow expression of genes potentially co-regulated by p38 signaling and elt-2 . Because p38-dependent responses are more pronounced in younger worms [37] , we measured gene expression at the end of larval development . And while knock-down of elt-2 during development has more pronounced effects than during adulthood , giving rise to scrawny worms ( S1B Fig ) , elt-2 ( RNAi-dev ) worms are healthy enough to reach adulthood and lay eggs . Expression was measured for F55G11 . 2 , and for genes that were part of the overlap between elt-2 and p38 targets ( Fig 2A ) : C32H11 . 12 ( ‘elt-2-induced’ ) , T24G8 . 5 , clec-85 and clec-186 ( all three ‘elt-2-regulated’ according to the microarray analysis , and infection-induced in younger animals according to [22] ) . Two additional p38 targets were included , C17H12 . 8 , and F08G5 . 6 , the latter of which was previously shown to provide protection from infection [22] . All examined genes included proximal-promoter GATA motifs . qRT-PCR demonstrated that the seven genes were all regulated by both elt-2 and pmk-1 . Basal expression was significantly reduced following elt-2 knock-down , compared to age-matched control-treated animals , and was similarly reduced in pmk-1 mutants ( Fig 2D ) . A twelve-hour exposure to P . aeruginosa induced the expression of all seven in wildtype animals , but the regulation of this induction divided the genes into two subsets . Induction of 5/7 genes was abolished by either pmk-1 or elt-2 disruption , indicating dependence on the two factors . However , F55G11 . 2 and C32H11 . 12 , which depended on pmk-1 or elt-2 for basal expression , were significantly induced above basal levels , even when both pmk-1 and elt-2 were disrupted , suggesting that F55G11 . 2 and C32H11 . 12 may be regulated by additional factor ( s ) ( Fig 2E ) . The relative induction observed in these experiments was not apparent in the GFP reporter strain , presumably due to the increased sensitivity of qRT-PCR compared to fluorescence measurements . Similar experiments were performed with adult worms , which showed significantly lower gene induction during infection , but otherwise , similar contributions of elt-2 and pmk-1 to gene expression ( S5 Fig ) . Lastly , whether elt-2 disruption can exacerbate gene repression in pmk-1 mutants is not clear , since additive effects were observed in two-day adults ( S5 Fig ) , but not in L4 larvae ( Fig 2E ) . Survival and gene expression analyses in L4 larvae suggested that elt-2 may be epistatic to pmk-1 . To examine whether elt-2 knock-down could abrogate pmk-1-dependent gene expression , we knocked down vhp-1 , which encodes a phosphatase that dephosphorylates and inactivates PMK-1 [10] . Accordingly , knock-down of vhp-1 caused a significant induction of T24B8 . 5 , C32H11 . 12 , clec-85 and clec-186 ( Fig 2F ) . Simultaneous knock-down of elt-2 abrogated this induction . This was not due to reduced efficiency of vhp-1 RNAi in a double knock-down setting , as vhp-1 knock-down was able to induce gene expression when mixed with another RNAi ( see below ) . Instead , these results suggested that elt-2 was essential for pmk-1 dependent immune gene expression .
ELT-2 was previously shown to be an immune regulator in adult worms , contributing to immune responses and infection resistance [22] . Whereas the vertebrate protein GATA3 activates gene expression following nuclear translocation induced by p38 phosphorylation [41] , nuclear localization of the elt-2 ortholog GATA4 was instead shown to be controlled by the kinase GSK3β [42] . In contrast , ELT-2 was proposed to be constitutively localized to the nucleus [35] . Thus , how elt-2 contributed to induced responses was not clear , and if p38 was responsible for infection-induced activation of ELT-2 , it was still unclear how this was achieved . While our results cannot rule out ELT-2 phosphorylation by the p38 pathway , they suggest a model in which ELT-2 functions as a master regulator of immune gene expression , cooperating with transcription factors activated by the p38 pathway , namely ATF-7 and SKN-1 ( Fig 5 ) . Under normal conditions , ATF-7 functions as a repressor and interferes with elt-2-dependent gene expression; SKN-1 contributes positively to the expression of some genes ( of group B , see Fig 5 ) , but not others ( group A ) . Upon exposure to a pathogen , PMK-1 is activated , phosphorylating ATF-7 and transforming it into a transcriptional activator [11] . In this capacity , ATF-7 cooperates with ELT-2 to induce immune gene expression . To better fit this model to the results , it is necessary to consider that under normal conditions activated PMK-1 is present ( supported by [43 , 44] ) ; indeed , “normal” conditions include the presence of E . coli OP50 , which is a weak pathogen [38 , 45] . Thus , by constitutively controlling the interference of ATF-7 with elt-2-dependent expression , PMK-1 plays a role in establishing basal levels of immune gene expression . Whereas co-regulation by ELT-2 and ATF-7 was sufficient to explain immune responses of group A genes , group B genes additionally depended on SKN-1 . Our results support a model in which elt-2 is independently required for atf-7- and skn-1-dependent gene expression , which could explain the observed additive effects in the contributions of skn-1 and atf-7 to the expression of group B genes . SKN-1 can be directly phosphorylated and activated by the p38 pathway [13] , but can alternatively be activated by reactive oxygen species ( ROS ) generated as part of the protective immune response [14] . Furthermore , alternative sources of ROS ( e . g . induced by infecting pathogens [46] ) may activate SKN-1 independent of the p38 pathway , as suggested by the reported inability of p38 disruption to completely abolish the induction of oxidative stress response genes during infection [14] . A p38-indepedent SKN-1 activation could explain the results presented in Fig 2 , demonstrating induction of F55G11 . 2 and C32H11 . 12 in infected pmk-1 mutants . Lastly , a recent report suggested an involvement of the PQM-1 transcription factor in regulating F55G11 . 2 under normal conditions [47] . pqm-1 affected F55G11 . 2 expression , but its contribution appears to be small compared to what we have observed with elt-2 . While pqm-1 may provide yet another regulatory input to F55G11 . 2 gene expression , its contribution is not required for explaining F55G11 . 2’s expression patterns during infection . As a key regulator of intestinal terminal differentiation , the continued expression of elt-2 in the adult worm has been considered as required for maintenance of intestinal structure and function . Support for this was offered by experiments showing that ectopic elt-2 expression , or elt-2 disruption , during embryogenesis , affected expression of intestinal genes , some of which are expressed in adults [20] . However , with only about 10% overlap between adult and embryonic intestinal gene sets it seems that such experiments might reflect elt-2 contributions in embryos and not necessarily in adults . Differences in elt-2 contributions in different ages have been described . For example , expression of ifb-2 , which encodes an intermediate filament protein , is abolished by elt-2 disruption in embryos , but is unaffected in L1 larvae [21]; similarly , it is unaffected in adults [22] ( and this study ) , suggesting diminishing regulatory contributions . It was demonstrated that past embryogenesis , elt-2 contributed redundantly to intestinal gene expression with a second intestinal GATA transcription factor , ELT-7 [21]: whereas neither disruption of elt-2 , nor elt-7 , affected larval ifb-2 expression , disruption of both abolished this expression; this pattern of redundant regulation was shared by several genes , most of which encode intestinal structural proteins . It is quite possible that elt-2 , together with elt-7 , maintains its contributions to expression of structure-related genes in the adult intestine . However , our results suggest a distinct , and dominant , role for elt-2 in the adult intestine—regulating the expression of hydrolytic enzymes . Such regulation is potentially important for intestinal function ( digestion ) , but also creates a hostile environment for invading pathogens . It is tempting to suggest that the lack of redundancy in regulating these genes ( manifested as reduction in gene expression following knock-down of elt-2 alone ) is related to the dominant contribution of elt-2 for immune responses . While hydrolytic enzyme genes are the only ones that we found to be enriched among the ‘elt-2-regulated’ genes , they make up only 20% of this subset . It is possible that additional elt-2-regulated functions are included in this subset , but are obscured by indirectly regulated genes , which our bioinformatic analysis suggests make up a significant part of this gene subset . In summary , our genome-wide analysis helps distinguish between basal and pathogen-induced elt-2-dependent regulons in the adult worm . Whereas the functional composition of the two appears to be distinct , an overarching theme of anti-bacterial functions is consistent with the idea that bacteria can be both food and pathogens . Additional results further shed light on the largely uncharacterized contribution of elt-2 to induced responses , revealing cooperation with the transcription factors ATF-7 and SKN-1 downstream to the p38 pathway , and suggesting a function of a tissue-specific master regulator . Whereas elt-2 contributions to gene expression during and after development seem to differ both compositionally and mechanistically , it seems that its status as a master regulator is maintained in the adult intestine .
They were obtained from the Caenorhabditis Genetics Center and included wild-type N2; sek-1 ( km4 ) , pmk-1 ( km25 ) and atf-7 ( qd22qd130 ) signaling mutants; and spe-26 ( it112 ) temperature-dependent sterile mutants , which lay unfertilized eggs . PF55G11 . 2::gfp worms were designed as described below , and further mated to generated PF55G11 . 2::gfp;pmk-1 ( km25 ) worms . Bacterial strains included: E . coli strain OP50-1 , Pseudomonas aeruginosa strain PA14 , and the latter’s GFP-expressing derivative PA14-GFP [48] . It was performed with the standard feeding protocol , using bacterial clones from the Ahringer library , with empty RNAi vector ( EV ) serving as control [22 , 49] . The exception is atf-7 RNAi , which was from the Open Biosystems library . RNAi feeding was performed for two days , starting at the egg stage ( RNAi-dev ) , or late L4 ( RNAi-ad ) . The protocol used here was previously shown ( in worms expressing ELT-2::GFP ) to result in a complete knock-down of ELT-2 [22] . All experiments were carried out using synchronized worm populations grown on E . coli at 25°C . Infections were performed using the slow killing protocol , typically at 25°C , or when following survival of sensitive strains , at 20°C [48] . Survival analysis of adult sek-1 ( km4 ) mutants was performed with cdc-25 . 1 ( RNAi ) -sterilized animals [50] , to avoid confounding effects of internal egg hatching . Statistical evaluation of differences between survival curves was performed using Kaplan-Meier analysis followed by the Log-rank test . Worms were exposed to RNAi ( control or elt-2 ) beginning at the L4 stage , and following two days were transferred either to E . coli OP50 or to P . aeruginosa PA14-GFP . Following eighteen hours of exposure ( control ) , or twelve hours ( elt-2 RNAi ) , worms were harvested for RNA extraction and microarray analysis . In a previous study we sought to determine the contribution of colonization ( and its associated damage ) , versus specific pathogen recognition , to differential innate immune responses , and what role elt-2 played in regulating these responses . Therefore , worms were separated into those that were conspicuously colonized with the GFP-expressing pathogen , and those that were not visibly colonized . Times of exposure to the pathogen were optimized to maximize colonization variability in the population and were therefore shorter in the more susceptible elt-2 ( RNAi ) worms . In our previous study we focused on immune responses only in control-treated animals and found them to be identical irrespective of colonization status [51] . In the current analysis we focused on the role of elt-2 in innate immune responses as a whole , utilizing data from control-treated animals as a reference for comparison . For this purpose , data from colonized and non-colonized worm groups can be pooled into one group—exposure to pathogen . This results in six independent repeats in control ( RNAi ) animals exposed to the pathogen , compared to three repeats of similarly-treated animals exposed to E . coli; for the elt-2 ( RNAi ) animals , the exposure to E . coli was performed in duplicate , and to the pathogen—in triplicate . RNA was extracted from worms using Trizol ( Invitrogen ) ( 100–700 worms per group ) , and amplified using the MessageAmp II aRNA Amplification Kit ( Ambion ) , labeled with the ULS aRNA Labeling Kit ( Kreatech ) and co-hybridized to Epoxy ( Corning ) microarrays spotted with 60-mer oligonucleotides ( Washington University Genome Sequencing Center ) with a similarly amplified and labeled reference RNA sample [51] . Filtering for high-quality data resulted in 7 , 880 genes with expression values >2 . 5 fold over background in >70% of the microarrays . These gene expression profiles were analyzed with the SAM microarray analysis package [52]; a two-class testing configuration was used to identify genes differentially-expressed during infection in untreated worms compared to elt-2 ( RNAi ) worms , with a false discovery rate of 9% . A genomic fragment including 1 . 7 Kb of F55G11 . 2 upstream region was amplified ( annealing: 60°C ) using specific primers A-gaagcgcattggtctttga , and B- AGTCGACCTGCAGGCATGCAAGCTttccagcggcggaaact , the latter tailed ( capitalized ) , for subsequent recombinant PCR . This fragment includes part of the F55G11 . 3 upstream pseudogene , as well as the initial 58 bp of F55G11 . 2 coding sequence . Recombinant PCR fused this fragment with gfp , as previously described , using the nested primer A* ( caatttggacacggcaaact ) together with the previously described D* primer [53] . Transgenic animals were generated by microinjecting PCR products , together with the rol-6 ( su1006 ) dominant marker , into worms . Genome integration was subsequently achieved by UV irradiation , as described [54] . GFP signal was quantified in worm images using the MetaMorph analysis software ( Molecular Devices ) . RNA extracted as described above was used as template with primers listed in S1 Table . Gene-specific threshold cycle ( Ct ) values were normalized to the respective actin values , and presented as fold change over normalized values from control-treated animals exposed to E . coli , or when relative induction was assessed , as fold change in worms exposed to P . aeruginosa over values in worms of similar genetic background/treatment exposed to E . coli . Statistical significance was evaluated with a t-test using actin-normalized Ct values . Management and analysis of gene lists was performed using WormMine ( http://www . wormbase . org/tools/wormmine/ ) . Searches for the GATA DNA motif were performed using the MEME suite ( http://meme . nbcr . net ) : FIMO , for analysis of motif distribution; and MAST , for motif prevalence . The DNA motif used for searches was the consensus sequence TGATAA , shared by GATA motifs in different datasets [20 , 22] . Promoter sequences were retrieved with Worm mart , from Wormbase version WS220 . GO analysis was performed with Generic GO Term Finder ( http://go . princeton . edu/ ) , using a gene association file downloaded from Wormbase version WS245 , and applying Bonferroni correction for p-value calculation ( unless otherwise mentioned ) .
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C . elegans provides a tractable genetic model to study the regulation of the evolutionarily conserved innate immune system . One of the central signaling modules of innate immunity in all organisms is the p38 pathway , which has been studied extensively in C . elegans . Such studies identified the transcription factors ATF-7 and SKN-1 as proteins mediating downstream effects of the p38 pathway on immune and oxidative stress gene expression . Previous studies in C . elegans also identified ELT-2 , a conserved transcription factor important for intestinal development , as a major regulator of immune responses in the adult worm . The current study aimed to characterize the interactions between these two immune regulatory modules . Microarray gene expression analysis in animals with disrupted elt-2 expression revealed two gene subsets that were regulated by elt-2: one that included constitutively regulated genes , and was mostly comprised of digestive enzyme genes , and a second that included genes induced by infection with Pseudomonas aeruginosa . Both subsets were enriched for p38 targets . Genetic analyses and gene expression measurements of elt-2-regulated genes demonstrated that elt-2 cooperates with the p38 pathway and its downstream mediators . These results suggest that ELT-2 functions as a tissue-specific master regulator controlling the contribution of the p38 MAPK pathway to innate immune responses .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
The Developmental Intestinal Regulator ELT-2 Controls p38-Dependent Immune Responses in Adult C. elegans
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The finding of power law scaling in neural recordings lends support to the hypothesis of critical brain dynamics . However , power laws are not unique to critical systems and can arise from alternative mechanisms . Here , we investigate whether a common time-varying external drive to a set of Poisson units can give rise to neuronal avalanches and exhibit apparent criticality . To this end , we analytically derive the avalanche size and duration distributions , as well as additional measures , first for homogeneous Poisson activity , and then for slowly varying inhomogeneous Poisson activity . We show that homogeneous Poisson activity cannot give rise to power law distributions . Inhomogeneous activity can also not generate perfect power laws , but it can exhibit approximate power laws with cutoffs that are comparable to those typically observed in experiments . The mechanism of generating apparent criticality by time-varying external fields , forces or input may generalize to many other systems like dynamics of swarms , diseases or extinction cascades . Here , we illustrate the analytically derived effects for spike recordings in vivo and discuss approaches to distinguish true from apparent criticality . Ultimately , this requires causal interventions , which allow separating internal system properties from externally imposed ones .
In the quest to understand the principles that govern collective neural dynamics , it has been proposed that brains operate at or near criticality [1–5] , i . e . , a dynamical state that arises at second-order phase transitions and is characterized by scale-invariant activity cascades or avalanches . Criticality is an important candidate state for brain function , because in models criticality optimizes information processing capacities [6–9] . Since the expected power law distributions for avalanches have been found for neural activity on many scales – from spiking activity in vitro [10–12] to local field potential , EEG , MEG and BOLD signals in humans [13–18] – these power laws are taken as evidence that the brain does indeed operate at criticality . However , it is known that power laws can also be generated by alternative mechanisms [19] . Most of those mechanisms do not map naturally onto neural networks and are therefore not plausible . However , here we identify a particular mechanism , namely , time-varying changes in the strength of an external drive , as a potential candidate to generate approximate power law scaling in the absence of criticality . Specifically , we investigate the hypothesis that a generic model of neural network dynamics , implemented by an inhomogeneous Poisson process ( IPP ) , can give rise to power law avalanche size and duration distributions . In the following sections , we outline the conditions under which approximate power law scaling for avalanches arises from IPPs . Specifically , we first derive analytically the duration and size distributions for a homogeneous Poisson process ( HPP ) and show that they follow ( approximate ) exponential distributions , with rate-dependent decay constants . Subsequently , we derive the known result that superposition , i . e . , a weighted summation , of such exponential distributions with different decay constants could , in theory , lead to power laws with any exponent . However , this mechanism does not apply to neural activity , because the weighting function of the rates that is required for a perfect power law cannot be normalized . Hence , this mechanism can generate only approximate power laws with cutoffs . Finally , we show how these approximate power laws can be generated by IPPs and how they resemble avalanche distributions that are typically observed experimentally . Thus , they can , in principle , be mistaken as evidence for criticality . This paper focuses on the conditions leading to power law distributions from Poisson activity , but power laws form only one marker for criticality . To distinguish apparent criticality from true criticality , it is advisable to extend the criticality analysis beyond power laws . By applying additional measures and by studying the impact of the temporal scale ( bin size ) , many types of IPP can be distinguished from critical processes . In Section 3 . 3 , we also present a number of measures that aid in distinguishing apparent criticality from true criticality , in the hope that this overview will serve as a guide for future rigorous analysis of critical systems . However , it is necessary to bear in mind that because of the correlative nature of any data analysis , a very sophisticated external drive ( i . e . , very specific IPPs ) could perfectly mimic the neural activity of critical systems . Thus , ultimately , the distinction between criticality and apparent criticality can be achieved only by causal interventions that probe the internal system dynamics and disentangle it from the impact of some hidden drive . This idea not only holds for the analysis of critical systems but also points to the fundamental limitation of correlative system analysis in general , which can be overcome only by causal intervention . Nonetheless , even without causal intervention , analyses that go beyond the standard set of avalanche measures can increase the confidence that a particular system is critical .
In this section , we review the avalanche analysis , discuss the impact of the bin size parameter , and then derive analytically the duration and size distributions for an HPP . We also derive or review other measures , including the avalanche shape , the scaling of the shape with duration , the inter-event/avalanche distributions , the spike-count ratio or branching parameter Q , the power spectrum and the Fano factor . For an HPP , it is commonly assumed that the avalanche measures are exponential and not power law distributed . We show analytically that the duration distribution , PD ( d ) , is indeed exponential , but the expression for the size distribution , PS ( s ) , deviates from the exponential assumption . In the main text we provide the results together with the outline of the derivation , and the full analytical derivations are detailed in the Methods section . Avalanche duration distribution PD ( d ) . Consider an HPP with rate r and bin size Δt = 1 time step . The avalanche duration d is defined as the number of non-empty bins in a sequence . The probability of a bin being empty is p0 = e−r , and the probability of a non-empty bin is thus p = 1 − p0 = 1 − e−r . Because the events in different time bins are independent , the probability of obtaining a sequence of d non-empty bins between two empty bins is proportional to pdp02 . This gives PD ( d ) ~ ( 1 − e−r ) d = e−μ ( r ) d , where μ ( r ) = −ln ( 1 − e−r ) is the rate-dependent decay constant of the exponential . Thus , the avalanche durations are exponentially distributed , and the distributions become flatter as r increases ( Fig 2B ) . The normalized distribution is given by ( see Methods ) : PD ( d ) =1ZDp02pd=e−rd ( er−1 ) d−1 ( 1 ) The above results hold for any rate ( or equivalently for any bin size ) . For the widely used bin size of one “average inter-event interval , ” Δt = 〈IEI〉 = 1/r , the duration distribution is independent of the rate r and simplifies to: PD ( d|Δt=1/r ) =1e−1 ( e−1e ) d ( 2 ) Avalanche size distribution PS ( s ) . The derivation of the avalanche size distribution PS ( s ) is more intricate than the derivation of PD ( d ) ( see Methods for full details ) . The first step involves obtaining an expression for the conditional size distribution , PS ( s|d ) . This requires knowing the probability of having A = a events in a bin , which is given by the Poisson distribution , PA≥0 ( a ) =rae−ra ! . However , within an avalanche , all bins have a ≥ 1 events , and therefore the probability must be renormalized , yielding: PA≥1 ( a ) =PA≥0 ( a ) 1−po=ra ( er−1 ) a ! ( 3 ) The size of each avalanche is then the sum of the events from all the bins that constitute it , namely , the sum of d independent random variables A . The conditional size distribution PS ( s|d ) can be derived from the corresponding probability-generating function ( see Methods ) . The resulting expression involves Stirling numbers of the second kind , {sd} , which represent the number of ways to distribute s events into d bins such that none of the bins is empty ( the number of surjections from s to d ) : PS ( s|d ) =rsd ! {sd}s ! ( er−1 ) d=rs[∑i=0d ( −1 ) i ( di ) ( d−i ) s]s ! ( er−1 ) d ( 4 ) In the second step , PS ( s|d ) is combined with PD ( d ) to yield the size distribution: PS ( s ) =∑d=1sPS ( s|d ) PD ( d ) =∑d=1srs[∑i=0d ( −1 ) i ( di ) ( d−i ) s] ( er−1 ) d−1s ! ( er−1 ) derd=rss ! 1 ( er−1 ) ∑d=1se−rd∑i=0d ( −1 ) i ( di ) ( d−i ) s ( 5 ) This distribution is not exponential and does not resemble a power law ( Fig 2A ) . Note that it does also not necessarily decrease monotonically with s . In fact , for large enough rates , r>2 , PS ( s ) shows a global maximum at s>1 . However , the tail of the distribution approximates an exponential ( see Methods ) . More precisely , for large s the distribution can be approximated by: PS ( s ) ≈λe−λs , ( 6 ) where λ is a function of r λ ( r , sc ) =lims→∞−logPS ( s+1 ) PS ( s ) =−logr−1+B ( sc ) ( 7 ) B ( sc ) accounts for the slow change of λ with s and is evaluated for a representative sc ( see Methods ) . Thus , in contrast to the duration distribution , the size distribution is not exponential and is not necessarily monotonic . In this section we derive or review additional common time series measures for the HPP . All results are shown in Fig 3A . Avalanches shape . In critical systems , the avalanche shape is expected to be "universal , " i . e . , the characteristic shape Fu ( t/d ) of the avalanche scales with the duration d of the avalanche F ( t , d ) = Fu ( t/d ) dν [23 , 24] . This relationship implies that the average avalanche size s¯ also scales with d . For homogeneous Poisson processes , the shape is flat , because the expected number of events per bin of size 1 is simply r . The expected size s¯ for a given duration is thus: s¯ ( d ) =d⋅r ( 8 ) Thus , s¯ ( d ) follows a trivial power law with slope ν = 1 , or , more simply , s¯ is proportional to d ( Fig 3A ) . For certain critical systems , specific relations between the exponents of PS ( s ) , PD ( d ) , and s¯ ( d ) have been predicted [23 , 24] . However , for HPPs neither PS ( s ) nor PD ( d ) follows a power law , and thus the scaling relationships are not applicable . Inter-event and inter-avalanche-interval distributions . The inter-avalanche-interval ( IAI ) distribution is closely related to the IEI distribution of A ( t ) , that is the IEI is calculated from taking all events together . More precisely , the IAI distribution is a left-truncated version of the IEI distribution , where the truncation is determined by the bin size . In other words , all IEIs that are smaller than Δt do not contribute to P ( IAI ) , whereas for all IEI or IAI > 2Δt , the counts for IEI and IAI are exactly equal . As P ( IEI ) is the more general distribution , we report only P ( IEI ) here . Analytically , P ( IEI ) is the inter-event distribution of a Poisson process P ( IEI ) =re−rIEI ( 9 ) and follows an exponential ( Fig 3A ) . Note that even under very high rates ( r ≫ 1 ) , there is still a non-zero probability of obtaining empty bins . This allows parsing the process into avalanches . Fano factor F . The Fano factor is the mean-normalized variance of a process and for Poisson processes F=1 , independently of the bin size ( Fig 3A ) . Event or spike count ratio Q . The spike count ratio ( or branching parameter ) , Q , is defined as the expected value of activity in one bin divided by the activity in the previous bin , Q ( Δt ) =⟨A ( t+1|Δt ) A ( t|Δt ) ⟩ , and the expectation is taken over all bin pairs with A ( t|Δt ) ≥ 1 [4] . For HPPs , Q can be derived analytically . Q changes with Δt , and , as before , the dependence on Δt is equal to that on r , i . e . , Q ( Δt = z|r = 1 ) = Q ( r = z|Δt = 1 ) . The analytical expression for Q for Poisson processes is derived in the Methods . It yields: Q ( Δt ) =Δt ( ln ( Δt ) +γ−Ei ( Δt ) ) 1−eΔt ( 10 ) where Ei ( Δt ) is the exponential integral , and γ is the Euler-Mascheroni constant ( γ ≈ 0 . 577 ) [25] . The spike count ratio Q ( Δt ) increases for small Δt , equals unity for Δt ≈ 1 . 5 ∙ 〈IEI〉 , assumes a maximum at Δt ≈ 3 . 75 ∙ 〈IEI〉 , and finally approaches unity from above for Δt → ∞ ( Fig 3A ) . Fourier spectrum . Finally , the Fourier spectrum of a Poisson process is known to be flat ( Fig 3A , bottom panel ) . As derived above , the durations and size distributions of HPPs are ( approximately ) exponential . The decay constant of the exponentials depends on the rate of the process , r . This dependence is the key to obtaining approximate power law distributions from IPPs , via superimposing multiple exponential distributions , which are each generated by periods of activity with different rates . Mathematically , it is known that specific superpositions ( i . e . , weighted sums ) of exponential functions lead to power laws . In this section , we review the general conditions under which such a superposition can lead to a power law with a given exponent . We then translate these conditions to neural activity with a time varying rate ( IPP ) and show that a perfect power law cannot be obtained . However , superposition of a few exponentials can result in approximate power law distributions , spanning a few orders of magnitude . To obtain a perfect power law P ( x ) ~ x−α from the superposition of exponentials , the weighting function w ( λ ) for each decay rate λ must fulfill the following condition: P ( x ) =∫λ1λ2dλw ( λ ) e−λx∼x−α ( 11 ) Note that here , for the sake of clarity , generic exponential functions e−λx are first used; later we replace them with the full avalanche duration distributions of HPPs . To obtain a power law without a cutoff , the bounds of the integral have to extend over the entire interval λ1 = 0 to λ2 → ∞ . Otherwise , the range of the power law distribution is limited on the right or left , respectively . The weighting function that results in a power law is a power law in itself: w ( λ ) ~λα−1 ( see Methods ) . Γ is the gamma function , Zp is the normalization , and α > 1 to allow normalization of the power law . However , w ( λ ) ~λα−1 cannot be normalized for α ≥ 0 , i . e . , the probabilities w ( λ ) with which each exponential e−λx would contribute to the power law are undefined . As a consequence , real-world systems cannot generate a perfect power law from addition ( superposition ) of exponentials . However , the weights can be normalized by choosing a reduced integration range [λ1 , λ2] at the cost of obtaining only an approximate power law with cutoffs . This approach is used below to study avalanche distributions generated by an IPP . To achieve this goal , we need first to translate the general relation for P ( x ) above to the specific cases of the duration distribution PD ( d ) ; in particular , we need to derive the specific weight function w ( r ) – instead of the generic function w ( λ ) – that gives rise to a power law for PD ( d ) ~d−β . The density or weighting function w ( r ) denotes the fraction of time that an IPP has to assume each rate r ( and hence sample from the respective exponential distribution ) , so that a power law is obtained across the full IPP . We assume that the IPP rate changes far more slowly than the typical duration of an avalanche . We can thus assume that an IPP takes a fixed rate r for some time window . During each time window , the duration distribution is PD ( d|r ) , as derived above for fixed rates ( HPP , see Eqs ( 1 ) and ( 2 ) ) . The resulting PD ( d ) of the IPP can be written as: PD ( d ) =∫0∞drw ( r ) ρ ( r ) PD ( d|r ) ZD∼d−β ( 13 ) where ZD is the appropriate normalization , and ρ ( r ) is the rate at which avalanches occur given a Poisson rate r . This equation holds , in analogy to the argument above , if all the factors in front of the exponential in PD ( d|r ) are proportional to rβ−1 . This condition yields the general expression for w ( r ) ( see Methods ) : w ( r ) = ( −log ( 1−e−r ) ) β−1 ( 1−e−r ) e−r ( 14 ) To obtain , for example , PD ( d ) ~ d−2 , which is characteristic for critical branching processes , the weighting function is: w ( r|β=2 ) =−log ( 1−e−r ) ( 1−e−r ) e−r ( 15 ) This function is approximately 1/r for r ≪ 1 and approximately constant for r>1 ( Fig 4A , black dashed line ) . Importantly , this implies that for low rates the number of events that each rate contributes is invariant: w ( r ) r ~ 1/r ∙ r = const . , whereas for large rates , each rate contributes an equal fraction of time ( w ( r ) = const . ) , and hence larger rates contribute more events ( ~ r ) . However , it immediately becomes clear that this weighting function cannot be normalized over the full range of rates from zero to infinity . Nonetheless , w ( r ) can be normalized if a limited integration range [r1 , r2] is chosen , albeit at the cost of introducing a right and left cutoff to the power law of PD ( d ) , respectively . For the numerical illustration in Fig 4 , we chose the range [r1 = 0 . 01 , r2 = 5] and sampled 300 values from w ( r ) in this range . For the analytical results , the functional form of the cutoffs can be obtained as follows ( see Methods ) : PD ( d ) ∼d−β ( γ ( β , −dlog ( 1−e−r1 ) ) −γ ( β , −dlog ( 1−e−r2 ) ) ) ≔d−β⋅Δγ ( β , r1 , r2 ) ( 16 ) where γ ( ⋅ , ⋅ ) is the lower incomplete gamma function . The terms γ ( ⋅ , ⋅ ) generate smooth cutoffs on both sides of the power law d−β by “windowing” it . The windowing function Δγ = Δγ ( β = 2 , r1 = 0 . 01 , r2 = 5 ) is depicted in Fig 4C for different Δt ( or r ) . With increasing bin size ( or , equally , with increasing rate ) it moves to larger avalanche durations d ( i . e . , to the right ) . Likewise , the cutoffs of the resulting PD ( d ) move from left to right ( Fig 4B ) . The right cutoff is thus prominent at small bin sizes ( Δt < 1 ) , whereas the left cutoff sets in at large bin sizes ( Δt > 1 ) . For Δt = 1 , this example IPP shows a power law that extends over more than two orders of magnitude . In branching processes , the characteristic exponent for the duration distribution is -2 , whereas for the size distributions it is -1 . 5 . Interestingly , we obtained the same pair of exponents for IPPs by naively applying to the size distributions PS ( s|r ) the exact weight function w ( r|β = 2 ) that we had derived for PD ( d ) . Thus , by construction , for an IPP that gives rise to PD ( d ) ≈ d−2/ZD ( with cutoff ) , the corresponding size distribution shows a power law with PS ( s ) ≈ s−1 . 5/ZS when applying a bin size of Δt = 1 ( Fig 4D ) . Avalanches extracted from this IPP can thus easily be taken as evidence for criticality . In summary , Poisson neurons with slowly changing finite rates can give rise to approximate power laws with the characteristic exponents -1 . 5 and -2 for the sizes and durations , respectively , if the different rates occur with probability w ( r|β = 2 ) as derived above . In practice , the generation of a power law from superimposed exponentials can be realized only with a cutoff and requires the weighted contribution of each exponential according to w ( r|β ) . As shown above , IPPs can give rise to approximate power laws with a cutoff if their rates change slowly and if they are distributed according to w ( r|β ) on an interval [r1 , r2] . In this section , we show that the rate distribution does not have to be exactly w ( r|β ) to generate distributions that resemble those obtained from experimental results . However , IPPs and truly critical processes typically differ in other measures . This differentiation allows us to distinguish apparent critical systems from truly critical systems , as described below . Consider an IPP that assumes one of four equiprobable rates {r1 , r2 , r3 , r4} = {0 . 1 , 0 . 2 , 0 . 5 , 1}/Z . The normalization Z = 5/9 assures 〈r〉 = 1 , without loss of generality . Each rate is maintained for a long time window compared to the typical avalanche duration—here for 250 , 000 time steps ( ≈ 4 min , assuming a sampling rate of 1 kHz ) . While each interval separately shows ( approximately ) exponential avalanche distributions , combining all epochs results in an approximate power law with a cutoff at around s = 80 , thus covering almost two orders of magnitude for PS ( s ) and PD ( d ) and also for P ( IEI ) , the inter-event interval distribution ( Fig 3B , same parameters as Fig 1 ) . Thus , avalanche distributions from this simple non-stationary Poisson process could easily be taken as evidence for criticality , especially since the exponents match those of a critical branching process ( -1 . 5 and -2 for the size and rate distribution , respectively , Fig 3B ) . Measures other than avalanche distributions , however , show clear differences between this inhomogeneous Poisson activity ( IPP ) and a critical branching process ( compare Fig 3B and 3C , respectively ) , as follows: ( i ) The relationship between mean avalanche size and duration , s¯ ( d ) , exhibits an almost perfect power law for the IPP , but not with the exponent of -2 that is expected from the exponents of the size and duration distributions [23 , 24] . Instead it shows the trivial exponent of unity , i . e . , s¯ ( d ) ~ d . ( ii ) The inter-event interval distribution P ( IEI ) is flat for the branching process but constitutes a sum of exponentials , approximating a power law , for the IPP . ( iii ) The density of events PA ( a ) approximates a power law for the branching process but not for the IPP . ( iv ) The estimated branching ratio or spike count ratio Q ( Δt ) shows a pronounced maximum for the branching process ( maxΔt Q ( Δt ) ≈ 500 ) , whereas for the IPP Q ( Δt ) is close to unity for all Δt ≥ 1 . ( v ) The Fano factor takes much higher values around Δt = 1 in the branching process than in the IPP ( note the different y-axis ranges ) . ( vi ) The Fourier spectrum of the population activity A ( t ) shows a power law spanning more than two orders of magnitude for the branching process , whereas it is flat for the IPP . ( vii ) Finally , PS ( s ) and PD ( d ) for the IPP change markedly with Δt , as predicted analytically , whereas for the branching process they are almost invariant against moderate changes in Δt ( Fig 2C–2F ) . This is because the critical branching process , despite having exactly the same average rate as the IPP , shows a moderate separation of time scales ( see Discussion ) .
We have shown that it is not possible to generate a perfect power law for avalanches with an IPP , whereas approximate power laws , extending over several orders of magnitude before cutoff , can be generated by assuming that the rates vary over time across only one or two orders of magnitude . Our findings thus indicate that power law distributions for avalanches may also appear in non-critical systems , given a specific time-varying external drive . For many types of input , an analysis that extends beyond avalanches alone can rule out or provide evidence for the criticality hypothesis . However , for certain types of input ( in particular r ( t ) of the IPP mimicking exactly the A ( t ) generated by a true critical system ) , "passive" data analysis , from avalanche size through Fourier spectrum to approaches from equilibrium thermodynamics [26] , cannot distinguish between them . The distinction between a critical-like driven system and a truly critical system ultimately requires manipulation , i . e . , the use of “active” causal interventions . Application , for example , of small , controlled perturbations can separate the intrinsic network properties from those imposed by the external input . In critical systems , these perturbations cause avalanches that should follow the predicted power-law distributions . Alternatively , manipulations could directly target the control parameter of the system and assess its impact on the correlation length , susceptibility and specific heat [8 , 27 , 28] . Thereby one can establish a second-order phase transition . While such manipulations may be feasible in models , not all experimental preparations allow for well-controlled manipulations , and alternatively manipulating a maximum entropy model fitted to the data may yield spurious results [29] . If direct manipulations cannot be applied , data analyses should make use of the diversity of measures available , including investigating the effects of different temporal bin sizes . Such combined analyses can distinguish between many types of rate-varying drives and truly critical systems . However , again , analyses without manipulations are not sufficient to distinguish between a drive that perfectly mimics the 1/f envelope expected for critical systems and 1/f dynamics generated because of criticality within a network . At the core of these considerations is the fundamental issue of correlative versus causal studies of the underlying system . In general , correlative approaches can be "fooled , " and thus the more rigorous , causal analysis is advisable . We have discussed here the superposition of exponentials as a potential alternative mechanism to criticality that may underlie power law generation . A number of other alternative mechanisms have been proposed , four of them compiled by Newman [19] , namely , a combination of exponentials , inverses of quantities , random walks , and the Yule process . There are basically two reasons why it is not possible for these alternative models to explain the power laws observed for neuronal avalanches: either the experimentally observed distributions do not agree with the model functions ( e . g . , the Yule process shows a power law tail , whereas neuronal avalanches show cutoffs; random walks show an exponent of -2 , whereas for avalanche sizes it is typically -1 . 5 ) , or it is not clear how the generating mechanism would map onto neural networks ( all four examples ) . In contrast , the branching process offers an elegant mechanistic approximation of spike propagation on a network and exhibits the same avalanche distributions as those observed in data [4 , 30 , 31] . Schwab et al . [32] and Aitchison et al . [33] have shown that power laws for pattern frequency , i . e . , Zipf's law , can emerge from a random external input or field . Their studies are similar to ours in that they used a varying external input , in effect , potentially also leading to a superposition of exponentials . However , avalanches – in contrast to Zipf patterns – are temporally extended , and thus the random external field is not sufficient to generate power law avalanche distributions . The spatio-temporal characteristics of avalanches require a temporally correlated external field . The effects of such a temporally correlated external field have been studied by Touboule & Destexhe [34] . They , in analogy to our study , applied a time-varying external field r ( t ) to all Poisson neurons . They chose one specific r ( t ) , namely , an Ornstein–Uhlenbeck ( OU ) process , which they realized with a long correlation time τ compared to the bin size Δt of the avalanche analysis . ( They chose τ = 1/α = 1 at simulation steps Δt = 0 . 0001; this corresponds to τ’ =104 at Δt′ = 1 , and implies a very small distance to criticality α′ = 10−4 ) . Thereby , the OU process introduces correlations among neurons and in time , and the resulting avalanche distributions display power laws with a cutoff . Overall , this choice of parameters makes the OU process more similar to our critical branching process than to a HPP [35] . Time varying external input may induce additional correlations not only for neural systems , but also in other collective systems , like the dynamics of flocks , which are subject to wind fluctuations and time varying external cues , or the dynamics of disease propagation that can be influenced seasonally , by weather conditions and by travel patterns . For all such systems , careful analyses are required to disentangle the external input from the internally generated dynamics . A classic example is that of solar flares , which evolve in cycles . Their inter event intervals ( IEI ) show a heavy tailed distribution . The generation of the heavy tail is derived from superposition of exponential distributions arising from different event rates [36 , 37] , in analogy to the derivations here ( Fig 3B ) . For the generation of power laws from IPPs , we assumed that some external mechanism , the drive , makes the Poisson neurons fire with a fixed rate for a certain time interval , and then with a different rate for another time interval . For the simulations , the changes in r were assumed to be abrupt to allow for analytical treatment . However , the rate changes can also be slow and continuous . The important constraint is that the rates change slowly compared to the duration of an avalanche . In past studies , avalanches typically lasted a few milliseconds or tens of milliseconds ( depending on the rate and bin size ) [4 , 12 , 13 , 24 , 30] . Thus , any change in r of seconds can be considered “slow . ” If the rate changed on very fast time scales , much shorter than typical avalanche durations , then the process would resemble an HPP with regard to the avalanche analysis . An example of a slowly varying drive is depicted in Fig 5 , where we simulated a simple time-varying input , specifically , a sinusoidal with mean rate 1 , amplitude 1 , and a slow period of about four minutes . With this naïve choice of parameters , the avalanche size distribution approximated a power law with an exponent of -1 . 5 over three orders of magnitude ( Fig 5A ) , and the numerical and analytical results still showed a good match ( Fig 5B ) . A power law could also arise from combining avalanche distributions from different experiments that differ in the mean event rate . Each recording might show an exponential distribution , but as the rates differ , the decay rates of the exponentials would differ , and adding them could yield approximate power law scaling . This effect is illustrated in Fig 6 , where avalanche size distributions from 12 spike recording sessions in macaque monkeys were plotted both individually ( gray ) and in a combined manner ( red ) . The data sets are precisely the same as those in [30 , 35] . The size distribution P ( s ) does not approximate a power law for any of the individual experiments , but combining the data from all twelve recording sessions yields a power law extending over more than two orders of magnitude . This is because each recording shows a different population spike rate , which translates to diverse decay behavior of P ( s ) . Thus , it is evident that avalanche distributions from different experiments should not be combined into distributions by simple averaging . In contrast , an experiment in which the rate diversity lies in the Poisson neurons does not yield approximate power laws: If each neuron spikes with a different , constant Poisson rate , then the overall process is again an HPP with a firing rate equal to the sum of the individual rates . Our current study of neural network dynamics using purely phenomenological models led us to ask: What can be achieved by using simple reduced models ? We show here that such models offer an alternative explanation for power law generation: Instead of arising from critical networks , power laws can be imposed by a sophisticated drive with long time scales and large rate variations onto a set of unconnected Poisson neurons . Is this a better model for neural population dynamics ? In terms of biophysical plausibility , certainly not: Single neuron dynamics are more complex than assumed here , and there is an abundance of connections between neurons and these connections are certainly used . Nonetheless , the phenomenological model allowed to disentangle the neural network dynamics generated within a network , and that imposed by external drive or input . A combination of the two determines the resulting population dynamics . Here we focused on the role of the external drive . Long time scales have been observed in many studies ( e . g . , [2 , 4 , 38] ) . One argument for their emergence from within the network , and not from the external world , is that evidence for criticality has been found in isolated systems: in vitro networks clearly lack an external input but show evidence of internally generated criticality [4 , 10 , 11 , 24 , 39] . In vivo evidence for critical dynamics has also been provided for states with reduced input from the outside world , i . e . , anesthesia and sleep in both animals and people [12 , 13 , 30] . In such a scenario , the long time scales could be imposed by input from a different part of the brain than the one recorded from , but these , in turn , need to generate the long time scales themselves . Thus , at least some brain areas need to generate the long time scales , e . g . , by being close to criticality . In other words , the problem of generating long time scales is shifted only to a different entity than the one investigated , without solving the question about the origin of the long correlations . Importantly , the emergence of long time scales – indicative of near critical dynamics – has also been predicted in a detailed hierarchical model of the primate cortex [40] . A property of critical systems ( with finite rate r ) is the separation of timescales ( STS ) . The STS imposes that the duration of an avalanche is typically much shorter than the pauses between avalanches . Assuming a certain rate r , a STS emerges in branching processes when approaching criticality . This is because the population rate r and the external input h obey the relation r = h/ ( 1 − σ ) = h/ϵ , where ϵ is the distance to the critical point . When approaching criticality ( ϵ → 0 ) , the drive rate h has to approach zero to assure a finite rate . Sufficiently close to criticality , the finite rate together with the diverging variance of the activity typically leads to long waiting times before a new avalanche is started and hence to a STS [35] . A STS implies that the avalanche size and duration do not change ( much ) when the bin size is changed , on condition that the bin size is shorter than the typical pauses ( Fig 2E and 2F ) . In experimental data , the waiting times or inter avalanche intervals ( IAI ) , which are closely related to the inter event intervals ( IEI ) across all events , can reveal the nature of the external drive . For branching processes with Poisson drive , p ( IEI ) is exponentially distributed ( Fig 3C ) . Different drives , however , would induce different IEI distributions . For the IPPs , for example , p ( IEI ) can resemble a power law ( Fig 3B ) , or a Gamma distribution [36 , 37] . In experiments , both , approximate power laws [13 , 41 , 42] , as well as exponential or gamma-like distributions [43 , 44] were observed . Thus the presence of a power-law distributed p ( IEI ) cannot prove a critical state , and the absence cannot rule out criticality . Similarly , temporal correlations between avalanche sizes have been observed in experiments and in some critical models , but not in all . Thus these correlations can narrow down the classes of generating models , but do not necessarily imply that the system is not critical . Inference about the collective dynamics of a network in extended networks is further complicated if only a small fraction of all neurons can be sampled , or alternatively if one has to resort to coarse measures of neural activity such as LFP , EEG or MEG ( coarse sampling ) [35 , 39 , 43–45] . Currently , neural recordings in vivo are constrained by either subsampling or coarse sampling , and the biases that are potentially induced by sampling should be treated with care in any data analysis project . While no panacea exists to date to overcome these limitations , incorporating subsampling or coarse sampling to models , when comparing them to neural activity obtained from experiments is highly advisable . In fact , subsampling effects are already being implemented on a regular basis [13 , 14 , 30 , 35 , 43–46] . Recent advances have even provided an analytical understanding of subsampling-induced biases , which now allows us to correctly infer aggregated properties of a full system from an observed subset [35 , 39 , 47] . In conclusion , a non-critical system that is externally driven by a time-varying input can give rise to power law avalanche distributions resembling empirical distributions . The main requirements are that the rate envelope of the external drive changes sufficiently slowly in time , that it spans a wide enough range of rates , and that each rate contributes approximately for the correct fraction of time , given by w ( r ) . An important question concerns the general mechanisms that could give rise to such slowly varying temporal envelops . Ironically , one potential general mechanism is critical dynamics , which exhibits slow time scales . In other words , a system of non-interacting or weakly-interacting elements that are driven by a critical system may be indistinguishable from a genuine critical system . Thus , from the point of view of Occam's razor , it may well be that an underlying critical system is still the most parsimonious explanation of the data .
The experiments were performed according to the German Law for the Protection of Experimental Animals and were approved by the Regierungspräsidium Darmstadt . The procedures also conformed to the regulations issued by the NIH and the Society for Neuroscience . The recordings were used in earlier publications already [30 , 35 , 48] . The recording sessions are the same as in Priesemann et al . [30] and in Wilting & Priesemann [35] . The relevant details can be found in those articles and in the original publication of Pipa et al . [48] . In brief , spikes were recorded simultaneously from up to 16 single-ended micro-electrodes or tetrodes in the lateral prefrontal cortex of each of three trained macaque monkeys . For each recording , avalanches were extracted as described below , using a bin size of Δt = 4 ms . In this study , we did not acquire new data but re-used data that had previously been recorded for different purposes . All relevant data are presented in this paper and in the Supporting Information files . Below we briefly review the definitions of avalanche measures and other time series measures . All definitions follow the standard definitions in the field . Most measures depend on the bin size Δt , and hence Δt introduces the relevant time scale for the time series . To define avalanches , events of all recorded units are combined into a single time series A ( t ) , which describes the instantaneous population rate ( Fig 7 ) . To segment this time series into avalanches , temporal binning is applied . An avalanche is thus defined as a sequence of non-empty time bins , preceded and followed by at least one empty bin [4] . The avalanche size s is the total number of events in the avalanche , and the avalanche duration d is the number of non-empty bins in the sequence . Both quantities are expected to follow power law distributions with characteristic exponents if a system is critical [4 , 54] . The average avalanche size s¯ given a duration d is denoted by s¯ ( d ) . The inter-event intervals ( IEI ) are defined as the time differences between subsequent events in the population rate vector A ( t ) . The probability of observing A = a events in a time bin is denoted by PA ( a|Δt ) or simply by PA ( a ) . The Fano factor F is defined as the variance of the binned signal , divided by the mean . Both A and F depend on the bin size . Finally , the spike count ratio , Q , is defined as the ratio of events in the ith bin , A ( t = i|Δt ) , and the previous bin , A ( t = i − 1|Δt ) , averaged over all bins with A ( t = i − 1|Δt ) > 0: Q=Q ( Δt ) =⟨A ( i|Δt ) A ( i−1|Δt ) ⟩ . ( 18 ) The measure Q is equivalent to the so-called “branching parameter” in Beggs & Plenz ( 2003 ) and subsequent studies; however , since the measure does not necessarily return the “branching parameter” of a branching process [25 , 30] , we opted to give it a different name to avoid confusion .
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The analysis of complex systems in nature introduces several challenges , because typically a number of parameters either remain unobserved or cannot be controlled . In particular , it can be challenging to disentangle the dynamics generated within the system from that imposed by the environment . With this difficulty in mind , we reinvestigate the popular hypothesis that neural dynamics is poised close to a critical point . Criticality is characterized by power-law scaling and has been linked to favorable computational properties of networks . Power-law distributions for “neural avalanches , ” i . e . , spatio-temporal clusters of neural activity , have been observed in various neural systems and support the criticality hypothesis . Here we show that approximate power laws do not necessarily reflect critical network dynamics but can be imposed externally on non-critical networks , i . e . , by driving the network with input of specific statistics . We derive these results analytically and illustrate them both in simulations and using neural recordings . The findings indicate that more caution and additional tests are required for distinguishing between genuine and apparent criticality . Ultimately , this requires causal interventions , not only in neural systems , but in many other complex dynamical systems that are subject to time-varying external forces , such as the dynamics of swarms , diseases or extinction cascades .
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2018
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Can a time varying external drive give rise to apparent criticality in neural systems?
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There is considerable debate on the health impacts of soil-transmitted helminth infections . We assessed effects of deworming on physical fitness and strength of children in an area in Yunnan , People's Republic of China , where soil-transmitted helminthiasis is highly endemic . The double-blind , randomized , placebo-controlled trial was conducted between October 2011 and May 2012 . Children , aged 9–12 years , were treated with either triple-dose albendazole or placebo , and monitored for 6 months post-treatment . The Kato-Katz and Baermann techniques were used for the diagnosis of soil-transmitted helminth infections . Physical fitness was assessed with a 20-m shuttle run test , where the maximum aerobic capacity within 1 min of exhaustive exercise ( VO2 max estimate ) and the number of 20-m laps completed were recorded . Physical strength was determined with grip strength and standing broad jump tests . Body height and weight , the sum of skinfolds , and hemoglobin levels were recorded as secondary outcomes . Children receiving triple-dose albendazole scored slightly higher in the primary and secondary outcomes than placebo recipients , but the difference lacked statistical significance . Trichuris trichiura-infected children had 1 . 6 ml kg−1 min−1 ( P = 0 . 02 ) less increase in their VO2 max estimate and completed 4 . 6 ( P = 0 . 04 ) fewer 20-m laps than at baseline compared to non-infected peers . Similar trends were detected in the VO2 max estimate and grip strength of children infected with hookworm and Ascaris lumbricoides , respectively . In addition , the increase in the VO2 max estimate from baseline was consistently higher in children with low-intensity T . trichiura and hookworm infections than in their peers with high-intensity infections of all soil-transmitted helminths ( range: 1 . 9–2 . 1 ml kg−1 min−1; all P<0 . 05 ) . We found no strong evidence for significant improvements in physical fitness and anthropometric indicators due to deworming over a 6-month follow-up period . However , the negative effect of T . trichiura infections on physical fitness warrants further investigation .
Soil-transmitted helminths , namely Ascaris lumbricoides , Trichuris trichiura , and the hookworms ( Ancylostoma duodenale and Necator americanus ) , are the most common parasitic worm infections of humans . Indeed , more than 1 billion people are infected and approximately 5 . 4 billion people are at risk of infection [1]–[3] . In 2011 , an estimated 875 million children , 70% of whom were school-aged , were at risk globally [4] . Impoverished communities with poor hygiene and no access to clean water and improved sanitation are especially vulnerable [5] , [6] . The global burden of soil-transmitted helminthiasis is currently estimated at 5 . 2 million disability-adjusted life years ( DALYs ) , mainly due to sub-clinical morbidities , but also anemia and reduced cognitive and physical development [7]–[9] . Infections are largely chronic and usually asymptomatic , and hence the study and quantification of the morbidity associated with soil-transmitted helminth infections are difficult , and only few studies have ventured to do so . In particular , no conclusive evidence has yet been established whether reduced physical fitness or strength are a consequence of soil-transmitted helminth infections . Physical fitness has been positively correlated with academic performance through enhanced memory and attention [10] , [11] , while physical strength is demanded in labor-intensive agriculture jobs , which often provide the main source of income in rural communities of the developing world [12] . A lack in both attributes due to soil-transmitted helminthiasis could arguably prevent school-aged children living in impoverished conditions from realizing their full potential and perpetuate their entrapment in the vicious cycle of poverty and poor health [13] , [14] . Based on the rationale that lowering infection intensity would help to control morbidity associated with chronic helminth infection , and that morbidity is infection intensity-dependent , the World Health Organization ( WHO ) advocates periodic deworming of at-risk populations ( e . g . , school-aged children and pregnant women ) with single-dose albendazole ( 400 mg ) or mebendazole ( 500 mg ) [15] , [16] . Such an approach indeed reduces infection intensity in the target population , but high-quality evidence on the health benefits of de-worming in children is scant [17] , [18] . Two randomized controlled trials have shown that physical fitness in school boys infected with soil-transmitted helminths improved 7 weeks to 4 months after treatment with single-dose albendazole [19] , [20] . Physical fitness was also negatively correlated with T . trichiura and hookworm infections in two cross-sectional studies [21] , [22] but another cross-sectional study did not find any correlation between physical fitness and soil-transmitted helminth infections [23] . However , it is important to note that in the latter study , both the prevalence and intensity of soil-transmitted helminth infections were very low . We designed a randomized controlled trial to investigate the health benefits of deworming and thereby deepen our understanding of the burden caused by soil-transmitted helminth infection among school-aged children . The study was conducted in a highly endemic area in the People's Republic of China ( P . R . China ) and assessed the effects of triple-dose albendazole on physical fitness and strength of initially soil-transmitted helminth-infected children . The infection and fitness dynamics were then studied over a 6-month period post-treatment . Changes in anthropometric indicators and hemoglobin levels were also measured , and are reported as secondary outcomes .
The study protocol was approved by the institutional research commission of the Swiss Tropical and Public Health Institute ( Basel , Switzerland ) . The ethics committee of Basel ( EKBB , reference no . 144/11 ) and the Academic Board of the National Institute of Parasitic Diseases , Chinese Center for Disease Control and Prevention ( Shanghai , P . R . China ) provided ethical clearance . The trial is registered with Current Controlled Trials ( identifier: ISRCTN 25371788 ) . The village doctor , chief , and teachers of each village were briefed on the aims of the study . With help from the teachers , the investigators further explained the procedures to the children and their parents/guardians . Written informed consent was obtained from parents/guardians , whereas children assented orally . Data were kept anonymous . After the 6-month final follow-up , all children attending the five schools were given triple-dose albendazole ( 3×400 mg ) irrespective of their infection status , study participation , and treatment during the study . Children diagnosed with Strongyloides stercoralis were offered a single dose of ivermectin ( 200 µg/kg ) . Participants were recruited from five primary schools , where a 70% or higher prevalence of soil-transmitted helminth infections had been detected during a rapid appraisal . All schools belonged to villages exclusively inhabited by the Bulang ethnic minority group , and were located in the mountainous Bulangshan township bordering Myanmar , a sub-division of Menghai county in Xishuangbanna Dai autonomous prefecture , situated in Yunnan province , P . R . China . The five villages are: ( i ) Sandui ( geographical coordinates: 21°33′07″N latitude , 100°19′34″E longitude , altitude: 1 , 566 m above sea level ( asl ) ) ; ( ii ) Kongkan ( 21°32′34″N , 100°20′25″E , 1 , 195 m asl ) ; ( iii ) Laozhai ( 21°31′37″N , 100°18′01″E , 1 , 399 m asl ) ; ( iv ) Laonandong ( 21°33′28″N , 100°21′45″E , 1 , 188 m asl ) ; and ( v ) Mannuo ( 21°33′27″N , 100°23′53″E , 1 , 352 m asl ) . Prior to the current trial , no survey or control activities targeting soil-transmitted helminthiasis had been implemented in the study villages . Detailed information on the study area has been published along with data on soil-transmitted helminth re-infection patterns among participants [24] . Moreover , the epidemiology and control of soil-transmitted helminthiasis in comparable Bulang communities previously studied by our group have been described elsewhere [25] , [26] . The study was designed as a double-blind , randomized , placebo-controlled trial with three follow-ups , and was carried out between October 2011 and May 2012 . Assuming a prevalence of 70% with any soil-transmitted helminth infection and 50% loss to follow-up , the trial aimed to enroll 250 children at baseline to achieve a power of 80% at an alpha error of 5% for the detection of a 2 . 5 ml kg−1 min−1 difference in the maximum aerobic capacity within 1 min of exhaustive exercise ( VO2 max estimate ) between the intervention and placebo groups . Inclusion criteria for the trial were: ( i ) provision of two stool samples at baseline; ( ii ) presence of at least one type of soil-transmitted helminth infection; ( iii ) no deworming treatment within 6 months before the current study; ( iv ) no known allergy to albendazole; ( v ) no major systemic illnesses as determined by a medical doctor; ( vi ) no concurrent participation in other clinical trials; ( vii ) residency in the study area for at least 1 year before enrolment; and ( viii ) participant should be between the age of 9 and 12 years . Children aged 9–12 years who met the inclusion criteria were enrolled by field investigators for a baseline assessment involving parasitological examination , physical fitness and strength tests , and anthropometric and hemoglobin measurements . The same measurements were repeated 1 , 4 , and 6 months after treatment , with the exception of anthropometric indicators that were only re-assessed at the 4- and 6-month follow-ups ( Figure 1 ) . The treatment allocation sequence was generated by a statistician using block randomization with randomly varying block sizes of 2 , 4 , and 6 . Albendazole and placebo tablets were packaged by staff not involved in the field work into sealed envelopes marked with unique identifiers . Following the order of the class list provided by the teachers , each child was sequentially assigned a random number , which corresponded to a number on the sealed envelopes . Both children and field investigators were blinded to the nature of the tablets . The assigned triple-dose treatment ( i . e . , 3×400 mg albendazole ( GlaxoSmithKline; London , United Kingdom ) or 3× shape- and color-matched placebo ( Fagron; Barsbüttel , Germany ) ) , was started on treatment day 1 with a single dose , with subsequent doses administered every day until treatment day 3 . The field investigators directly observed the consumption of each treatment by all children . Two stool samples were collected from each child on consecutive days . Both the Kato-Katz ( duplicate slides per sample ) and Baermann techniques ( one examination per sample ) were used; Kato-Katz for the detection of eggs of A . lumbricoides , hookworm , and T . trichiura , and Baermann for larvae of S . stercoralis [27] . Additionally , stool samples were visually inspected for Taenia spp . proglottids . Using the Kato-Katz technique , eggs of A . lumbricoides , hookworm , and T . trichiura were counted separately and the results of both slides averaged . The mean was then multiplied by a factor of 24 to obtain the number of eggs per 1 g of stool ( EPG ) . For quality control purposes , the two Kato-Katz slides were examined independently and results compared . Slides were re-read if inconsistencies were detected . An inconsistency was defined as a difference in the infection intensity ( EPG ) groupings based on WHO guidelines [15] . Physical fitness was assessed with a 20-m shuttle run test [22] . The running speed from the last completed 20-m lap and the total number of intervals completed were recorded . The child's age and speed were then converted into a VO2 max estimate ( to the nearest 0 . 1 ml kg−1 min−1 ) with an equation put forth by Léger et al . [28] . Physical strength was assessed with a grip strength and a standing broad jump test . For the grip strength test , the hand span ( distance from the tip of the thumb to the tip of the little finger ) of the child's dominant hand was measured ( to the nearest 0 . 5 cm ) and an electronic dynamometer ( Yi Lian Medicine; Shanghai , P . R . China ) adjusted accordingly to provide the optimal grip span [29] . Children were asked to stand straight yet relaxed , and grip the dynamometer with the dominant hand as hard as possible for 5 sec , with the arm fully extended and without other parts of the body touching it . Each child had two tries ( with a 15-sec rest in between ) , but only the maximum reading was recorded , to the nearest 0 . 1 kg . For the standing broad jump test , each child , standing behind a straight line , had two tries ( with a 15-sec rest in between ) to jump as far forward as possible with both legs . The jumps were recorded to the nearest 1 cm and the longer jump considered for an individual . The distance of the jump was measured from the starting line to the heel of the most back foot . For the measurement of body height and weight , children were asked to take off their shoes and sweater before standing on a digital weighing scale ( Model RCS-150; Nantong Xineng Ltd . , Jiangsu , P . R . China ) or stadiometer ( Nantong Xineng Ltd . , Jiangsu , P . R . China ) [22] . Both height and weight were recorded twice , to the nearest 0 . 1 cm or kg , respectively , and averaged . The body mass index ( BMI ) was defined as ( weight in kg ) / ( height in m ) 2; the BMI-for-age Z score ( BAZ ) and height-for-age Z score ( HAZ ) were used as indicators for wasting and stunting , respectively [30] . The thickness of skinfolds was measured at two sites , namely triceps and subscapular , with the Holtain skinfold caliper ( Holtain Ltd . ; Crymych , United Kingdom ) [31] . Measurements were performed in triplicate to the nearest 1 mm and averaged . The sum of the mean skinfolds at both sites was used as an estimate for body fat . The hemoglobin level was measured once , to the nearest 1 g l−1 , with a HemoCue Hb 301 system ( HemoCue AB . ; Ängelholm , Sweden ) using a drop of blood from the ear lobe . Anemia was defined according to WHO age- and sex-specific cut-offs [32] . The socioeconomic status of the participants at baseline was assessed through a questionnaire asking the children for the education level of their parents and the main source of their household income . The full trial protocol is available as supporting information ( Protocol S1 ) . Data were entered into Excel version 2008 ( Microsoft Corp . ; Redmond , United States of America ) , double-checked , and merged into a single database for statistical analysis with STATA version 10 . 0 ( STATA Corp . ; College Station , United States of America ) . The randomization code was broken after data entry and a series of internal consistency checks were completed . A per-protocol analysis was carried out in an un-blinded manner . In the primary analysis , physical fitness and strength scores , anthropometric measurements , and hemoglobin levels were expressed as means , and changes in the means between baseline and treatment follow-ups were compared between treatment groups in a multivariate linear regression model . In a sub-analysis , changes in the means of physical fitness and strength indicators between baseline and follow-up were compared among children of distinct soil-transmitted helminth infection status regardless of treatment status . To further explore the effect of infection intensity on these measurements , distinct groups of children were identified using principal component and cluster analysis , based on species-specific soil-transmitted helminth log transformed egg counts at baseline and at the 1- and 4-month follow-ups . Changes in the means of physical fitness and strength indicators between baseline and follow-up were compared among six biologically meaningful groups of varying soil-transmitted helminth infection intensity .
As illustrated in Figure 1 , an overall compliance of 92% was achieved with only nine children lost to follow-up over the 6-month trial period . These nine children were sick and absent during the follow-ups . Complete datasets were available for 99 children in the albendazole group and 95 children in the placebo group . No noteworthy difference in baseline socio-demographics or soil-transmitted helminth infection prevalence and intensity was observed between the albendazole and placebo groups ( Table 1 ) . Children were also comparable in terms of physical fitness and strength at baseline . The mean VO2 max estimate and number of 20-m laps completed were 44 . 9 ml kg−1 min−1 ( standard deviation ( SD ) : 2 . 8 ml kg−1 min−1 ) and 23 . 8 laps ( SD: 8 . 7 laps ) , respectively , for the albendazole group , and 45 . 4 ml kg−1 min−1 ( SD: 3 . 2 ml kg−1 min−1 ) and 25 . 2 laps ( SD: 9 . 9 laps ) , respectively , for the placebo group . With regard to physical strength , the mean grip strength and standing broad jump distance were 12 . 6 kg ( SD: 4 . 1 kg ) and 142 cm ( SD: 14 cm ) , respectively , for the albendazole group , and 12 . 6 kg ( SD: 3 . 5 kg ) and 142 cm ( SD: 14 cm ) , respectively , for the placebo group . However , when further stratified by sex , boys had higher physical fitness and strength than girls ( statistical significance achieved for all indicators except grip strength ) ( Table 1 ) . Treatment groups were also comparable in terms of anthropometric and hematologic characteristics at baseline . Despite the mean BMI for the albendazole and placebo group being 16 . 0 ( SD: 1 . 3 ) and 16 . 1 ( SD: 1 . 3 ) respectively , the baseline prevalence of wasting was only 3 . 6% . On the other hand , stunting was present in 76 . 8% ( mean height for the albendazole and placebo group was 127 . 4 cm ( SD: 8 . 9 cm ) and 126 . 3 cm ( SD: 7 . 1 cm ) , respectively ) of the cohort . Baseline prevalence of anemia was low at 4 . 6% , as the mean hemoglobin levels for the albendazole and placebo group were 161 g l−1 ( SD: 24 g l−1 ) and 157 g l−1 ( SD: 28 g l−1 ) , respectively . Children receiving triple-dose albendazole experienced a greater , but mostly not statistically significant , change in the means of their physical fitness scores than their peers from the placebo group at all three follow-ups ( Table 2 ) . VO2 max estimates increased by 1 . 0–2 . 3 ml kg−1 min−1 from baseline over the 6-month trial period for the albendazole group , while the increase for the placebo group ranged from 0 . 2–2 . 1 ml kg−1 min−1 . Likewise for the number of 20-m laps completed , the range of increase was 3 . 4–11 . 9 laps and 1 . 4–11 . 7 laps for the albendazole and placebo group , respectively . When adjusted for village , and at the individual level for sex , age , height , and weight at follow-up , the difference in the increase of physical fitness between both groups was highest at the 1-month follow-up , where the increase from baseline in the albendazole group was 0 . 9 ml kg−1 min−1 ( P = 0 . 05 ) or 2 . 1 laps ( P = 0 . 14 ) higher than the placebo group . With regard to physical strength , the grip strength increased 0 . 8–2 . 0 kg from baseline for the albendazole group , while the placebo group experienced an increase of 0 . 4–1 . 8 kg . The difference in the increase between both groups was highest at the 1-month follow-up ( 0 . 3 kg higher in the albendazole group ) , but this difference was not statistically significant . The largest change in standing broad jump distance from baseline was observed among the albendazole group at the 1-month follow-up ( +2 cm ) , but the placebo group fared better at the 6-month follow-up ( +2 cm ) . However , both of these increases were not statistically significant . In terms of secondary outcomes , children in the albendazole group had a larger increase , from baseline , in the means of their body height and weight and sum of skinfolds than their counterparts from the placebo group ( Table 3 ) . The range of increase for body height , weight , and sum of skin folds were 2 . 9–3 . 5 cm , 1 . 4–2 . 2 kg , and 1 mm , respectively , for the albendazole group , and 2 . 7–3 . 3 cm , 1 . 2–1 . 9 kg , and 1 mm for the placebo group . However , differences between both groups in the change from baseline were statistically non-significant at all follow-ups after adjusting for sex , age at follow-up , and village . A reduction in hemoglobin level was observed in both groups at the 1- and 6-month follow-ups , and the respective reduction in the albendazole group was 2 g l−1 ( P = 0 . 72 ) and 3 g l−1 ( P = 0 . 49 ) higher compared to the placebo group On the other hand , the increase from baseline in the albendazole group was 3 g l−1 higher than the placebo group at the 4-month follow-up ( P = 0 . 65 ) . When the soil-transmitted helminth infection status was used as explanatory variable for the primary outcomes ( Table 4 ) , T . trichiura-infected children had 1 . 6 ml kg−1 min−1 less increase in their VO2 max estimate from baseline than their non-infected peers at the 1-month follow-up ( P = 0 . 012 ) . Similarly , hookworm-infected children had 1 . 1 ml kg−1 min−1 less increase in their VO2 max estimate from baseline than their non-infected peers at the 6-month follow-up ( P = 0 . 03 ) . In addition , the increase in the number of 20-m laps completed from baseline was 4 . 6 ( P = 0 . 04 ) and 6 . 0 ( P = 0 . 01 ) laps less for T . trichiura-infected children than their non-infected counterparts at the 1- and 4-month follow-ups , respectively . As further illustrated in Figure 2 , an increase from baseline ( positive change ) in the number of 20-m laps completed at the 4-month follow-up was more dependent on a reduction in T . trichiura infection intensity than diminished A . lumbricoides and hookworm infection intensity . In terms of grip strength at the 1-month follow-up ( Table 4 ) , the increase from baseline among A . lumbricoides-infected children was 0 . 8 kg lower than among children not infected with this helminth species ( P = 0 . 05 ) , but hookworm-infected children had 0 . 9 kg more increase from baseline than their non-infected peers ( P = 0 . 04 ) . No statistically significant change in standing broad jump distance due to soil-transmitted helminth infection status was observed at each of the three follow-ups . When the children were grouped according to their longitudinal infection intensity patterns , the six groups that emerged ( Figure 3 ) had the following characteristics: group 1 , high infection intensity of all species at all time-points; group 2 , high infection intensity of all species except hookworm at all time-points; group 3 , high intensity of A . lumbricoides re-infection by the 4-month follow-up , high infection intensity of T . trichiura at all time-points , and no or minimal hookworm re-infection at follow-ups; group 4 , low intensity of A . lumbricoides re-infection by the 4-month follow-up , intermediate infection intensity of T . trichiura at all time-points , and no or minimal hookworm re-infection at follow-ups; group 5 , intermediate intensity of A . lumbricoides re-infection by the 4-month follow-up , and no or minimal T . trichiura and hookworm re-infection at follow-ups; and group 6 , infection intensity of all species were higher during the follow-ups compared to the pre-treatment baseline . When group 1 was used as the reference group in the multivariate linear regression models ( Table 5 ) , children from group 5 had consistently more increase in their VO2 max estimate from baseline than their peers from group 1 at all follow-ups ( range: 1 . 9–2 . 1 ml kg−1 min−1; all P<0 . 05 ) . A similar trend was observed for the number of 20-m laps completed and a statistically significant 5 . 7 more 20-m laps were completed by children from group 5 as compared to group 1 ( P = 0 . 04 ) and the baseline . In terms of standing broad jump distance , children from group 4 had a 6 cm higher increase from baseline than children from group 1 at the 4-month follow-up ( P = 0 . 03 ) . No statistically significant change in grip strength dependent on soil-transmitted helminth infection intensity was observed at all follow-ups .
As shown in our preceding work in Bulang communities [24] , [26] , the prevalence and intensity of soil-transmitted helminth infections in villages inhabited by this ethnic minority group can be very high . For example , in the current randomized controlled trial , we found baseline prevalence of T . trichiura , A . lumbricoides , and hookworm at 94 . 5% , 93 . 3% , and 61 . 3% , respectively . Therefore , an intensive de-worming regimen , consisting of triple-dose albendazole [24] , [33] , was employed to allow children a fair chance of developing their physical fitness unaffected by intestinal helminth infections . Re-infection with A . lumbricoides occurred more rapidly than expected and the prevalence of A . lumbricoides reached 80% of the pre-treatment prevalence 4 months after treatment [24] . Despite triple-dose albendazole treatment , a low cure rate of 19 . 6% was obtained against T . trichiura , corroborating previous conclusions that T . trichiura infection is particularly hard to cure with current anthelmintic drugs [16] , [34]–[36] . Such re-infection dynamics have complicated the evaluation of the potential health benefits of deworming and rendered the grouping of the children according to intervention near-irrelevant as the treated children might not have benefited from a meaningful helminth-free period for substantial catch-up growth . This finding further suggests that in our study area , the current WHO recommendation of single-dose albendazole ( 400 mg ) twice yearly [15] might be insufficient for controlling soil-transmitted helminthiasis . In a recently published trial conducted in India [37] , where 1 million preschool-aged children , 1 to 6 years old at baseline , were treated with albendazole every 6 months for 5 years , no statistically significant difference in anthropometric measurements was detected between the albendazole and control groups . In our study , even though a trend toward higher values was observed for the treated cohort , no statistically significant difference in most primary and secondary outcomes between the albendazole and placebo groups was detected during the 6-month follow-up period . However , we did find one statistically significant , and biologically important , difference in the VO2 max estimate at 1-month follow-up between the albendazole and placebo groups despite the relatively small sample size . It is interesting to note the variation in hemoglobin levels for both groups throughout the follow-up period and this could probably be due to seasonal dietary changes or the presence of other infections . In addition , we observed a general learning effect with the physical fitness and strength tests , especially the 20-m shuttle run test , amongst the children , but this was mitigated in the analysis by having a control group . In the sub-group analysis , we found that soil-transmitted helminth-infected children had performed significantly worse in the physical fitness and strength tests than their non-infected peers . When we grouped children according to their infection status at each follow-up , we observed that T . trichiura-infected children performed worse in the 20-m shuttle run than their non-infected peers . This confirmed the results from a cross-sectional study conducted by our group where T . trichiura-infected children were found to complete , on average , 6 . 1 20-m laps less and have a VO2 max estimate which was 1 . 9 ml kg−1 min−1 lower than their non-infected counterparts [22] . To survive in a host , adult T . trichiura worms anchor their whip-like anterior end into the wall of the large intestine and caecum by secreting pore-forming proteins . Such an invasive mechanism causes inflammation and bleeding , resulting in abdominal pain in the short term , and anemia and rectal prolapse in the long term , especially when large numbers of worms are present [38] . A significant change in physical fitness already at the 1-month follow-up could indicate that removing abdominal pain alone through the expulsion of T . trichiura might enhance the host's endurance in exhaustive exercises , such as the 20-m shuttle run . Hookworm-infected children were also found to have a significantly lower increase from baseline in their VO2 max estimates than children not infected with hookworm at the 6-month follow-up . Although anemia is a known symptom of hookworm infection and would be a plausible cause for reduced VO2 max estimates [21] , it was detected in only 10 . 7% of the hookworm-infected children and no significant association was found between any soil-transmitted helminth infection and hemoglobin level . The migration of the hookworm larvae through the pulmonary blood vessels , where they bore into the alveoli , could offer an alternative explanation to this observation . Although the larvae of A . lumbricoides undergo a similar migratory process [38] , no reduction in VO2 max estimates was observed in children infected with A . lumbricoides . In terms of grip strength , the increase from baseline among A . lumbricoides-infected children was significantly lower , while hookworm-infected children had a higher increase from baseline , when compared to their non-infected peers . As there is currently limited evidence on the association of soil-transmitted helminth infection and grip strength , these inconsistent findings warrant further investigation . When children were grouped according to infection intensity , we were able to take into consideration the degree of infection at baseline , 1-month , and 4-month follow-ups , and the extent of multiparasitism for each child . These analyses revealed that individuals with a combination of no or minimal T . trichiura and hookworm re-infection achieved higher improvements during the follow-ups in the 20-m shuttle run , as compared to peers with high infection intensity of all species . In addition , children with no or minimal A . lumbricoides and hookworm re-infection performed better in the standing broad jump than their counterparts with high infection intensity of all species . These findings provide further evidence of the impact of soil-transmitted helminth infections on the physical fitness and strength of school-aged children . The anthropometric and physical strength findings from this trial should be viewed in the light of the following limitations . A follow-up period of 6 months is probably too short for an accurate evaluation of anthropometric gains and physical strength increments from longer-term physical growth due to deworming . Taking into account that keeping controls untreated for a long period would be difficult due to ethical considerations , a 3- to 5-year prospective cohort study , where children are treated regularly to ensure that they are helminth-free , and the changes in anthropometric indicators and physical strength from baseline are monitored and compared with changes in soil-transmitted helminth infection intensity over time , could be a more appropriate study design . Finally , catch-up growth after anthelmintic treatment can only occur if the diet is sufficient [39] . Based on the investigators' observations in the field , most of the children's diet consists mainly of white rice with little protein sources . Dietary improvements , in addition to deworming , are therefore necessary in the current setting for catch-up growth to occur and perhaps to aid in the absorption of albendazole , and should be considered in future , more comprehensive studies . We conclude that there is no strong evidence for significant improvements in physical fitness and anthropometric indicators due to deworming with triple-dose albendazole . This might be partly explained by the rapid re-infection observed with A . lumbricoides and low cure rates with T . trichiura . However , negative impacts on the physical fitness and strength were observed in school-aged children infected with soil-transmitted helminths in sub-group analyses . In particular , the clear effects of T . trichiura infection on physical fitness in this trial is intriguing as the public health burden of this helminth species is currently not as well defined as that of the other two species . The fact that T . trichiura infection had the strongest negative impact on the physical fitness of the children but was hardly cured with triple-dose albendazole is another major concern . Finally , we also showed that the observed morbidities were infection intensity-dependent and in order to control them , regular deworming , coupled with dietary improvements and water , sanitation , and hygiene development , should be considered .
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Children from the developing world are often burdened with intestinal worms due to poor water supply , sanitation , and hygiene . However , the assessment of the burden due to intestinal worms is difficult , and thus , the benefits of deworming are unclear . In this study , we determined the effect of deworming on the physical fitness and strength of 9- to 12-year-old children in Yunnan , China , where intestinal worms are common . Children were treated with triple-dose albendazole or placebo and monitored over a 6-month period . Stool samples were collected for the diagnosis of intestinal worm infections . Physical fitness was estimated with a 20-m shuttle run test and physical strength was assessed with grip strength and standing broad jump tests . Children receiving triple-dose albendazole scored slightly higher values in the primary and secondary outcomes than those children who were given placebo . However , the differences were not significant . We also found that children infected with intestinal worms performed significantly worse in the physical fitness and strength tests than their non-infected counterparts . In particular , the negative impact of whipworm infection on physical fitness warrants further investigation .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"helminth",
"infections",
"infectious",
"diseases",
"medicine",
"and",
"health",
"sciences",
"neglected",
"tropical",
"diseases",
"tropical",
"diseases",
"soil-transmitted",
"helminthiases",
"parasitic",
"diseases"
] |
2014
|
Effect of Deworming on Physical Fitness of School-Aged Children in Yunnan, China: A Double-Blind, Randomized, Placebo-Controlled Trial
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During the adaptive evolution of a particular trait , some selectively fixed mutations may be directly causative and others may be purely compensatory . The relative contribution of these two classes of mutation to adaptive phenotypic evolution depends on the form and prevalence of mutational pleiotropy . To investigate the nature of adaptive substitutions and their pleiotropic effects , we used a protein engineering approach to characterize the molecular basis of hemoglobin ( Hb ) adaptation in the high-flying bar-headed goose ( Anser indicus ) , a hypoxia-tolerant species renowned for its trans-Himalayan migratory flights . To test the effects of observed substitutions on evolutionarily relevant genetic backgrounds , we synthesized all possible genotypic intermediates in the line of descent connecting the wildtype bar-headed goose genotype with the most recent common ancestor of bar-headed goose and its lowland relatives . Site-directed mutagenesis experiments revealed one major-effect mutation that significantly increased Hb-O2 affinity on all possible genetic backgrounds . Two other mutations exhibited smaller average effect sizes and less additivity across backgrounds . One of the latter mutations produced a concomitant increase in the autoxidation rate , a deleterious side-effect that was fully compensated by a second-site mutation at a spatially proximal residue . The experiments revealed three key insights: ( i ) subtle , localized structural changes can produce large functional effects; ( ii ) relative effect sizes of function-altering mutations may depend on the sequential order in which they occur; and ( iii ) compensation of deleterious pleiotropic effects may play an important role in the adaptive evolution of protein function .
During the adaptive evolution of a given trait , some of the selectively fixed mutations will be directly causative ( contributing to the adaptive improvement of the trait itself ) and some may be purely compensatory ( alleviating problems that were created by initial attempts at solution ) . Little is known about the relative contributions of these two types of substitution in adaptive phenotypic evolution and much depends on the prevalence and magnitude of antagonistic pleiotropy [1–9] . If mutations that produce an adaptive improvement in one trait have adverse effects on other traits , then the fixation of such mutations will select for compensatory mutations to mitigate the deleterious side effects , and evolution will proceed as a ‘two steps forward , one step back’ process . In systems where it is possible to identify the complete set of potentially causative mutations that are associated with an adaptive change in phenotype , key insights could be obtained by using reverse genetics experiments to measure the direct effects of individual mutations on the selected phenotype in conjunction with assessments of mutational pleiotropy in the same genetic background . To investigate the nature of adaptive mutations and their pleiotropic effects , we used a protein engineering approach to characterize the molecular basis of hemoglobin ( Hb ) adaptation in the high-flying bar-headed goose ( Anser indicus ) . This hypoxia-tolerant species is renowned for its trans-Himalayan migratory flights [10–12] , and its elevated Hb-O2 affinity is thought to make a key contribution to its capacity for powered flight at extreme elevations of 6000–9000 m [13–20] . At such elevations , an increased Hb-O2 affinity helps safeguard arterial O2 saturation , thereby compensating for the low O2 tension of inspired air . This can help sustain O2 delivery to metabolizing tissues because if environmental hypoxia is sufficiently severe , the benefit of increasing pulmonary O2 loading typically outweighs the cost associated with a lower O2 unloading pressure in the systemic circulation [21–24] . The Hb of birds and other jawed vertebrates is a heterotetramer consisting of two α-chain and two β-chain subunits . The Hb tetramer undergoes an oxygenation-linked transition in quaternary structure , whereby the two semi-rigid α1β1 and α2β2 dimers rotate around one another by 15° during the reversible switch between the deoxy ( low-affinity [T] ) conformation and the oxy ( high-affinity [R] ) conformation [25–28] . Oxygenation-linked shifts in the T↔R equilibrium govern the cooperativity of O2-binding and are central to Hb’s role in respiratory gas transport . The major Hb isoform of the bar-headed goose has an appreciably higher O2-affinity than that of the closely related greylag goose ( Anser anser ) , a strictly lowland species [13 , 29] . The major Hbs of the two species differ at five amino acid sites: three in the αA-chain subunit and two in the βA-chain subunit [30 , 31] . Of these five amino acid differences , Perutz [32] predicted that the Pro→Ala replacement at α119 ( αP119A ) is primarily responsible for the adaptive increase in Hb-O2 affinity in bar-headed goose . This site is located at an intersubunit ( α1β1/α2β2 ) interface where the ancestral Pro α119 forms a van der Waals contact with Met β55 on the opposing subunit of the same αβ dimer . Perutz predicted that the single αP119A mutation would eliminate this intradimer contact , thereby destabilizing the T-state and shifting the conformational equilibrium in favor of the high-affinity R-state . Jessen et al . [33] and Weber et al . [34] tested Perutz’s hypothesis using a protein engineering approach based on site-directed mutagenesis of recombinant human Hb , and their experiments confirmed the predicted mechanism . As a result of these experiments , bar-headed goose Hb is often held up as an example of a biochemical adaptation that is attributable to a single , large-effect substitution [35 , 36] . However , several key questions remain unanswered: Do the other substitutions also contribute to the change in Hb-O2 affinity ? If not , do they compensate for deleterious pleiotropic effects of the affinity-enhancing αP119A substitution ? Given that the substitutions in question involve closely linked sites in the same gene , another possibility is that neutral mutations at the other sites simply hitchhiked to fixation along with the positively selected mutation . Since the other substitutions in bar-headed goose Hb have not been tested , we do not know whether αP119A accounts for all or most of the evolved change in O2 affinity . Moreover , the original studies tested the effect of αP119A by introducing the goose-specific amino acid state into recombinant human Hb [33 , 34] . One potential problem with this type of ‘horizontal’ comparison–where residues are swapped between orthologous proteins of contemporary species–is that the focal mutation is introduced into a sequence context that is not evolutionarily relevant . If mutations have context-dependent effects , then introducing goose-specific substitutions into human Hb may not recapitulate the phenotypic effects of the mutations on the genetic background in which they actually occurred ( i . e . , in the ancestor of bar-headed goose ) . An alternative ‘vertical’ approach is to reconstruct and resurrect ancestral proteins to test the effects of historical mutations on the genetic background in which they actually occurred during evolution [37 , 38] . Here we revisit the functional evolution of bar-headed goose Hb , a classic text-book example of biochemical adaptation . We reconstructed the αA- and βA-chain Hb sequences of the most recent common ancestor of the bar-headed goose and its closest living relatives , all of which are lowland species in the genus Anser . After identifying the particular substitutions that are specific to bar-headed goose , we used a combinatorial approach to test the functional effects of each mutation in all possible multi-site combinations . To examine possible pleiotropic effects of causative mutations , we also measured several properties that potentially trade-off with Hb-O2 affinity: susceptibility to spontaneous heme oxidation ( autoxidation rate ) , allosteric regulatory capacity ( the sensitivity of Hb-O2 affinity to modulation by anionic effectors ) , and various secondary and tertiary structural properties . Measuring the direct and indirect effects of these mutations enabled us to address two fundamental questions about molecular adaptation: ( i ) Do each of the mutations contribute to the increased Hb-O2 affinity ? If so , what are their relative effects ? And ( ii ) Do function-altering mutations have deleterious pleiotropic effects on other aspects of protein structure or function ? If so , are these effects compensated by mutations at other sites ?
Using globin sequences from bar-headed goose , greylag goose , and other waterfowl species in the subfamily Anserinae , we reconstructed the α- and β-chain sequences of the bar-headed goose/greylag goose ancestor , which we call ‘AncAnser’ because it represents the most recent common ancestor of all extant species in the genus Anser ( Fig 1A ) . The principle of parsimony clearly indicates that all three of the α-chain substitutions that distinguish the Hbs of bar-headed goose and greylag goose occurred in the bar-headed goose lineage ( Gα18S , Aα63V , and αP119A ) , whereas each of the two β-globin substitutions occurred in the greylag goose lineage ( βT4S and βD125E ) ( Fig 1A and 1B ) . It is often implicitly assumed that the difference in Hb-O2 affinity between bar-headed goose and greylag goose is attributable to a derived increase in Hb-O2 affinity in the bar-headed goose lineage [14 , 35 , 36 , 39] . In principle , however , the pattern could be at least partly attributable to a derived reduction in Hb-O2 affinity in the greylag goose lineage , even if αP119A does account for the majority of the change in bar-headed goose . To resolve the polarity of character state change , we synthesized , purified , and functionally tested recombinant Hbs ( rHbs ) representing the wildtype Hb of bar-headed goose , the wildtype Hb of greylag goose , and the reconstructed Hb of their common ancestor , AncAnser . Functional differences between bar-headed goose and AncAnser rHbs reflect the net effect of three substitutions ( αG18S , αA63V , and αP119A ) and differences between greylag goose and AncAnser reflect the net effect of two substitutions ( βT4S and βD125E; Fig 1B ) . Since genetically based differences in Hb-O2 affinity may be attributable to differences in intrinsic O2-affinity and/or changes in sensitivity to allosteric effectors in the red blood cell , we measured O2-equilibria of purified rHbs under four standardized treatments: ( i ) in the absence of allosteric effectors ( stripped ) , ( ii ) in the presence of Cl- ions ( added as KCl ) , ( iii ) in the presence of inositol hexaphosphate ( IHP , a chemical analog of the endogenously produced inositol pentaphosphate ) , and ( iv ) in the simultaneous presence of KCl and IHP . This latter treatment is most relevant to in vivo conditions in avian red blood cells . In each treatment , we measured P50 , the partial pressure of O2 ( PO2 ) at which Hb is 50% saturated . To complement equilibrium measurements on the set of three rHbs and to gain further insight into functional mechanisms , we also performed stopped-flow kinetic experiments to estimate apparent O2 dissociation rates under the same conditions . The O2-equilibrium measurements confirmed the results of previous studies [13 , 29] by demonstrating that the wildtype rHb of bar-headed goose has a higher intrinsic O2-affinity than that of greylag goose ( as revealed by the lower P50 for stripped Hb ) ( Fig 2A , Table 1 ) . This difference persisted in the presence of Cl- ions ( P50 ( KCl ) ) , in the presence of IHP ( P50 ( IHP ) ) , and in the simultaneous presence of both anions ( P50 ( KCl+IHP ) ) ( Fig 2A , Table 1 ) . All rHbs exhibited cooperative O2-binding , as indicated by Hill coefficients ( n50’s ) >2 in the presence of IHP . The difference in Hb-O2 affinity between bar-headed goose and greylag goose is mainly attributable to differences in intrinsic affinity , as there were no appreciable differences in sensitivities to allosteric effectors ( Table 1 ) . This is consistent with a previous report that native Hbs of bar-headed goose and greylag goose have similarly high binding constants for inositol pentaphosphate [29] . Pairwise comparisons between each of the two modern-day species and their reconstructed ancestor ( AncAnser ) revealed that the elevated Hb-O2 affinity of the bar-headed goose is a derived character state . O2-equilibrium properties of greylag goose and AncAnser rHbs were generally very similar ( Fig 2A ) . The triangulated comparison involving rHbs from the two contemporary species ( bar-headed goose and greylag goose ) and their reconstructed ancestor ( AncAnser ) revealed that the observed difference in Hb-O2 affinity ( P50 ( KCl+IHP ) ) between bar-headed goose and greylag goose is mainly attributable to a derived increase in Hb-O2 affinity in the bar-headed goose lineage , but it is also partly attributable to a derived reduction in Hb-O2 affinity in the greylag goose lineage ( Fig 2A ) . This demonstrates the value of ancestral protein resurrection for inferring the direction and magnitude of historical evolutionary changes in character state . Kinetic measurements demonstrated that the increased O2-affinity of bar-headed goose rHb is associated with a lower apparent rate of O2 dissociation , koff ( Fig 2B ) relative to the rHbs of both greylag goose and AncAnser . In combination with the inferred history of sequence changes ( Fig 1A and 1B ) , the comparison between the rHbs of bar-headed goose and AncAnser indicates that the derived increase in Hb-O2 affinity in bar-headed goose must be attributable to the independent or joint effects of the three substitutions at sites α18 , α63 , and α119 . To measure the effects of each individual mutation in all possible multi-site combinations , we used site-directed mutagenesis to synthesize each of the six possible mutational intermediates that connect the ancestral and descendant genotypes ( Fig 1B ) . In similar fashion , we synthesized each of the two possible mutational intermediates that connect AncAnser and the wildtype genotype of greylag goose ( Fig 1B ) . The analysis of the bar-headed goose mutations on the AncAnser background revealed that mutations at each of the three α-chain sites ( αG18S , αA63V , and αP119A ) produced significant increases in intrinsic Hb-O2 affinity ( indicated by reductions in P50 ( stripped ) ) ( Fig 3 , Table 1 ) . The Pα119A mutation had the largest effect on the ancestral background , producing an 18% reduction in P50 ( stripped ) ( increase in intrinsic Hb-O2 affinity ) . On the same background , αG18S or αA63V produced 7% and 14% reductions in P50 ( stripped ) , respectively . In the set of six ( = 3 ! ) possible mutational pathways connecting the low-affinity AncAnser genotype ( GAP ) and the high-affinity bar-headed goose genotype ( SVA ) , the αP119A mutation produced a significant increase in Hb-O2 affinity on each of four possible backgrounds ( corresponding to the first step in the pathway , two alternative second steps , and the third step; Fig 3 ) . When tested on identical backgrounds , αP119A invariably produced a larger increase in intrinsic Hb-O2 affinity than either αG18S or αA63V . Nonetheless , of the six possible forward pathways connecting GAP and SVA , αP119A had the largest effect in four pathways and αA63V had the largest effect in the remaining two . The two pathways in which αA63V had the largest effect were those in which it occurred as the first step . In fact , αG18S or αA63V only produced significant increases in Hb-O2 affinity when they occurred as the first step . The effects of these two mutations were always smaller in magnitude when they occurred on backgrounds in which the derived Ala α119 was present . In addition to differences in average effect size , αP119A also exhibited a higher degree of additivity across backgrounds than the other two mutations . For example , the affinity-enhancing effect of αP119A on the AncAnser background is mirrored by a similarly pronounced reduction in O2-affinity when the mutation is reverted on the wildtype bar-headed goose background ( αA119P ) . By contrast , forward and reverse mutations at α18 and α63 do not show the same symmetry of effect ( S1 Fig ) . Comparison of crystal structures for human and bar-headed goose Hbs [40] revealed that each of the three bar-headed goose α-chain substitutions have structurally localized effects . In the major bar-headed goose Hb isoform , Ser α18 and Ala α119 are located at the edges of the α1β1 intradimer interface . As noted by Jessen et al . [33] , the αP119A mutation has very little effect on the main-chain formation and appears to exert its functional effect via the elimination of side chain contacts and increased backbone flexibility . With regard to the αA63V mutation , the introduction of the valine side chain causes minor steric clashes with Gly 25 and Gly 59 of the same subunit ( Fig 4 ) . This interaction may alter O2-affinity by impinging on the neighboring His α58 , the ‘distal histidine’ that stabilizes the α-heme Fe-O2 bond [41–46] . Given that the AncAnser and greylag goose rHbs exhibit similar equilibrium and kinetic O2-binding properties ( Fig 2 ) , the two greylag goose substitutions ( βT4S and βD125E ) do not produce an appreciable net change in combination . Interestingly , however , each mutation by itself produces a slightly reduced sensitivity to IHP ( Table 1 ) , such that values of P50 ( IHP ) and P50 ( KCl+IHP ) for the single-mutant intermediates were lower than those for AncAnser and the wildtype genotype of greylag goose . Since amino acid mutations often affect multiple aspects of protein biochemistry [47–50] , it is of interest to test whether adaptive mutations that improve one aspect of protein function simultaneously compromise other properties . Amino acid mutations that alter the oxygenation properties of Hb often have pleiotropic effects on allosteric regulatory capacity , structural stability , and susceptibility to heme loss and/or heme oxidation [51–58] . Accordingly , we tested whether mutational changes in intrinsic O2-affinity are associated with potentially deleterious changes in other structural and functional properties . Analysis of the full set of bar-headed goose and greylag goose rHb mutants revealed modest variability in autoxidation rate ( S2A Fig , Table 2 ) . This property is physiologically relevant because oxidation of the ferrous ( Fe2+ ) heme iron to the ferric state ( Fe3+ ) releases superoxide ( O2- ) or perhydroxy ( HO2• ) radical , and prevents reversible Fe-O2 binding , rendering Hb inoperative as an O2-transport molecule . Although mutational changes in intrinsic O2 affinity ( ∆log P50 ( stripped ) ) were not significantly correlated with changes in autoxidation rate in the full dataset ( r = -0 . 311 ) , analysis of the bar-headed goose rHb mutants revealed a striking pairwise interaction between mutations at α18 and α63 ( residues which are located within 7 Å of one another ) . The αA63V mutation produced a significant >2-fold increase in the autoxidation rate on backgrounds in which the ancestral Gly is present at α18 ( Fig 5 , Table 2 ) . The adjacent Val α62 is highly conserved because it plays a critical role in restricting solvent access to the distal heme pocket , thereby preventing water-catalyzed rupture of the Fe-O2 bond to release a superoxide ion [58–61] . An increase in side chain volume at α63 may compromise this gating function , resulting in an increased susceptibility to heme oxidation . The increased autoxidation rate caused by αA63V is fully compensated by αG18S ( Fig 5 ) , a highly unusual amino acid replacement because glycine is the only amino acid at this site ( the C-terminal end of the A helix ) that permits the main chain to adopt the typical Ramachandran angles ( S3 Fig ) . Introduction of the serine side chain at α18 in bar-headed goose Hb forces this residue to undergo a peptide flip relative to human Hb , so the carbonyl oxygen points in the opposite direction . This unusual replacement at α18 may be required to accommodate the bulkier Val side chain at α63 , thereby alleviating conformational stress . Site-directed mutagenesis experiments on mutant Hbs and myoglobins have documented a positive , linear correlation between log ( P50 ) and log ( kauto ) [58–61] . The αG18S and αA63V mutations are therefore unusual because reductions in Hb-O2 affinity are not invariably coupled with increases in autoxidation rate . Aside from the compensatory interaction between mutations at α18 and α63 , we observed no evidence for trade-offs between O2-affinity and any of the other measured functional or structural properties . There were no significant correlations between ∆log P50 ( stripped ) and changes in allosteric regulatory capacity ( Table 1 ) , as measured by sensitivity to Cl- ( r = -0 . 534 ) , IHP ( r = -0 . 137 ) , or both anions in combination ( r = -0 . 300 ) . The goose rHbs revealed no appreciable variation in α-helical secondary structure as measured by circular dichroism spectroscopy ( S2B Fig , S1 Table ) and there were no significant correlations between Δlog P50 ( stripped ) and changes in secondary structure over the physiological range ( pH 6 . 5 , r = -0 . 357; pH 7 . 5 , r = -0 . 052 ) . Likewise , the rHbs exhibited very little variation in the stability of tertiary structure as measured by UV-visible spectroscopy ( S2C Fig , S2 Table ) and there were no significant correlations between Δlog P50 ( stripped ) and changes in stability over the physiological range ( pH 6 . 5 , r = -0 . 511; pH 7 . 5 , r = -0 . 338 ) . In summary , we found no evidence for pleiotropic trade-offs between intrinsic O2-affinity and any measured properties of Hb structure or function other than autoxidation rate .
We now return to the two questions we posed at the outset: ( 1 ) Do each of the bar-headed goose substitutions contribute to the increased Hb-O2 affinity ? It depends on the order in which the substitutions occur . Our experiments demonstrated that the αP119A mutation always produced a significant increase in intrinsic Hb-O2 affinity regardless of the background in which it occurred . As documented previously [33 , 34] , the αP119A mutation also produces a significant affinity-enhancing effect on the far more divergent background of human Hb ( which differs from bar-headed goose Hb at 89 of 267 amino acid sites in each αβ half-molecule [33% divergence in protein sequence] ) . By contrast , αG18S or αA63V only produced significant affinity-enhancing effects when they occurred as the first step in the pathway ( on the AncAnser background ) . If it was advantageous for the ancestor of today’s bar-headed geese to have an increased Hb-O2 affinity , our experiments suggest that any of the three α-chain mutations alone would have conferred a beneficial effect , but only αP119A would have produced the same effect after the other two had already fixed . This illustrates an important point about distributions of mutational effect sizes in adaptive walks: in the presence of epistasis , relative effect sizes may be highly dependent on the sequential order in which the substitutions occur . ( 2 ) Do function-altering mutations have deleterious pleiotropic effects on other aspects of protein structure or function ? On the AncAnser background , the affinity-enhancing mutation , αA63V , produces a pronounced increase in the autoxidation rate . This is consistent with the fact that engineered Hb and myoglobin mutants with altered affinities often exhibit increased autoxidation rates [54 , 56 , 58 , 62] . In the case of bar-headed goose Hb , the increased autoxidation rate caused by αA63V is completely compensated by a polarity-changing mutation at a spatially proximal site , αG18S . This compensatory interaction suggests that the αG18S mutation may have been fixed by selection not because it produced a beneficial main effect on Hb-O2 affinity , but because it mitigated the deleterious pleiotropic effects of the affinity-altering αA63V mutation . Alternatively , if αG18S preceded αA63V during the evolution of bar-headed goose Hb , then the ( conditionally ) deleterious side effects of αA63V would not have been manifest . Our experiments revealed no evidence to suggest that the affinity-altering αP119A mutation perturbed other structural and functional properties of Hb . Data on natural and engineered human Hb mutants have provided important insights into structure-function relationships and the nature of trade-offs between different functional properties [52 , 54 , 56–58 , 63] . An important question concerns the extent to which function-altering spontaneous mutations are generally representative of those that eventually fix and contribute to divergence in protein function between species . There are good reasons to expect that the spectrum of pleiotropic effects among spontaneous mutations or low-frequency variants may be different from the spectrum of effects among evolutionary substitutions ( mutations that passed through the filter of purifying selection and eventually increased to a frequency of 1 . 0 ) [64] . The affinity-altering mutations that are most likely to fix ( whether due to drift or positive selection ) may be those that have minimal pleiotropic effects and therefore do not require compensatory mutations at other sites .
We took sequence data for the α A- and βA-globin genes of all waterfowl species from published sources [30 , 31] . After optimizing nucleotide sequences of AncAnser αA- and βA-globin genes in accordance with E . coli codon preferences , we synthesized the αA-βA-globin cassette ( Eurofins MWG Operon ) . We cloned the globin cassette into a custom pGM vector system [65 , 66] , as described previously [67–74] , and we then used site-directed mutagenesis to derive globin sequences of greylag goose , bar-headed goose , and each of the mutational intermediates connecting these wildtype sequences with AncAnser . We conducted the codon mutagenesis using the QuikChange II XL Site-Directed Mutagenesis kit ( Agilent Technologies ) and we verified all codon changes by DNA sequencing . We carried out recombinant Hb ( rHb ) expression in the E . coli JM109 ( DE3 ) strain as described previously [66] . To ensure the complete cleavage of N-terminal methionines from the nascent globin chains , we over-expressed methionine aminopeptidase ( MAP ) by co-transforming a plasmid ( pCO-MAP ) along with a kanamycin resistance gene ( 48 ) . We then co-transformed the pGM and pCO-MAP plasmids and subjected them to dual selection in an LB agar plate containing ampicillin and kanamycin . We carried out the over expression of each rHb mutant in 1 . 5 L of TB medium . We grew bacterial cells at 37°C in an orbital shaker at 200 rpm until absorbance values reached 0 . 6 to 0 . 8 at 600 nm . We then induced the bacterial cultures with 0 . 2 mM IPTG and supplemented them with hemin ( 50 μg/ml ) and glucose ( 20 g/L ) . The bacterial culture conditions and the protocol for preparing cell lysates were described previously [66] . We resuspended bacterial cells in lysis buffer ( 50 mM Tris , 1 mM EDTA , 0 . 5 mM DTT , pH 7 . 0 ) with lysozyme ( 1 mg/g wet cells ) and incubated them in an ice bath for 30 min . Following sonication of the cells , we added 0 . 5–1 . 0% polyethyleneimine solution , and we then centrifuged the crude lysate at 13 , 000 rpm for 45 min at 4°C . We purified the rHbs by means of two-step ion-exchange chromatography . Using high-performance liquid chromatography ( Äkta start , GE Healthcare ) , we passed the samples through a cation exchange-column ( SP-Sepharose ) followed by passage through an anion-exchange column ( Q-Sepharose ) . We subjected the clarified supernatant to overnight dialysis in Hepes buffer ( 20 mM Hepes with 0 . 5mM EDTA , 1 mM DTT , 0 . 5mM IHP , pH 7 . 0 ) at 4°C . We used prepackaged SP-Sepharose columns ( HiTrap SPHP , 5 mL , 17–516101; GE Healthcare ) equilibrated with Hepes buffer ( 20 mM Hepes with 0 . 5mM EDTA , 1 mM DTT , 0 . 5mM IHP pH 7 . 0 ) . After passing the samples through the column , we eluted the rHb solutions against a linear gradient of 0–1 . 0 M NaCl . After desalting the eluted samples , we performed an overnight dialysis against Tris buffer ( 20 mM Tris , 0 . 5mM EDTA , 1 mM DTT , pH 8 . 4 ) at 4°C . We then passed the dialyzed samples through a pre-equilibrated Q-Sepharose column ( HiTrap QHP , 1 mL , 17-5158-01; GE Healthcare ) with Tris buffer ( 20 mM Tris , 0 . 5mM EDTA , 1 mM DTT , pH 8 . 4 ) . We eluted the rHb samples with a linear gradient of 0–1 . 0 M NaCl . We then concentrated the samples and desalted them by means of overnight dialysis against 10 mM Hepes buffer ( pH 7 . 4 ) . We then stored the purified samples at -80° C prior to the measurement of O2-equilibria and O2 dissociation kinetics . We analyzed the purified rHb samples by means of sodium dodecyl sulphate ( SDS ) polyacrylamide gel electrophoresis and isoelectric focusing . After preparing rHb samples as oxyHb , deoxyHb , and carbonmonoxy derivatives , we measured absorbance at 450–600 nm to confirm the expected absorbance maxima . Using purified rHb solutions ( 0 . 3 mM heme ) , we measured O2-equilibrium curves at 37°C in 0 . 1 M Hepes buffer ( pH 7 . 4 ) in the absence ( ‘stripped’ ) and presence of 0 . 1 M KCl and IHP ( at two-fold molar excess over tetrameric Hb ) , and in the simultaneous presence of KCl and IHP . We measured O2-equilibria of 5 μl thin-film samples in a modified diffusion chamber where absorption at 436 nm was monitored during stepwise changes in the equilibration of N2/O2 gas mixtures generated by precision Wösthoff mixing pumps [75–77] . We estimated values of P50 and n50 ( Hill’s cooperativity coefficient ) by fitting the Hill equation Y = PO2n/ ( P50n + PO2n ) to the experimental O2 saturation data by means of nonlinear regression ( Y = fractional O2 saturation; n , cooperativity coefficient ) . Standard errors of the mean P50 were based on triplicate measurements of independently purified rHbs , and the nonlinear fitting of each curve was based on 5–8 equilibration steps . Free Cl- concentrations were measured with a model 926S Mark II chloride analyzer ( Sherwood Scientific Ltd , Cambridge , UK ) . We determined apparent O2 dissociation constants ( koff ) of purified oxy rHbs at 37°C using an OLIS RSM 1000 UV/Vis rapid-scanning stopped flow spectrophotometer ( OLIS , Bogart , CA ) equipped with an OLIS data collection software . Briefly , rHb ( 10 μM heme ) in 200 mM Hepes , pH 7 . 4 , was mixed 1:1 with N2-equilibrated 200 mM Hepes , pH 7 . 4 , containing 40 mM freshly dissolved sodium dithionite [78] . We monitored absorbance at 431 nm as a function of time . All traces exhibited the best fit to a monoexponential function ( r2 > 0 . 99 ) . To estimate autoxidation rates , we treated purified rHb samples with potassium ferricyanide ( K3[Fe ( CN ) 6] ) , and we then reduced rHbs to the ferrous ( Fe2+ ) state by treating the samples with sodium dithionite ( Na2S2O4 ) . We removed the dithionite by means of chromatography ( Sephadex G-50 ) . For each rate measurement , we used 200 μl of 20 μM oxyHb in 100 mM potassium phosphate buffer , pH 7 . 0 , containing 1 mM EDTA and 3 mM catalase and superoxide dismutase per mole oxyHb . To measure the spontaneous conversion of ferrous ( Fe2+ ) oxyHb to ferric ( Fe3+ ) metHb we recorded the absorbance spectrum at regular intervals over a 90 h period . We collected spectra between 400nm and 700nm using a BioTek Synergy2 multi-mode microplate reader ( BioTek Instruments ) . We estimated autoxidation rates by plotting the A541/A630 ratio ( ratio of absorbances at 540nm and 630nm ) vs time , using IGOR Pro 6 . 37 software ( Wavemetrics ) . We used the exponential offset formula in IGOR to calculate the 50% absorbance per half-life ( i . e . , 0 . 5AU/half-life ) . Standard errors of the mean autoxidation rate were based on triplicate measurements of independently purified rHbs . We assessed the pH-dependent stability of the rHbs by means of UV-visible spectroscopy . We prepared 20 mM filtered buffers spanning the pH range 2 . 0–11 . 0 . We prepared 20 mM glycine-HCl for pH 2 . 0–3 . 5; 20 mM acetate for pH 4 . 0–5 . 5; 20 mM phosphate for pH 6 . 0–8 . 0; 20 mM glycine-NaOH for pH 8 . 5–10 . 0; 20 mM carbonate-NaOH for pH 10 . 5 and phosphate-NaOH for pH 11 . 0 . We diluted the purified rHb samples in the pH-specific buffers to achieve uniform protein concentrations of 0 . 15 mg/ml . We incubated the samples for 3–4 h at 25°C prior to spectroscopic measurements , and maintained this same temperature during the course of the experiments . We measured absorbance in the range 260–700 nm using a Cary Varian Bio100 UV-Vis spectrophotometer ( Varian ) with Quartz cuvettes , and used IGOR Pro 6 . 37 ( WaveMetrics ) to process the raw spectra . For the same set of rHbs , we tested for changes in secondary structure of the globin chains by measuring circular dichroism spectra on a JASCO J-815 spectropolarimeter using a quartz cell with a path length of 1 mm . We assessed changes in secondary structure by measuring molar ellipticity in the far UV region between 190 and 260 nm in three consecutive spectral scans per sample . We modelled structures of goose Hbs and the various mutational intermediates using the program COOT [79] , based on the crystal structures of bar-headed goose Hb ( PDB models 1hv4 and 1c40 ) [40 , 80] , greylag goose Hb ( PDB 1faw ) [81] , and human deoxyHb ( PDB 2dn2 ) .
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During adaptive phenotypic evolution , some of the associated genetic changes may contribute directly to changes in the selected trait ( causative mutations ) and other changes may ameliorate the negative side-effects of the causative changes ( compensatory mutations ) . To assess the nature of such changes and their relative prevalence , we used a protein engineering approach to characterize the molecular basis of a well-documented biochemical adaptation: the increased hemoglobin-oxygen affinity in the bar-headed goose ( Anser indicus ) , a champion of high-altitude flight . The experiments revealed the contributions of specific substitutions to the adaptive increase in hemoglobin-oxygen affinity in bar-headed goose and demonstrated that compensatory interactions may play an important role in adaptive protein evolution due to trade-offs between different functional properties .
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2018
|
Molecular basis of hemoglobin adaptation in the high-flying bar-headed goose
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Schistosoma eggs cause chronic liver inflammation and a complex disease characterized by hepatic fibrosis ( HF ) and splenomegaly ( SplM ) . FOXP3+ Tregs could regulate inflammation , but it is unclear where these cells are produced and what roles they play in human schistosomiasis . We investigated blood and spleen FOXP3+ Tregs in Chinese fishermen with lifelong exposure to Schistosoma japonicum and various degrees of liver and spleen disease . FOXP3+ Tregs accounted for 4 . 3% of CD4+ T cells and 41 . 2% of FOXP3+CD4+ T cells; they could be divided into CD45RA-FOXP3hi effector ( eTregs ) and CD45RA+FOXP3low naive Tregs . Blood Treg levels were high in severe HF ( +1 . 3; p = 0 . 004 ) and in SplM ( +1 . 03 , p = 0 . 03 ) . Multivariate regression showed that severe HF ( +0 . 85 , p = 0 . 01 ) and SplM ( +0 . 97; p = 0 . 05 ) were independently associated with the higher proportion of Tregs in the blood . This effect was mostly due to an increase in the proportion of eTregs in the blood of HF+++ ( +0 . 9%; p = 0 . 04 ) and SplM ( +0 . 9%; p = 0 . 04 ) patients . The proportion of eTregs expressing CXCR3 in the blood was lower in the HF+++ patients ( 37 . 4 +/- 5 . 9% ) than in those with milder fibrosis ( 51 . 7 ± 2%; p = 0 . 009 ) , whereas proportion were similar for cells expressing CD25hi , CCR7 , and CTLA-4 . Splenectomy improves symptoms and was associated with decreases in blood FOXP3+ Treg ( -2 . 5; p<0 . 001 ) and eTreg ( -1 . 3; p = 0 . 03 ) levels . SplM spleens contained a high proportion of eTregs with CXCR3 , CCR5 and CTLA4 upregulation and CCR7 downregulation . This , and the strong expression of ligands of CXCR3 and CCR5 in the liver ( n = 8 ) but not in the spleen suggested that spleen eTregs migrated to Th1-infiltrated liver tissues . Such migration may be attenuated in hepatosplenic patients due to lower levels of CXCR3 expression on Tregs ( p = 0 . 009 ) . Thus , higher blood Treg levels are associated with severe liver disease and splenomegaly . Our data are consistent with the hypothesis that the spleen is a major source of Tregs in subjects with splenomegaly . In most cases , Tregs migrate to the Th1-infiltrated liver and the lower levels of CXCR3+ Tregs in the blood of patients with severe schistosomiasis suggest that decreases in Treg migration sites of inflammation may aggravate the disease .
Regulatory T cells expressing the Forkhead box protein P3 ( Foxp3 ) transcription factor are crucial regulators of immunological self-tolerance and homeostasis [1 , 2] . They suppress the activation , proliferation and effector functions of many immune cells , including CD4+ and CD8+ T cells , natural killer cells , NKT cells , B cells , and antigen-presenting cells . The Treg phenotype results from two major regulatory events: the upregulation of genes associated with Treg function , including FOXP3 , CTLA4 , IL2RA , TNFRSF18 ( encoding GITR ) , IKZF2 ( encoding Helios ) and IKZF4 ( encoding Eos ) , the expression of is epigenetically regulated [3 , 4] , and the FOXP3-mediated downregulation of several genes , including IL2 and IFNG [5–8] . FOXP3+ Tregs have been divided into CD45RA+FOXP3low CD4+ naïve Tregs and CD45RA-FOXP3hiCD4+ effector Tregs ( eTregs ) , whereas blood CD45RA-FOXP3low CD4+ T cells are effector T cells without suppressive activity [9 , 10] . FOXP3+ Tregs are produced either in the thymus ( tTregs ) , mostly by self-antigens , or in the periphery ( pTregs ) after stimulation by conventional antigens [11–13] . FOXP3+ Tregs regulate inflammation in response to infectious pathogens [14] . Schistosome worms lay their eggs in the mesenteric and portal veins of their human host; the eggs are trapped in liver sinusoids where they cause intense inflammation and fibrosis in the portal spaces . This , in turn , causes an increase in portal blood pressure and the development of varicose veins , leading to hemorrhage and death . In some patients , advanced hepatic fibrosis is associated with splenomegaly; this association is referred to as the hepatosplenic clinical form . Splenomegaly is invariably associated with a worsening of the disease , at least partly due to an aggravation of portal blood hypertension . However , the role of the spleen in severe schistosomiasis has been little explored and probably involves more than just a contribution to portal blood hypertension . We investigated the properties and fate of naïve and effector Tregs in the blood of Schistosoma ( S . ) japonicum-infected subjects with various degrees of hepatic fibrosis , with and without splenomegaly . The induction of pTregs should occur during egg-induced inflammation , but it may also occur in the hyperstimulated spleen of schistosome-infected individuals . Naïve Tregs express homing receptors for lymphoid organs ( CCR7 ) , whereas eTregs expressing high levels of CCR5 , CXCR3 , CCR6 , and CCR8 [15] are attracted to non-lymphoid , inflamed tissues . Under these conditions , FOXP3+ Tregs become phenotypically and functionally specialized and develop into Th2 , Th1 or Th17 cells [16] . It is unclear how mediators produced in the environment created by schistosome eggs influence Tregs . We first investigated the level of activation of FOXP3+ Tregs in the blood of Schistosoma japonicum-infected patients with schistosomiasis of various degrees of severity . We then evaluated homing receptors on Tregs and determined whether changes in Treg migration to the spleen and liver were associated with disease aggravation .
The study was approved by the ethics committee of the Hunan Institute of Parasitic Diseases , Hunan Province , China and by the WHO . The French ethics committee did not authorize tests for HCV and HBV infection for the whole cohort . Only compliant participants were recruited and they were free to drop out at any point . Written informed consent was obtained from each subject . Hepatic fibrosis was evaluated by ultrasound and with the WHO grading scale [17] , modified as described below . The WHO scale grades peripheral ( NetF ) and central fibrosis ( CentF ) separately . CentF is graded A , B , C , CL , D , E or F . The C linear thickening pattern ( CL ) of CentF represents the thickness of the uninterrupted fibrosis of the linear wall of the portal vein extending from the portal vein to its branches . The uninterrupted nature of the fibrosis distinguishes CL from grade C ( discontinuous thickness ) , and the linear pattern differentiates it from the patches observed in grades D , E , and F . More than 60% of the fishermen had grade CL fibrosis . We therefore subdivided CL into CLL ( CL light ) , CLM ( CL medium ) and CLH ( CL heavy ) : CLL was observed in the left lobe of the liver only and CLM and CLH were observed in both lobes . Subjects with right lobe fibrosis extending to second-order branches were classified as CLM and those with right lobe fibrosis extending well into the second-order branches were classified as CLH . Only CLH was associated with evidence of portal hypertension , and was therefore grouped with grades D , E and F to define a severe CentF phenotype . The WHO grades peripheral fibrosis ( network fibrosis , NetF ) as narrow mesh ( GN ) when the lumen diameter of net was <12 mm across and wide mesh ( GW ) if >12 mm . We also refined GN grading into three categories: GNL ( or GWL ) if the mesh streak ( or band ) was <2 mm thick , GNM ( or GWM ) if 2–4 mm , and GNH ( or GWH ) if >4 mm thick . Patients were assigned to three hepatic fibrosis ( HF ) groups on the basis of CentF: HF+/- ( B , C ) , HF++ ( CLL ) and HF+++ ( CLM , CLH , D , E ) . Multivariate regression analysis showed that NetF had no effect on any of the dependent variables studied . We nevertheless indicate the NetF grade in our analysis: absent ( G0 ) or light ( GNL ) in the HF+/- group , and intermediate ( GNM ) or high ( GNH , GW ) in the HF++ and HF+++ groups . Study subjects were also assigned to two groups on the basis of spleen size: normal spleen ( Spl , spleen size <110 mm ) and splenomegaly ( SplM , >110 mm ) . Patients who had undergone splenectomy were included in a separate Spl- group . Finally , individuals with moderate or severe hepatic fibrosis and splenomegaly were historically described as hepatosplenic patients . All the subjects studied were fishermen working on the Dong Ting Lake who were recruited ( from 2003 to 2009 ) from the same region , and were highly exposed to infection with S . japonicum . Exposure was evaluated by interviews , as previously described [18] . Only subjects with high levels of exposure were included in this study and exposure was not , therefore , a significant covariate in the analyses . In this population , we found no correlation between clinical disease and the number ( 0 to more than 20 ) of praziquantel treatments , probably because treatments were taken no more frequently than every two to three years , on average . Liver and spleen diseases were carefully evaluated by at least two ultrasound scans , carried out during different periods . It was not possible to perform such studies on a very large number of fishermen due to the long distance between the field and the laboratory . The Chinese patients studied ( n = 76 ) were from a large population of fishermen ( a few thousand ) investigated in a previous study [18] . They were selected according to the criteria mentioned above . All blood samples were collected and processed on the same day . All FACS analyses were performed within 36 hours of blood or tissue collection; none of the samples were frozen . Samples were from controls ( n = 20 ) were collected and studied on the same days as those of the patients . The controls were living in the same region but reported no contact with lake water; they tested negative for schistosome antigens by ELISA and showed no signs of spleen or liver disease . All study subjects were aged between 30 and 65 years . Eleven of the 16 HF+++ patients , seven of the 23 HF++ patients and six of the 29 HF+/- patients had splenomegaly and therefore also belonged to the SplM group . The splenectomy group ( Spl- ) included eight subjects with HF++ or HF+++ . All tissues were obtained from subjects undergoing splenectomy at Yueyang Hospital . These subjects came from the same population of fishermen ( four men and four women ) and they were 25–59 years old ( 48 . 2 ± 3 . 8 ) . None of these individuals was infected with HCV or HBV and all had schistosome eggs in liver biopsy specimens . All but three had advanced or severe CentF or NetF . Three patients displayed milder but nevertheless significant CentF , which was associated with advanced NetF in two patients . These patients had severe splenomegaly . Control healthy tissues were obtained from a tissue bank in France . Liver biopsy specimens were collected from eight patients; blood and spleen tissues were obtained from five ( four men and one woman ) of these eight subjects . All cell labeling was performed on cells immediately after their purification from the blood , without stimulation . Counts and viability were determined with a hemocytometer and the trypan blue dye exclusion technique . An average of 95% of the cells were viable cells . PBMCs or spleen cells were dispensed ( 4 x 105 cells/tube ) into 5 ml polystyrene tubes ( Falcon ) for surface and intracellular staining with the Human FOXP3 Buffer Set ( BD Pharmingen ) . Quadruple staining was carried out with FITC-conjugated anti-CD45RA ( H1100; BD Pharmingen ) , PE-Cy7-conjugated anti-CD4 ( L3T4 , eBiosciences ) , and PE-conjugated anti-FOXP3 ( 259D/C7; BD Pharmingen ) antibodies , together with one of the following APC-conjugated antibodies: anti-CD25 ( M-A251; BD Pharmingen ) antibodies; anti-CCR7 ( 3D12; eBiosciences ) , anti-CXCR3 ( 1C6/CXCR3; BD Pharmingen ) , anti-CCR5 ( 2D7/CCR5; BD Pharmingen ) or anti-CTLA-4 ( BNI3; BD Pharmingen ) antibody . For the analysis of IFN-γ production , cells were first double-stained with FITC-conjugated anti-CD3 ( UCHT1; BD Pharmingen ) and PE-Cy7–conjugated anti-CD4 antibodies and then stained with APC-conjugated anti-IFNγ intracellular markers ( 4S-B3; eBiosciences ) . They were incubated with 100 ng/ml PMA , 1 μg/ml ionomycin and monensin ( BD GolgiStop ) for 6 hours at 37°C before intracellular cytokine labeling . Isotype controls were obtained from the corresponding manufacturers . All antibodies were used according to the manufacturers’ recommendations . Flow cytometry was carried out with a FACScalibur flow cytometer ( BD Biosciences ) and cellquest software . DIVA and FlowJo software ( TreeStar ) software was used for analysis . Liver and spleen biopsy specimens were stored in RNA Later ( Life Technologies , Courtaboeuf , France ) at -20°C . Tissue homogenization was carried out with a Precellys-24 device ( Bertin Technologies , Ozyme , Saint-Quentin-en-Yvelines , France ) , with ceramic beads ( 1 . 4 mm diameter , CK14 ) , in 350 μl RLT lysis buffer ( Qiagen SAS , Courtaboeuf , France ) supplemented with 3 . 5 μl β-mercaptoethanol . We added 400 μl of Tri-reagent ( Life Technologies ) and 150 μl of chloroform . The aqueous phase was mixed with 500 μl of 50% ethanol ( liver ) or 70% ethanol ( spleen ) , and RNA was purified on an RNeasy spin column ( Qiagen SAS , Courtaboeuf , France ) . RNA integrity was assessed with a 2100 Bioanalyzer ( Agilent , Palo Alto , CA , USA ) . Liver and spleen “controls” were from the Biological Resource Center , Curie Institute , Paris , and from Stratagene ( Agilent ) , Clonetech ( Ozyme ) , Panomics ( Ozyme ) , and INSERM U1040 , Montpellier . Biopsy specimens were collected from deceased individuals with no known history of infection ( i . e . from untransplanted organs ) or from liver biopsies carried out for diagnostic purposes . We checked that the donors were healthy , by assessing inflammatory cytokine levels in these tissues . If a “healthy tissue” displayed an abnormal pattern of inflammatory cytokine expression ( with respect to that in the other biopsies ) , it was excluded from the study . Total RNA ( 1 μg ) , RIN > 7 , was reverse-transcribed with the High Capacity cDNA Reverse Transcription Kit ( Life Technologies , Courtaboeuf , France ) . Real-time quantitative PCR , with 20 ng of cDNA , was performed with the ABI 7900HT Fast Real-Time PCR System and TaqMan Universal PCR Master Mix ( Applied Biosystems , Life Technologies ) . The TaqMan gene expression assays used in this study were as follows: CCL3 ( Hs00234142_m1 ) , CCL5 ( hs00174575_m1 ) , CCL19 ( Hs00171149_m1 ) , CCL20 ( Hs00171125_m1 ) , CCL21 ( Hs99999110_m1 ) , CXCL9 ( Hs00171065_m1 ) , CXCL10 ( Hs00171042_m1 ) , CXCL11 ( Hs00171138_m1 ) , IFNG ( Hs99999041_m1 ) , IL12B ( Hs00233688_m1 ) , IL12RB2 ( Hs00155486_m1 ) , RPLP0 ( Hs99999902_m1 ) , TBX21 ( Hs00203436_m1 ) , from Applied Biosystems . Gene expression values were normalized relative to those for the housekeeping gene RPLP0 ( ribosomal phosphoprotein large P0 ) . Transcript levels for this housekeeping gene were stable in all the study groups . A significant difference ( a value differing from the mean for the other samples by more than twice the SEM ) in the abundance of RPLP0 transcripts between one sample and the mean value for the other samples was considered to indicate a problem with RNA extraction . Group comparisons were performed by nonparametric analysis in SPSS software . We assessed how hepatic disease ( fibrosis ) , spleen disease ( splenomegaly ) and splenectomy affected subpopulations of Tregs , by carrying out linear regression analysis on these dependent variables . Hepatic fibrosis was divided into three binary classes , as previously described [19] . The variables introduced into the regression model were made binary to avoid making assumptions about the existence of a linear relationship between the dependent variable and the independent variables . Spleen disease was also divided into three binary classes . All binary variables were included in the linear regression model . Age and sex were not significant covariates in most models . Results are given as the non-standardized slope ( A ) , its 95% confidence interval ( CI ) and the p value of the association . The statistical significance of the effect of splenectomy was systematically assessed by comparisons with the splenomegaly group or the HF+++ group , because all the splenectomized subjects belonged to these groups before surgery . Praziquantel treatment varied considerably between fishermen and was included as a covariate . Surprisingly , despite considerable effort , we found no correlation between the number of praziquantel treatments ( 0 to >20 ) and disease intensity or Treg response . Exposure was not included as a covariate because all the study subjects had high levels of exposure to the infected waters of the lake . As independent testing was carried out , p values above 0 . 01 are suggestive of an association and the corresponding variables will be investigated again in a future study on a different population . No correction method was used because multivariate analyses involving nested models do not require statistical correction .
We evaluated the proportion of Tregs among blood FOXP3+ T cells in all the fishermen ( group 1 ) . These individuals had been exposed to the risk of infection with S . japonicum for more than 10 years . Tregs ( CD45RA+FOXP3low and CD45RA-FOXP3hi ) accounted for 4 . 3 ± 0 . 26% ( SEM ) of all CD4+ T cells and 41 . 2 ± 0 . 16% of all FOXP3+CD4+ T cells ( Fig 1A and 1B ) . The remaining FOXP3+CD4+ cells , CD45RA-FOXP3low T cells , corresponding to FOXP3+ non-regulatory T cells , accounted for 6 . 1 ± 0 . 4% of all CD4+ T cells and 58 . 7 ± 0 . 15% of the FOXP3+CD4+ T cells . We phenotyped patient CD45RA-FOXP3hi and CD45RA+FOXP3low Tregs for CD25 , CCR7 , CXCR3 , CCR5 and CTLA-4 ( splenectomized patients were excluded from this analysis ) ( Figs 1C and S1 ) . CCR7 directs Tregs to lymphoid organs and CXCR3 and CCR5 direct these cells to Th1-infiltrated tissues [20] . Strong CD25 expression is a marker of Treg activation , whereas CTLA-4 expression is strongly correlated with suppressive activity . Most CD45RA+FOXP3low Tregs expressed CCR7 ( 80 . 5 ± 1 . 2% ) , but a few expressed CD25 ( 18 . 3 ± 2 . 3% ) , CXCR3 ( 11 . 6 ± 2% ) , CCR5 ( 3 . 2 ± 0 . 8% ) and CTLA-4 ( 8 . 3 ± 0 . 7% ) . By contrast , the proportion of cells expressing CCR7 ( 34 . 1 ± 2 . 2% ) was lower in CD45RA-Foxp3hi than in CD45RA+Foxp3low Tregs ( p<0 . 01 ) . By contrast the proportions of cells with CD25hi ( 51 . 2 ± 2 . 2% ) , CXCR3 ( 48 . 1 ± 2% ) , CCR5 ( 45 . 8 ± 2 . 1% ) or CTLA-4 ( 71 . 7 ± 1 . 5% ) expression were higher for CD45RA-FOXP3hi than for CD45RA+FOXP3low Tregs ( p <0 . 01 for all comparisons ) . These patterns were used to characterize CD45RA+FOXP3low naive Tregs and CD45RA-FOXP3hi eTregs , respectively in normal blood [9] . Thus , blood Tregs from schistosome-infected patients can be divided into eTregs and naïve Tregs . Splenomegaly ( SplM ) occurs in patients with advanced hepatic fibrosis ( HF ) . Tregs may be produced and/or attracted to both the liver and spleen , because these organs are sites of intense inflammation and cell proliferation . We evaluated the frequency of Tregs in the blood of individuals ( study group 1 ) with spleen and/or liver disease of various degrees of severity , to investigate a possible link between SplM , HF and Tregs . Study subjects were assigned to three groups according to the severity of HF and to two groups on the basis of spleen size , as described in the methods . We found that the levels of all Tregs and of eTregs in the blood increased with increasing hepatic fibrosis grade ( Fig 2A ( all Tregs ) and Fig 2C ( effector Tregs ) ; black and green dotted lines ) . However , this effect was not observed in subjects with splenomegaly ( dotted red lines ) , because splenomegaly markedly increases blood Treg and eTreg levels regardless of the degree of hepatic fibrosis ( for all HF grades: HF+/- , HF++ HF+++ ) . However , we observed no clear effect of HF ( dotted blue line ) and splenomegaly ( dotted red line ) on blood levels of naïve Tregs ( Fig 2B ) . Thus , our findings suggest that both hepatic fibrosis and splenomegaly increase the levels of all Tregs and of eTregs in the blood . This was confirmed by the simultaneous testing of HF and SplM in the regression model , which showed that HF ( +0 . 85 ± 0 . 3% , p = 0 . 01 ) and SplM ( +0 . 97 ± 0 . 49% , p = 0 . 05 ) were independently associated with high blood Treg levels . Furthermore , eTregs and naïve Tregs were not equally affected . Blood eTreg levels ( Fig 2C ) were higher both in patients with HF ( p = 0 . 05 ) and in patients with SplM ( p = 0 . 08 ) than in healthy controls , but the levels of naïve Tregs in the blood were similar in these two groups ( Fig 2B ) . The largest differences observed were those between eTreg levels in HF++ and HF+++ patients ( +0 . 9 ± 0 . 4% , p = 0 . 04 ) and between SplM patient and patients with a normal spleen ( +0 . 9 ± 0 . 36% , p = 0 . 04 ) . We analyzed the expression of molecules crucial for the activation and homing of Tregs , to evaluate the migratory capacities of these cells . We also evaluated CTLA-4 , a marker of suppressor activity . The proportions of eTregs expressing CD25hi , CCR7 , and CTLA-4 were similar between the Spl and SplM group and among HF groups . However , the proportion of CXCR3+ eTregs was lower in the HF+++ group ( 37 . 4 ± 5 . 9% ) than in patients with milder fibrosis ( HF+/- and HF++ , as compared to HF+++ ) ( 51 . 7 ± 2%; p = 0 . 009 ) and was not affected by SplM ( Fig 2D ) . There seemed to be a higher proportion of CCR5+ eTregs ( Fig 2E ) in subjects with splenomegaly ( p = 0 . 04 ) than in subjects with a normal spleen , regardless of the degree of hepatic fibrosis . There are , thus significantly fewer CXCR3+eTregs in the blood of hepatosplenic subjects than in the blood of subjects with milder disease . Conversely , our data suggest that the proportion of CCR5+ eTregs may be higher in patients with splenomegaly , but it proportion may not be affected by the degree of hepatic fibrosis . CXCR3 and CCR5 direct FOXP3+ Tregs to sites infiltrated with TH1 cells . We therefore hypothesized that CXCR3 and CCR5 regulated the trafficking of eTregs toward Th1-infiltrated egg granulomas in the liver . We tested this hypothesis by evaluating the production of CCR5 and CXCR3 ligands in liver biopsy specimens from hepatosplenic patients who underwent splenectomy ( study group 2 ) . We also evaluated the production of CCR7 and CCR6 ligands , because these receptors are also expressed by FOXP3+ Tregs ( this study and [15] ) . Transcript levels for CCL5 ( CCR5 ligand ) , CXCL9 , 10 , 11 ( CXCR3 ) , CCL19 , CCL21 ( CCR7 ) and CCL20 ( CCR6 ) were four to 33 times higher ( p<0 . 01 ) in infected than in control livers ( Fig 3A ) . The largest difference in expression detected was that for CCL20 ( 33-fold , p<0 . 001 ) . By contrast , in the spleen , only CXCL11 ( CXCR3 ) and CXCL10 ( CXCR3 ) transcripts were more abundant in HSP patients than in control individuals ( p<0 . 01 ) ( Fig 3B ) . We also evaluated transcripts of TH1-related genes in infected livers: IL12RB2 mRNA levels ( p = 0 . 004 ) were four to five times higher in infected than in control livers , and IFNγ , IL12B and TBX21 mRNA levels followed a similar pattern ( p<0 . 15 ) . Moreover , transcripts of CXCR3 , CXCL9 ( r = 0 . 93 , p = 0 . 02 ) and CXCL11 ( r = 0 . 88 , p = 0 . 05 ) were correlated with IFNG transcript levels in the liver , but not in the spleen of infected subjects ( CXCL10 also showed a trend towards correlation; r = 0 . 79 p = 0 . 1 ) . No such correlation was found for the other chemokines tested: CCL3 ( ligand for CCR1 , 5 ) , CCL5 ( CCR1 , 3 , 5 ) , CCL19 ( CCR7 ) , CCL20 ( CCR6 ) , CCL21 ( CCR7 ) and CXCL9 ( CXCR3 ) . Similarly , eTreg levels in the blood were negatively correlated ( r = -0 . 73 , p = 0 . 002 ) with TH1 cell levels in the blood ( Fig 3C ) , consistent with the negative regulation of TH1 cells exerted by e Tregs . We found that blood levels of FOXP3+ Tregs ( -2 . 5 ± 0 . 56 , p<0 . 001 ) and eTregs ( -1 . 3±0 . 6 , p = 0 . 03 ) were lower in patients with severe liver ( HF++ or HF+++ ) and spleen ( SplM ) disease who had undergone splenectomy ( Fig 2A and 2C ) than in HF+++ subjects with splenomegaly . This observation , in addition to the high Treg counts in the blood of patients with SplM , suggests that the spleen may be involved in eTreg production ( induction or proliferation ) . We thus analyzed Tregs in the spleens removed from patients with severe hepatosplenic disease ( Fig 4 ) . The proportion of naïve Tregs among CD4+ T cells was lower in the spleen ( 0 . 95 ± 0 . 2 ) than in the blood ( 2 . 68 ± 0 . 7 ) ( p = 0 . 02 ) ( Fig 4A ) . The proportion of naïve Tregs expressing CCR7 was also lower in the spleen than in the blood ( p = 0 . 006 ) ( Fig 4B ) . However , the proportion of naïve Tregs expressing CCR5 tended to be higher in the spleen than in the blood ( p = 0 . 07 ) ( Fig 4B ) . There was no statistically significant difference in the proportion of eTregs in the spleen ( 2 . 8 ± 0 . 64 ) and blood ( 5 . 2 ± 1 . 6 ) ( Fig 4A ) . However , the proportion of eTregs expressing CD25 or CCR7 was lower in the spleen than in the blood ( p = 0 . 05 and p = 0 . 04 , respectively ) whereas the proportion of eTregs expressing CCR5 tended to be higher in the spleen than in the blood ( p = 0 . 08 ) ( Fig 4C ) . Thus the composition of the naïve Treg and eTreg populations differed between the spleen and the blood . Spleen eTregs may be less activated than blood eTregs . Nevertheless , spleen eTregs displayed higher levels of CCR5 and CTLA-4 expression , typical of cells committed to migrate to inflamed tissues . The observation that spleen naïve Tregs have weaker CCR7 expression and stronger CCR5 expression than blood naïve Tregs suggests that they have been activated , a process that might ultimately lead to their transformation into eTregs .
Few studies have analyzed FOXP3+ Tregs in patients infected with schistosomes and no other study has focused on FOXP3+ Tregs without the interference of FOXP3+ non-Tregs . Indeed , to define human Treg cells , many studies used the following combination of markers CD4+CD25hiFOXP3+ or CD4+CD25hiFOXP3+CD127-/low , however they don’t allow the exclusion of FOXP3+ non-Tregs cells [21] . Therefore , being limited by a four colors cytometer , we chose to use the CD45RA marker to eliminate the FOXP3+ non-Tregs cells from the study as well as analyzing the naïve versus effector Tregs populations . Although others have described that naïve Tregs and eTregs are CD127-/low , this marker combination might underestimate the frequencies of Tregs [9 , 21] , therefore , it will be necessary in future study , to analyze simultaneously additional markers such as CD127 , Helios , Ki67 to better define the Treg cells populations . Thus , we studied naïve Tregs and effector Tregs separately , which relate to different stages of differentiation/activation of the Treg population . Effector Tregs are partly derived from the activation of naïve Tregs . They may also be generated by eTreg multiplication . Peripheral Tregs ( pTregs ) and thymic Tregs ( tTregs ) are named according to the part of the body in which Treg differentiation occurred , either during thymic development or after birth . Tregs can be induced ( induced Tregs ) from pTregs and tTregs and it is generally accepted that inducible Tregs are produced in peripheral organs and require various stimuli that are probably best delivered in a lymphoid environment . Most of the reported observations relate to mice . In humans , the origin and fate of inducible Tregs are less clear . In this work , the modulation of blood Treg and eTreg levels and our observations for spleen Tregs are indicative of Treg induction in the periphery by signals such as egg-derived molecules . Our data are also consistent with the occurrence of induction in the spleen of hepatosplenic subjects . We found that both fibrosis and spleen disease were independently associated with high FOXP3+ eTreg levels in the blood . Others have shown that CD4+CD25+FOXP3+CD127low T-cell levels are high in the blood of S . haematobium-infected children and in the blood of S . mansoni-infected individuals after anti-helminthic treatment [22 , 23] . These results , together with those presented here , indicate that schistosome infection stimulates the production of FOXP3+ Tregs . The positive correlation between eTreg frequencies in the blood and the severity of both hepatic and spleen disease suggests that infection stimulates the production or regulates the induction of Tregs . Such induction may occur in lymphoid organs strongly simulated with schistosome eggs , such as the spleen or the mesenteric lymph nodes . Our findings suggest that Treg induction may occur in the spleen of subjects with splenomegaly . We found that the spleens of these patients contained a large proportion of Tregs , most of which were already activated , although not to the same extent as the Tregs in the blood . Thus , the spleen may be a source of Tregs , particularly given its large size in patients with splenomegaly . Alternatively , spleen Tregs may be produced in the blood and captured by the spleen . However , this seems unlikely because splenomegaly in patients was associated with the highest eTreg levels in blood and splenectomy resulted in a drop in blood eTreg levels . Moreover , the properties ( high proportions of CXCR3 , CCR5 and CTLA-4 ) of spleen eTregs indicated that these cells were unlikely to remain in the spleen , instead being poised to migrate to inflamed tissues , such as tissues infiltrated with large numbers of eggs , such as the liver and the intestine . Unlike the spleen , the liver displayed high levels of CXCR3 and CCR5 ligands and Th1 inflammation known to attract CXCR3+ and CCR5+ Tregs . The blood and spleen Tregs may therefore home to the liver rather than remaining in the spleen . However , we cannot rule out the possibility that unknown mechanisms prevent Treg egression from the spleen in patients with splenomegaly , causing Treg accumulation in this organ . We studied RNA levels in the liver with non-endemic controls ( control tissues from a French blood bank ) because we could not obtain local controls ( there was no local tissue bank ) . The differences reported here may not be entirely due to schistosome infections , instead reflecting genetic differences between controls and patients . We recently performed transcriptome analyses on these samples and found that only 50 genes displayed increases at least as important as those reported here for the chemokine receptor ligands . We therefore think it highly unlikely that the differences observed were not specific to the infection . Thus , our data suggest that some of the blood eTregs in schistosome-infected patients are produced in the spleen ( pTregs ) , either by induction from naïve Tregs or by the proliferation of eTregs . The induction of peripheral FOXP3+ Tregs has been demonstrated in mice [11–13] . It is triggered mostly by conventional antigens and results in the selection of high-affinity TCRs . These cells have been less studied in human diseases , due to the lack of markers for distinguishing human pTregs from tTregs . However , in a recent report , neuropilin was identified as a marker of tTregs [24 , 25] in mice . If this result is subsequently confirmed in humans , then it will be possible to use neuropilin and proliferation markers , such as Ki67 , to distinguish between tTregs and pTregs and to demonstrate definitively the active production of eTregs in the spleen and , possibly , in other tissues hyperstimulated by eggs , such as the mesenteric lymph nodes , through induction , proliferation , or both . Our findings also suggest a possible explanation for high eTreg levels in the blood of both HF and SplM patients . First , a low proportion of CXCR3+ eTregs may limit the recruitment of eTregs to the liver . Second , the spleen of hepatosplenic patients may release larger numbers of Tregs into the blood , and , finally , the liver and mesenteric nodes may contribute to Treg production , as suggested in patients infected with HCV [26] . It is important to determine the role of Tregs in human schistosomiasis , because splenectomy , which is performed in hepatosplenic patients , may eliminate a major source of Tregs . The elimination of CD25+ T cells ( accounting for >50% of non-Tregs ) promotes collagen deposition in the liver of S . mansoni-infected mice [27] . However , the same treatment reduces worm and egg load in S . japonicum-infected animals , suggesting that Tregs may decrease the clinical manifestations of schistosomiasis but prevent the development of sterile immunity [28 , 29] . However , these results require confirmation and no study has yet assessed the effects of highly enriched preparations of FOXP3+ Tregs in schistosome-infected mice . Our finding that the proportion of CXCR3+ eTregs is lower in patients with severe HF indirectly supports the hypothesis that impaired Treg recruitment in the liver contributes to disease , although we cannot exclude the possibility that CXCR3+ Tregs are retained selectively in other organs , such as the spleen . Nevertheless , this conclusion is consistent with several reports showing that CXCR3 is crucial for the localization of Tregs in the inflamed liver [30 , 31] . Helbig et al . [32] reported that the CXCR3 chemokines were the most strongly expressed chemokines in the livers of patients with chronic hepatitis C . Others have more directly implicated Foxp3+ Tregs in protection against chronic hepatitis: in a model of autoimmune inflammation of the liver , Lapierre et al . [33] observed that the adoptive transfer of ex vivo-expanded CXCR3+ Tregs in mice with auto-immune hepatitis deficiency resulted in targeting to the inflamed liver and the restoration of peripheral tolerance , inducing a remission of auto-immune disease . Furthermore Hasegawa et al . [34] showed that acute GVHD could be improved in the intestine , liver and lungs by the accumulation of CXCR3-expressing CD4+CD25+ regulatory T cells ( but not CXCR3-Tregs ) in target organs . CXCR3+ Treg cells accumulated in Th1-associated chemokine-expressing target organs , resulting in a stronger suppression of alloreactive donor T cells . Interestingly , Oo et al . [35] compared blood- and liver-derived Tregs and showed that liver-derived Tregs expressed large numbers of CXCR3 chemokine receptors . In flow-based adhesion assays with human hepatic sinusoidal endothelium , Tregs used CXCR3 for binding and transmigration . The authors suggested that CXCR3 mediated the recruitment of Tregs via the hepatic sinusoidal endothelium . Erhardt et al . [36] reported that CXCR3+Foxp3+ Tregs generated in mice with ConA–induced hepatitis disseminated in the body and migrated specifically to the liver , where they limited immune system-mediated liver damage . Finally , mice lacking CXCR3 are more prone to liver fibrosis initiated by the loss of the anti-fibrogenic and angiostatic effects of CXCL9 on hepatic stellate cells [37] and sinusoidal endothelial cells [38] . These and our findings suggest that the small proportion of CXCR3+ eTregs in individuals with severe schistosomiasis may impair the influx of eTregs into the liver , thereby contributing to HF . No such association with disease was observed with CCR5+ eTregs , the proportion of which in the blood may be increased in subjects with splenomegaly , as suggested here . If confirmed in a larger number of subjects , these results would favor attempts to compensate for the decrease the frequencies of CXCR3+ eTregs . It would be interesting to investigate the respective roles of these receptors in the migration to the different egg-infiltrated tissues , including the intestine , where they may also play important regulatory roles . The role of FOXP3+ Tregs in the spleen , which does not contain schistosome eggs , is less clear . Tregs may limit inflammation in the spleen , thereby inefficiently containing the splenomegaly . Conversely , our observations raises the intriguing possibility that eTregs generated in the spleen of hepatosplenic patients may be pathogenic . Tregs are normally stable due to both TSDR demethylation and the FOXP3-mediated suppression of IL2 . However , IL-2 activation may cause these cells to lose FOXP3 expression and their suppressive capacities [8] . However , in normal physiological conditions , they conserve their TSDR demethylation pattern , and this prevents them from becoming pathogenic . However , Tregs produced in the massive hyperplasic spleen of hepatosplenic patients may not acquire the epigenetic demethylation pattern of normal Tregs , like the Tregs of lymphopenic mice , which develop pathogenic properties [39] . Thus , Tregs generated in a huge , hyperactive spleen may be unstable and develop into pathogenic T cells ( e . g . Th17 cells ) aggravating spleen disease . We are currently evaluating this possibility . The observation that splenomegaly is associated with high counts of potentially unstable Tregs that may develop pathogenic properties may also stimulate research into Tregs in malaria and visceral leishmaniasis , which are also associated with marked splenomegaly . In both infections , the liver and the spleen play important roles in controlling parasite multiplication . It is therefore essential to confirm that the spleens of individuals with splenomegaly overproduce eTregs and to check whether these eTregs present the epigenetic signature of stable suppressive Tregs . In summary , blood and spleen Treg levels are increased in individuals with severe hepatosplenic disease caused by S . japonicum . An analysis of the homing receptors on these cells and of the receptor ligands in the liver suggest that these cells may migrate to the liver ( and probably also the intestine ) , to contain Th1 inflammation . Treg migration to tissues may be reduced by impaired CXCR3 expression on these cells . Further investigations are required to confirm these observations in a larger number of individuals with different clinical forms of schistosomiasis as well as the inclusion in the study of additional markers to define Tregs populations .
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Schistosomes are human parasites that cause severe hepatic disease in their host . They cause chronic inflammation when their eggs become trapped in small hepatic vessels . Most subjects from areas in which schistosomes are endemic display lifelong infection , and liver inflammation progresses to advanced hepatic fibrosis , portal hypertension and hypersplenism in 10 to 20% of infected subjects . The mechanisms controlling inflammation and limiting severe hepatic disease in most infected subjects remain unclear . We evaluated the role in this control of FOXP3+ Tregs , which exert strong control over inflammation . We found that activated FOXP3+ Treg levels were high in the blood of subjects with severe disease , probably due to the production of large numbers of these cells by the hyperactive spleen . We also found that the proportion of CXCR3+ effector FOXP3+ Tregs was lower , resulting in potentially lower migration rates and an aggravation of liver disease .
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[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2016
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FOXP3+ Regulatory T Cells in Hepatic Fibrosis and Splenomegaly Caused by Schistosoma japonicum: The Spleen May Be a Major Source of Tregs in Subjects with Splenomegaly
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Gene expression in chloroplasts is controlled primarily through the regulation of translation . This regulation allows coordinate expression between the plastid and nuclear genomes , and is responsive to environmental conditions . Despite common ancestry with bacterial translation , chloroplast translation is more complex and involves positive regulatory mRNA elements and a host of requisite protein translation factors that do not have counterparts in bacteria . Previous proteomic analyses of the chloroplast ribosome identified a significant number of chloroplast-unique ribosomal proteins that expand upon a basic bacterial 70S-like composition . In this study , cryo-electron microscopy and single-particle reconstruction were used to calculate the structure of the chloroplast ribosome to a resolution of 15 . 5 Å . Chloroplast-unique proteins are visualized as novel structural additions to a basic bacterial ribosome core . These structures are located at optimal positions on the chloroplast ribosome for interaction with mRNAs during translation initiation . Visualization of these chloroplast-unique structures on the ribosome , combined with mRNA cross-linking , allows us to propose a model for translation initiation in chloroplasts in which chloroplast-unique ribosomal proteins interact with plastid-specific translation factors and RNA elements to facilitate regulated translation of chloroplast mRNAs .
The chloroplast of plants and algae is believed to have originated from the endosymbiosis of an ancient photosynthetic bacteria into a eukaryotic host . Remnants of that ancient bacteria remain in the modern chloroplast , as it maintains a circular genome and transcription and translation machinery similar to that of prokaryotes [1 , 2] . Chloroplasts are responsible for photosynthetic energy production in plants and algae , and have recently been targeted as a platform for production of recombinant therapeutic proteins , making the understanding of translation in this organelle essential [3] . Approximately 60 proteins are translated in the plastid , a small fraction of the total proteins functioning in this organelle . The majority of chloroplast proteins are encoded by the nuclear genome and post-translationally imported into the plastid [4] . Coordinate expression from the nuclear and plastid genomes is required for development in photosynthetic organisms , and is achieved in chloroplasts primarily through regulation of translation [5 , 6] . Translation of many chloroplast genes is also regulated in response to light , and to maintain stoichiometric accumulation of multiprotein-complex subunits [7 , 8] . All of this regulation involves a host of protein translation factors , and the formation of RNA–protein complexes on chloroplast mRNA 5′ untranslated regions ( 5′ UTRs ) [9–13] . Some of these protein factors are specific to individual mRNAs , whereas others serve classes of messages . Due to the bacterial ancestry of the organelle , translation in the chloroplast has been considered bacterial-type translation , and many of the requisite bacterial-type translation factors can be identified in chloroplasts , although not all of these are exact homologs of the bacterial proteins [14] . Translation regulation in the chloroplast is more complex than in bacteria , and this complexity requires additional RNA and protein components not found in prokaryotic systems ( reviewed in [5 , 15] ) . A number of protein factors have been identified as essential components of chloroplast translation , although how these factors interact with an mRNA to facilitate chloroplast translation is not known . Chloroplast messages also experience pausing during their translation , which has been implicated in maintaining the proper stoichiometry of gene expression from polycistronic mRNAs , as well as in cotranslational membrane insertion or cofactor association [16 , 17] . mRNA secondary structures or rare codon usage are often suggested as the cause of pausing during elongation; however , for mRNAs studied in chloroplasts ( particularly psbA and atpA ) , these alone are insufficient to account for the pause sites . RNA elements identified as regulatory components in the translation of chloroplast messages are primarily located in the 5′ UTR . These elements include Shine-Dalgarno ( S-D ) sequences , stem-loop structures , and A/U rich elements [10 , 18–20] . Nearly all bacterial mRNAs use base pairing between a S-D sequence located in the 5′ UTR of the mRNA and a complementary sequence located near the 3′ end of the 16S rRNA [21] . Base pairing between these sequences is essential for bacterial translation initiation , and bacterial S-D elements are located 7 ± 2 nucleotides ( nt ) upstream of the initiator AUG to allow for a simple physical positioning of the initiator AUG in the P-site of the ribosome [21 , 22] . In plastids , only some mRNAs have recognizable S-D sequences , and these are found over a large range of the 5′ UTR , some up to 100 nt upstream of the start site AUG [23 , 24] . This diverse positioning of S-D elements precludes a simple physical positioning of plastid mRNAs on the ribosome , and indicates that chloroplasts have a fundamentally different mechanism than bacteria for translation initiation . The complete proteome of chloroplast ribosomes from both green algae [25 , 26] and higher plants [27 , 28] has been elucidated . A majority of the protein components of chloroplast ribosomes have clear homologs in bacterial 70S ribosomes . However , a significant number of chloroplast-unique proteins and domains were also identified ( Tables S1 and S2 ) . Five plastid-specific ribosomal proteins ( PSRPs ) have been identified in Chlamydomonas reinhardtii , four of which are located on the small subunit of the ribosome . Three other ribosomal proteins , S2 , S3 , and S5 , have large chloroplast-unique domains on otherwise homologous bacterial ribosomal proteins [26] . Together , these protein additions increase the mass of the small subunit of the chloroplast ribosome by 25% compared to a bacterial 30S subunit ( Table S1 ) . Based on overall conservation of protein components and rRNAs , and the locations of proteins with chloroplast-unique domains ( Figure 1 ) , it has been hypothesized that novel structures have been added to the small subunit of the ribosome to accommodate the specific demands on chloroplast translation regulation [26] . In light of the accumulating evidence that translation regulation in the chloroplast is far more complex than in bacteria , and that chloroplast ribosomes contain unique protein components compared to 70S-type ribosomes , it is important to elucidate the structure of a chloroplast ribosome from the model organism most used to study chloroplast gene expression , C . reinhardtii . Using single-particle reconstruction from cryo-electron micrographs we have determined the structure of the C . reinhardtii chloroplast ribosome to a resolution of 15 . 5 Å . This structure shows that the chloroplast ribosome expands upon a core 70S-type bacterial ribosome structure with multiple chloroplast-unique domains . These chloroplast-unique structures are found on the small subunit of the ribosome near the mRNA entrance and exit channels . The potential role of these structures in translation regulation in the chloroplast is discussed , including their involvement in translation initiation via positioning of initiation mRNA–protein complexes ( mRNPs ) , and the potential involvement of these unique domains in the processivity of chloroplast translation .
Single-particle reconstruction was used to calculate a three-dimensional map of the chloroplast ribosome to a resolution of 15 . 5 Å ( as determined by 0 . 5 cutoff Fourier Shell Correlation criteria; Figure S2 ) . The chloroplast ribosome has clearly defined large and small subunits , and a distinct intersubunit space ( Figure 2 and Video S1 ) . Common features defined from bacterial 70S ribosome structures can also be identified on the chloroplast ribosome; the large subunit of the chloroplast ribosome has an easily distinguished central protuberance ( CP in Figure 2A ) , L1 arm ( L1 ) , and stalk ( ST ) . The small subunit has clear head ( h ) , body ( b ) , platform ( pt ) , shoulder ( sh ) , and spur ( sp ) domains . The small subunit also has clearly distinguishable additional structures on its solvent-exposed face that are not present on bacterial ribosomes; these include a large multilobed structure emerging in the vicinity of the mRNA exit channel and extending down across the body of the ribosome ( cuα , Figure 2A ) , additional connection of the head and beak regions ( cuβ ) , and a thickening of the shoulder region ( cuγ ) . The platform is also lifted slightly away from the body and towards cuα . On the large subunit , chloroplast-unique density extends from the base of the L1 arm back towards the center of the large subunit ( CUλ ) . There is also extensive connection between the L1 arm and nearby features on the body of the large subunit , but this is also seen to some degree in the Escherichia coli cryo-electron microscopy ( cryoEM ) maps ( Figure 2B; [29 , 30] ) and represents flexibility inherent to this part of the large subunit . Overall , interface surfaces and the tRNA and essential translation factor-binding regions located between the large and small subunits of the chloroplast ribosome appear highly similar with these same features in bacterial ribosomes , whereas solvent-exposed surfaces of the chloroplast ribosome show some striking differences from those of bacteria . It is difficult to distinguish individual bridges ( as defined in [29] and [31] ) in the central region of the intersubunit space ( i . e . , bridges B3 and B5 ) , but the bridging patterns appear conserved between E . coli and the chloroplast ribosome , with only a few exceptions . Bridges 1b and 1c are seen as a single bridge off the upper central protuberance , and bridge 4 is expanded in the chloroplast ribosome and makes contact with the lower body region of the small subunit ( unpublished data ) . The identification of these conserved bridges supports the idea that interactions between the large and small subunits are largely unchanged between chloroplast and bacterial ribosomes . Normal mode flexible fitting was used to fit bacterial ribosome crystal structure data to our chloroplast ribosome map ( see Materials and Methods ) . A difference map between this fitting and our chloroplast map reveals densities both unique to ( cu structures ) and lacking from ( mesh/ribbon in Figure 3 ) the chloroplast ribosome . A majority of the densities lacking from the chloroplast ribosome can be understood in light of proteomic and genomic data on the chloroplast ribosomal proteins [25] and comparison of predicted rRNA secondary structures ( Comparative RNA Web Site , http://www . rna . ccbb . utexas . edu; Figures S3 and S4 ) . For example , large subunit proteins L25 and L30 are clearly identified in a difference map as lacking from the chloroplast ribosome ( Figure 3A ) . These proteins were not identified in proteomic analysis of the C . reinhardtii chloroplast ribosome , nor were genes encoding these proteins identified in the completed C . reinhardtii nuclear genome sequence ( http://genome . jgi-psf . org/Chlre3/Chlre3 . home . html ) . There is no known function for either of these proteins on the ribosome [32] . The L29 protein was not identified via proteomics [25] , and an L29 homolog has yet to be found in the C . reinhardtii genome database , but density in the area where this protein is found on the bacterial ribosome is clearly present in the chloroplast ribosome structure . L29 is involved in interactions with trigger factor and SRP , both of which have homologs in the C . reinhardtii chloroplast [33 , 34] . It is likely that the small size of L29 precluded its identification via proteomics , and that remaining gaps in the genome sequence are harboring the gene for chloroplast L29 . Small rRNA helices are also lacking from the chloroplast ribosome in a number of places on both the small and large subunits ( Figure 3 ) . In each case , the absence of rRNA density corresponds to a small region of the rRNA that is not conserved between chloroplasts and bacteria ( Figures S3 and S4 ) . The regions of rRNA that differ between chloroplast and bacteria are not involved in any known function of the ribosome; they do not interact with antibiotics , are not associated with any aspect of translation initiation , and do not participate in intersubunit bridges [35–38] . Comparison with predicted secondary structure diagrams from both mitochondrial and 80S ribosomes indicates that all of these helices are found in regions of variability off the conserved rRNA core shared by all ribosomes [39] . The identification of structural differences that correspond exactly with our previous proteomic analysis and with predicted rRNA secondary structure differences gives us a very high degree of confidence that the map of the chloroplast ribosome that we have calculated is correct , and validates the chloroplast-unique densities that we identify as real and significant . Comparison of the chloroplast ribosome with cryoEM reconstructions of the E . coli ribosome reveals that the head of the chloroplast ribosome is rotated and tilted away from the large subunit by approximately 5° , which results in a slight lift of the beak ( Figure S5 ) . Connectivity between the beak and the shoulder in this area originates from chloroplast-unique density ( cuβ ) , whereas connections are only seen between the beak helix and the shoulder in E . coli ribosomes . This is similar to movements seen in eukaryotic ribosomes upon internal ribosome entry site ( IRES ) binding ( see Discussion ) . Similarity to the mitochondrial ribosome is also observed in the differences between chloroplast and bacterial large subunits . Like the chloroplast ribosome , mitoribosomes do not have L25 , and there is a crevasse between the central protuberance and the stalk side of the large subunit where this protein sits in bacteria [40] . This effect is smaller but similar on the chloroplast ribosome since the central protuberance is much expanded on the mitoribosome . In bacteria , the ribosomal E-site is commonly occupied by tRNA after purification , but we do not see this for our chloroplast ribosome . There is some evidence of partial occupancy at the factor-binding site of the chloroplast ribosome . The discontinuous density in the factor-binding site is not shown here , but may contribute to regions of large subunit density in the stalk base area and small subunit density on the back of the shoulder that appear extended into the intersubunit space , and also to cuε on the PSRP-7 antibody-bound map ( see below ) . Further computational separation of a larger dataset may allow us to calculate a map representing full occupancy at this site , and further proteomic analysis could verify the identity of the bound factor . Chloroplast-unique structures and changes near the mRNA exit and entrance channels dominate a comparison of E . coli and chloroplast ribosomes ( Figures 3 and 4 ) . Connectivity with rRNA or proteins that have bacterial homologs allows prediction of the identity of some of the novel structures found on the chloroplast ribosome . The largest region of chloroplast-unique density on the small subunit emerges from the neck region of the ribosome , adjacent to the mRNA exit channel , and extends down along the platform ( cuα; Figure 4A ) . This multilobed structure of approximately 90 kDa makes contact with the head and neck of the small subunit , and partially overlaps the positions of S1 and S2 on bacterial ribosomes ( compare Figure 4A and 4C ) . The upper lobe of cuα limits access to the mRNA exit channel to about 25 Å from both side and top . The mRNA exit channel is the site of initial interactions between mRNAs and the ribosome; and in bacteria , this is the site of the S-D interaction that positions the start site AUG of the mRNA at the P-site of the ribosome [21 , 22 , 41 , 42] . Below cuα at the mRNA exit channel and following down the underside of the chloroplast-unique density , there is an extended trough on the chloroplast ribosome , accentuated by the lifting of the platform domain ( Figure 4B ) . Proteins S21 , near the mRNA exit channel , and S1 , S2 , and S5 are partially displaced from their positions on the bacterial ribosome by the chloroplast ribosome trough . These displacements may indicate movement of these proteins into cuα . Connectivity of cuα with the main body of the small subunit of the ribosome suggests that S1 and the chloroplast-unique domain of S2 comprise the majority of cuα . Chloroplast S1 is the only small subunit protein that is significantly smaller than its bacterial homolog ( Table S1 ) , but like bacterial S1 , binds mRNA [43] . Chloroplast S2 has a large chloroplast-unique amino-terminal extension [26] , more than doubling its size compared to bacterial S2 ( 63 kDa vs . 27 kDa , Table S1 ) ; two TRAM domains in this addition give chloroplast S2 the potential to bind RNA [44] . In ultraviolet ( UV ) cross-linking experiments , both of these proteins are strongly labeled by a radiolabeled mRNA 5′ UTR ( Figure 5 ) . Also labeled were L1 and an incompletely denatured protein complex containing at least S5 and PSRP-7 , the other two large chloroplast-unique proteins on the small ribosomal subunit . In the same experiment using E . coli ribosomes , only S1 is strongly labeled ( Figure 5 ) . The mass of cuα is estimated at 90 kDa , which adds over 10% greater mass to the small subunit of the chloroplast ribosome compared with the E . coli 30S subunit , and allows for S1 ( 44 kDa ) and S2 to be contained within this structure . Given proximity to the mRNA exit channel and the RNA-binding properties of S1 and S2 , cuα is perfectly situated to act as a landing pad for chloroplast initiation complex mRNPs . Interaction between these mRNPs and the ribosome could be utilized to position mRNAs , both with and without S-D sequences , for translation initiation . Another large region of chloroplast-unique density on the small subunit is found in the beak and head region of the ribosome , adjacent to the mRNA entrance channel ( cuβ; Figure 6A ) . This is the first surface of the ribosome that coding regions of mRNA encounter during translation , and proteins in this region are important for helicase activity of the bacterial ribosome [45] . cuβ connects the beak helix ( h33 ) with S3 and S10 ( see Figure 3C ) , and approaches the mRNA entrance channel at the front underside of the beak . Chloroplast S3 has a large internal chloroplast-unique domain , as well as good homology with bacterial S3 at its N- and C-termini , which together predict that the S3 chloroplast-unique domain comprises cuβ ( compare predicted location of S3 cu domain in Figure 1 with cuβ in Figure 6 ) . In an attempt to localize the largest plastid-specific ribosomal protein , PSRP-7 , chloroplast ribosomes were incubated with PSRP-7 antibody prior to freezing and imaging . A separate reconstruction from this data yielded a map at 19 . 4 Å , and revealed additional structure on the solvent-exposed face of the small subunit ( Figure 6B and 6C ) . Most of this additional density stems from chloroplast-unique structures already defined on the unliganded chloroplast ribosome , which are expanded in this antibody-bound map . A few regions of density that do not correspond to densities on the unliganded structure may represent the bound antibody ( Figure 6C ) . These densities are found both emerging from the expanded shoulder of the small subunit ( cuδ ) , and from the head of the small subunit and towards the factor-binding site at the subunit interface ( cuε ) . cuε may also be related to the protein occupancy at the factor-binding site , because visualization at lowered thresholds reveals that cuε is contiguous with density in the factor-binding site . Antibody-binding appears to stabilize chloroplast-unique structures on the small subunit , particularly cuα , and at very low thresholds connects the mid-region of cuα with the tip of cuδ ( asterisk in Figure 6B ) . The tip region of cuδ is visualized at very low thresholds on the unliganded chloroplast ribosome map , which further suggests that antibody binding is stabilizing part of the chloroplast-unique density on the surface of the small subunit . cuδ extends toward the head parallel to cuα , and lies across the line of direct access to the mRNA entrance channel ( Figure 6C ) . Chloroplast-unique structures near the mRNA entrance channel—via S3 , which is involved in helicase activity in bacterial ribosomes [45] , or PSRP-7 , which binds mRNA ( Figure 5 and [46] ) —are likely involved in recognizing structured elements in coding regions of chloroplast mRNAs and may act to alter the processivity of translation . These structures may also be involved in mRNA positioning for translation initiation , in analogy to the mRNA gate structure on the mitochondrial ribosome [40] . Mammalian mitochondrial mRNAs do not have 5′ UTRs [47 , 48] , and the mRNA gate is hypothesized to function in the proper positioning of these leaderless messages for translation initiation [40] . Antibody-binding stabilization of at least cuα indicates that there is flexibility in these chloroplast-unique structures and that , because of this flexibility , the full extent of these features is not yet resolved in our structure . Lowering the threshold visualization levels of either the bound or the unbound maps reveals additional density near the mRNA exit channel , extending up along the head and occluding access to the channel ( unpublished data ) , suggesting that alterations in this area must occur to provide mRNAs access to the small subunit of the ribosome . Chloroplast ribosomes imaged in complex with mRNA , tRNA , and protein factors may be needed to fully resolve structures in this area .
This is the first report of the structure of a chloroplast ribosome , and comes from the organism from which the majority of information on plastid translation has been derived ( C . reinhardtii ) . The translation machinery in chloroplasts is clearly based on a prokaryotic-like core , though translation in eukaryotic plastids is more complex than in bacteria from both regulatory and physical perspectives . A large body of research has shown that translation is the key regulated step in chloroplast gene expression [49] . These studies have identified many key events in chloroplast gene expression: interactions of individual photosynthetic proteins with their own and partner protein mRNAs , the formation of mRNPs between nuclear-encoded translation factors and the 5′ UTRs of mRNAs , and the effects of light-induced signals on mRNP formation and translation initiation [7 , 9 , 10 , 12 , 13 , 50–52] . Structural analysis of the chloroplast ribosome and identification of chloroplast-unique structures on the ribosome provide an important understanding of the physical components utilized for translation regulation in this organelle . Chloroplast-unique structures dominate the solvent-exposed face of the small subunit , and approach both the mRNA entrance and exit channels . These structures are ideally situated to regulate translation initiation , and genetic and biochemical data suggest that these structures accompany and complement the use of modified S-D sequences and translation initiation mRNP formation . Proteomic studies identified chloroplast-unique ribosomal proteins , primarily on the small subunit of the ribosome [26] ( Tables S1 and S2 ) . The structure presented here allows us to visualize these chloroplast-unique proteins as novel structural domains on the chloroplast ribosome . The large subunit of the chloroplast ribosome differs from the bacterial 50S subunit by only a few proteins , and we see only one significant chloroplast-unique region on this subunit ( CUλ; Figures 2A and 3B ) . The primary function of the large subunit of the ribosome is peptide bond formation , and this most basic function of the ribosome has been conserved between eukaryotic , bacterial , and organellar ribosomes . Here , we confirm this expected structural conservation in the core of chloroplast ribosomes . The small ribosomal subunit is responsible for interactions with mRNAs and initiation factors that position messages for translation initiation [21] , and it also has the duty of quality control in codon decoding during translation . Regions of the small subunit that are responsible for decoding and quality control are structurally conserved with bacterial ribosomes , whereas the chloroplast-unique additions are seen on the small subunit of the ribosome in areas that intersect the path of mRNA during translation initiation , the key regulated step of chloroplast translation . The large chloroplast-unique structure found near the mRNA exit channel and extending down along the platform of the small subunit ( cuα; Figures 2–4 ) is located near the site of binding for S1 in bacteria . S1 is the only ribosomal protein to bind mRNAs in bacteria , and it binds to mRNAs and the ribosome through six repeats of an RNA-binding motif; the S1 protein in chloroplasts has only three RNA-binding motifs . The additional domains on S2 and the chloroplast-unique protein PSRP-7 both contain RNA-binding domains that may complement the smaller chloroplast S1 protein in mRNA binding ( Figure 5 ) . The S-D interaction between bacterial mRNAs and the 16S rRNA also occurs in the mRNA exit channel area , and functions to position mRNAs for translation initiation . As mentioned previously , S-D sequences in chloroplast mRNAs do not share the bacterial consensus spacing from the start site AUG . This difference in spacing requires a fundamentally different mechanism for bacteria and chloroplasts to position mRNAs for translation initiation , and suggests that the additional chloroplast-unique structure located at the mRNA exit channel may function as adapters that positions chloroplast mRNAs properly for initiation . cuα is perfectly situated to function as this adapter for interactions between chloroplast initiation complex mRNPs and the chloroplast ribosome . Programmed pausing has been observed in the translation of several chloroplast mRNAs , and in the case of D1 protein , this pausing is intimately associated with assembly of the nascent polypeptide with cofactors and partner subunits into the thylakoid membrane [16] . In bacteria , side chains from S3 form part of the lining of the mRNA entrance channel [22] , and mutation to S3 affects the ability of the bacterial ribosome to unwind downstream coding-region secondary structure for ribosome translocation along an mRNA [45] . Interactions between coding regions and 5′ UTRs of chloroplast mRNAs also impact translation efficiency , and these interactions can be modified by proteins binding to the 5′ UTR of the mRNA [53 , 54] . The locations of cuβ and cuδ near the mRNA entrance channel ( see Figure 6 ) , combined with the mRNA-binding properties of PSRP-7 , allow for interactions between these ribosomal proteins and coding regions of mRNAs that may assist positioning during translation initiation , or that recognize structured elements in coding regions of mRNAs and modify processivity during translation elongation . That cuε reaches from the beak into the factor-binding site , and that elongation factor Ts is covalently linked to the ribosome through PSRP-7 in many chloroplasts ( as the PETs polyprotein [46] ) , suggest possible involvement in programmed pausing through modification of ribosome function in this important region . Modified ribosome structures that are thought to impact translation initiation have also been identified in other organisms . A large structural element was found adjacent to the platform on the small subunit of the 80S-type ribosome from trypanosome [55] . The rRNA responsible for this structure is found in expansion segments of the small subunit rRNA that are found only in trypanosomes . Trypanosome mRNAs are also unique in that they all receive the same 5′ UTR through transsplicing , and interactions between the 5′ UTR and the ribosome are required for translation [56] . The novel structure on the trypanosome ribosome is implicated in translation initiation by virtue of its proximity to the mRNA exit channel , and also its potential to interact through base pairing with conserved regions in the trypanosome mRNA 5′ UTR [55] . The structure of the hepatitis C virus ( HCV ) IRES element complexed to the human 40S ribosomal subunit also revealed a structure similarly situated to cuα [57] . This IRES is used for positioning viral mRNAs with their start site AUG at the P-site of the ribosome in the absence of cellular translation factors [58] . The collective movements of the 40S subunit head upon IRES binding are quite similar to those seen between chloroplast and bacterial ribosomes . It has been suggested that these movements promote formation of preinitiation complexes in the absence of canonical initiation factors [57 , 59] . Each of these systems has highly regulated translation initiation , which suggests that structural adaptation to the ribosome for specialized translation regulation may be quite ubiquitous in nature . The small subunit of the ribosome has evolved and adapted as a means to regulate translation initiation , whereas the large subunit of the ribosome is more evolutionarily stable , maintaining the basic function and integrity of the core reactions of peptide bond formation and nascent peptide delivery . The chloroplast ribosome structure and mRNA cross-linking presented here have allowed us to propose a mechanistic model for chloroplast translation initiation ( Figure 7 ) . Such a model for bacterial translation is quite simple ( as in Figure 1 ) : mRNAs , even as they are being transcribed , are positioned via S1 binding and the S-D interaction with their start site AUG in the P-site of the ribosome ready for initiation . For this reason , bacterial initiation is dominated by accessibility of the S-D element and its physical spacing from the start site AUG . Plastid mRNAs are normally unable to access the ribosome on their own ( Figure 7 , panel 1 ) , and require binding of nuclear-encoded proteins to activate translation ( panel 2 ) . We propose that cuα acts as a landing pad for initiation complex mRNPs as a first stage of mRNA interaction with the chloroplast ribosome ( panel 3 ) . Via this interaction , mRNAs with divergently spaced S-D sequences , or without S-D sequences , can be placed such that their start site AUG is correctly positioned in the ribosomal P-site for translation initiation . Interactions between chloroplast mRNA coding regions and 5′ UTRs may be sensed or accommodated by cuβ and cuδ near the mRNA entrance channel , and these domains may also assist in positioning of the start site AUG for initiation . Additionally , during translation , these chloroplast-unique structures may recognize sequence-specific or secondary-structured mRNA elements ( Figure 7 , panel 4 ) and communicate this to the ribosome in the form of modification of the processivity of translation or translocation . Such interactions would explain programmed pausing during translation that allow for proper membrane or cofactor association of nascent polypeptide chains . The chloroplast ribosome structure presented here will allow for focused experimental design to examine interactions of ribosomal proteins and plastid mRNAs , as well as the role that these interactions play in translation initiation in the chloroplast . A clearer picture of the physical interactions involved in translation initiation—between mRNAs , their associated proteins , and the chloroplast ribosome—will also assist in designing more appropriate transgenes for increased recombinant protein expression in chloroplasts . This structure will also serve as a complement to studies on the basic mechanisms of translation that have traditionally used bacteria as a model .
C . reinhardtii cultures , strain cc3395 , were grown to mid-log phase , harvested , and disrupted using a nitrogen bomb at 600 psi in buffer containing 25 mM Tris-HCl ( pH 8 . 0 ) , 25 mM KCl , 25 mM MgCl2 , 5 mM DTT , 0 . 05 mM spermine , 2 mM spermidine , 1% Triton X-100 , 2% polyoxyethylene 10 tridecyl ether . Lysates were cleared at 40 , 000×g prior to sucrose gradient centrifugation . Gradients were made in the above buffer ( minus detergents ) at 25%–45% sucrose with a 10% sucrose step on top . Sucrose gradients were overlaid with cleared cell lysate and centrifuged at 100 , 000 × g for 18 h . Fractions were collected down the gradients and monitored by SDS-PAGE and RNA gel staining . Chloroplast ribosomes and 80S cytosolic ribosomes partially copurify , so gradient fractions containing detectable cytosolic ribosomes were omitted from further processing . Fractions containing chloroplast ribosomes were then diluted in sucrose-free buffer ( as above , minus detergents ) , and collected by ultracentrifugation at 250 , 000×g . Ribosome pellets were resuspended ( buffer as above , minus detergents ) , snap frozen in liquid nitrogen , and stored at −80 °C until use . Ribosomes , or ribosomes that had been incubated with PSRP-7 antibody , were applied to 300-mesh copper grid covered with a continuous carbon substrate that had been plasma cleaned for 20 s using a Fischione model 1020 plasma cleaner ( E . A . Fischione Instruments , http://www . fischione . com/ ) . Grids were blotted and plunged into liquid ethane through the use of a Vitrobot ( http://www . vitrobot . com; FEI Company , http://www . fei . com ) . The Leginon system [60 , 61] was used for the automated acquisition of images . All images were collected on a Philips Tecnai F20 ( FEI Company ) operating at 120 keV equipped with a Gatan cryospecimen holder ( http://www . gatan . com ) . Images used for reconstruction were collected under low-dose conditions ( <11 e−/Å2 ) to a 4k × 4k Tietz CCD camera ( Tietz Video and Image Processing Systems , http://www . tvips . com ) at a magnification of 50 , 000× corresponding to a pixel size of 2 . 263 Å . ACE ( Automated CTF Estimation ) software was used to calculate the contrast transfer function ( CTF ) of micrographs [62] . Of 392 defocus pairs , 324 near-to-focus micrographs , with a defocus range from 0 . 7 to 2 . 2 μM under focus , contributed particles to the reconstruction . A small set of particles ( 8 , 882 ) were hand picked using the BOXER function of EMAN software [63] . These particles were used to construct a template for automated particle picking , and were also used in a reconstruction and refinement . This initial refinement used a bacterial ribosome crystal structure ( 1PNX and 1PNY [64] ) as a starting model , and proceeded for six rounds over which the angular increment for projections was decreased from 10° to 6° . After automated particle picking on far-from-focus micrographs , a total of 101 , 512 particles were selected from near-to-focus pairs , and CTF was corrected by flipping the phases . The entire dataset was subjected to one round of refinement using the map calculated from the small dataset as a starting model . CORAN analysis and hierarchical clustering using SPIDER [65] then allowed for classification of the dataset , and separation of intact chloroplast ribosome images from dissociated subunit images ( see Figure S1B ) . A total of 42 , 934 particles were used in the final reconstruction and refinement that produced the map presented here . EMAN refinement proceeded for 12 rounds , with a decreasing angular increment for projections from 8° to 4° . The resolution of the map presented here was estimated from the Fourier Shell Correlation curve using a 0 . 5 cutoff value ( Figure S2 ) . Processing of antibody-bound ribosomes proceeded as above . Two hundred thirty-two of 416 micrographs , with a defocus range from 1 . 2 to 2 . 5 μM under focus , contributed particles the reconstruction , and an initial pool of 44 , 748 particles were subjected to one round of refinement in EMAN using the map from the small dataset described above as a starting model . A total of 14 , 866 particles passed through the CORAN analysis and hierarchical clustering , and were subjected to further refinement . Refinement was productive only through an angular increment for projections of 6° . The resolution of the map presented here was estimated from a Fourier Shell Correlation curve using a 0 . 5 cutoff , and is 19 . 4 Å ( Figure S2 ) . Normal mode flexible fitting ( NMFF ) [66 , 67] was used to fit E . coli ribosome crystal structure data ( RCSB Protein Data Bank [PDB] IDs 1VS7 and 1VS8 [68] ) to the chloroplast ribosome map . The large subunit proteins that do not have chloroplast ribosome homologs , and the small subunit proteins with large chloroplast-unique domains , were omitted from the fitting , and could be reintroduced at the all-atom model fitting stage . Individual chains were treated as phosphate or c-alpha atoms only ( coarse-grained model ) , and each protein chain was treated as a rigid block . A maximum displacement of 2 Å per iteration was allowed , and motions were considered along ten degrees of freedom ( the lowest frequency normal modes from the elastic network normal mode calculations ) . After over 150 iterations , NMFF brought the correlation coefficient of the fit from 0 . 38 ( a rough hand docking was used as a starting position ) to 0 . 56 for the coarse-grained model . The final all-atom model was constructed by a rigid-body fitting of 1VS7 and 1VS8 to the final coarse-grained model , and yielded a structure with 0 . 664 correlation with the chloroplast ribosome map . Further refinements allowing additional flexibility in the rRNAs were explored; however , little improvement over the initial fit was achieved . S1 position on the bacterial ribosome was recreated using differences between PDB ID 2AVY and an E . coli cryoEM reconstruction [29] . Molecular graphics images were produced using UCSF Chimera [69] . E . coli ribosomes for cross-linking were prepared via sucrose gradient separation similar to that described for chloroplast ribosomes , with a few modifications . Late log-phase BL21 cells were broken via freeze/thaw in buffer containing 10 mM Tris-HCl ( pH 8 . 0 ) , 50 mM KCl , 10 mM MgCl2 , 2 mM DTT , 25 mM EGTA , and 1 . 6 μg/μl lysozyme . Gradients used for ribosome separation were 10%–40% sucrose in 25 mM Tris-HCl ( pH 8 . 0 ) , 25 mM KCl , 25 mM MgCl2 . All other steps were as described above . RNA was transcribed using T7 RNA polymerase with the incorporation of [α-P32]-uridine 5′-triphosphate . The entire 91-nt psbA 5′ UTR in its unprocessed form was used in this experiment . UV cross-linking reactions were carried out in the presence of 150 mM Tris-HCl ( pH 7 . 0 ) , 250 mM KCl , 25 mM MgCl2 , 25 mM DTT . Reactions were exposed to 2 × 600 mJ of UV radiation , after which the RNA was digested prior to separation of ribosomal proteins via denaturing SDS-PAGE . Gels were stained with Coomassie Brilliant Blue to visualize protein bands and then exposed to phosphorescent screens to visualize radiolabeled bands . Gel slices were cut from SDS-PAGE gels , and trypsinized peptides were prepared from the gel slices according to [70] . Mass spectrometry was performed as described in [71] .
The RCSB Protein Data Bank ( http://www . rcsb . org/pdb/home/home . do ) accession numbers for E . coli ribosome proteins discussed in this paper are 1VS7 , 1VS8 , and 2AVY .
|
Translation of mRNA into protein is the main step for the regulation of gene expression in the chloroplast , the photosynthetic organelle of plant cells . Translation is conducted by the ribosome , a large macromolecular machine composed of RNA and protein . Studies have shown that the composition of the chloroplast ribosome is similar to that of bacterial ribosomes , but also that chloroplast ribosomes contain a number of unique proteins . We present the three-dimensional structure of the chloroplast ribosome , as calculated using cryo-electron microscopy and single-particle reconstruction . Chloroplast-unique structures are clearly visible on our ribosome map , and expand upon a basic bacterial ribosome-like core . The role of these chloroplast-unique ribosomal proteins in regulating translation of chloroplast mRNAs , including light-regulated translation , is suggested by the location of these structures on the ribosome . Biochemical data confirm a predicted function in chloroplast translation for some of the unique proteins . Our model for translation in the chloroplast incorporates decades of biochemical and genetic studies with the structure presented here , and should help guide future studies to understand the molecular mechanisms of translation regulation in the chloroplast .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods",
"Supporting",
"Information"
] |
[
"biochemistry",
"cell",
"biology",
"in",
"vitro",
"molecular",
"biology"
] |
2007
|
Structure of the Chloroplast Ribosome: Novel Domains for Translation Regulation
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The development of sex-specific traits , including the female-specific ability to bite humans and vector disease , is critical for vector mosquito reproduction and pathogen transmission . Doublesex ( Dsx ) , a terminal transcription factor in the sex determination pathway , is known to regulate sex-specific gene expression during development of the dengue fever vector mosquito Aedes aegypti . Here , the effects of developmental siRNA-mediated dsx silencing were assessed in adult females . Targeting of dsx during A . aegypti development resulted in decreased female wing size , a correlate for body size , which is typically larger in females . siRNA-mediated targeting of dsx also resulted in decreased length of the adult female proboscis . Although dsx silencing did not impact female membrane blood feeding or mating behavior in the laboratory , decreased fecundity and fertility correlated with decreased ovary length , ovariole length , and ovariole number in dsx knockdown females . Dsx silencing also resulted in disruption of olfactory system development , as evidenced by reduced length of the female antenna and maxillary palp and the sensilla present on these structures , as well as disrupted odorant receptor expression . Female lifespan , a critical component of the ability of A . aegypti to transmit pathogens , was also significantly reduced in adult females following developmental targeting of dsx . The results of this investigation demonstrate that silencing of dsx during A . aegypti development disrupts multiple sex-specific morphological , physiological , and behavioral traits of adult females , a number of which are directly or indirectly linked to mosquito reproduction and pathogen transmission . Moreover , the olfactory phenotypes observed connect Dsx to development of the olfactory system , suggesting that A . aegypti will be an excellent system in which to further assess the developmental genetics of sex-specific chemosensation .
Most animal species display sexually dimorphic behaviors , the majority of which are linked to sexual reproduction [1] . Disease vector mosquitoes are excellent subjects for studies that explore the biological basis of sexual dimorphism . Only adult female mosquitoes , which require blood meals for reproduction , bite humans and transmit pathogens . Females differ from males in morphological , physiological , and behavioral traits that are critical components of their ability to spread diseases , including feeding behaviors , longevity , and susceptibility to infections . Researchers have therefore had a long-standing interest in the potential to manipulate genetic components of the sex determination pathway and sexual differentiation for vector control . Moreover , success of the sterile insect technique ( SIT ) and other genetic strategies designed to eliminate large populations of mosquitoes is dependent upon efficient sex-sorting of males and females , and many have argued that such sex-sorting , as well as insect sterilization itself , is best achieved through large-scale genetic or transgenic approaches ( reviewed by [2 , 3] ) . Although the genes that regulate sex-specification and development of mosquito sexual dimorphism may represent novel targets for vector control , most of these genes have not yet been functionally characterized in vector mosquitoes . Research in Drosophila melanogaster identified a mutation in the doublesex ( dsx ) gene that transformed males and females into intersexes [4] . Subsequent molecular analyses demonstrated that the dsx gene encodes a key terminal transcription factor in the sex-determination pathway that controls Drosophila male and female sexual differentiation [5–7] . Drosophila dsx pre-mRNAs are spliced in a sex-specific manner [8 , 9] , generating male ( DsxM ) and female ( DsxF ) proteins with a common N-terminus and DNA-binding domain , but distinct male and female C-termini that differentially regulate sex-specific gene expression ( reviewed by [10 , 11] ) . Studies in diverse insects have demonstrated that although primary signals for sex determination vary within the insect order [12] , all relay their signal through the sex-specific splicing of dsx , which plays a well-conserved role as a transcription factor that regulates expression of downstream target genes which contribute to sexual differentiation [13–25] . The roles of Dsx have been particularly well studied in a variety of beetle species [17 , 18 , 19 , 21 , 26] . For example , sex-specific Dsx splice forms are known to regulate sexually dimorphic exaggerated horn development in two species of beetles , Onthophagus taurus [18] and the rhinoceros beetle Trypoxylus dichotomus [19] . Dsx function was also characterized in the red flour beetle Tribolium castaneum , in which it is required for oocyte development , egg production , and egg hatching [17] . More recently , Dsx was shown to regulate sexually dimorphic mandible development in the stag beetle Cyclommatus metallifer [21] . Here , we examine the function of dsx during development of the disease vector mosquito Aedes aegypti , which exhibits innate sexually dimorphic behaviors that contribute to the transmission of dengue , yellow fever , and chikungunya viruses [27] . Salvemini et al . [28] detected male ( dsxM ) and female ( dsxF ) splice variants of dsx in A . aegypti . Recently , Hall et al . [29] described characterization of a male-determining locus ( M-locus ) gene , Nix , a male-determining factor ( M factor ) in A . aegypti that is required and sufficient to initiate male development , and which encodes a potential splicing factor . The absence of Nix shifts the alternative splicing of dsx toward the female-specific dsxF splice form , suggesting that Nix normally promotes splicing of dsxM . Although the sex-specific dsx splice forms likely direct sexually dimorphic mosquito development , functional analysis of dsx in A . aegypti is lacking . In a recent study from our laboratory [30] , we detected sex-specific dsx expression in the pupal brain suggesting that sexually dimorphic neural development in A . aegypti may require dsx function . In support of this , a search of the A . aegypti genome sequence uncovered 732 Dsx consensus binding sites , 48 of which flank dimorphically expressed genes identified in male vs . female pupal head transcriptome microarray experiments [30] . A . aegypti genes flanked by Dsx consensus binding sites group under a number of significant gene ontology terms , many of which are linked to neurological processes or neural development , suggesting that Dsx may regulate sex-specific gene expression in the developing mosquito brain . To examine this , we [30] used small interfering RNA ( siRNA ) -mediated gene targeting to silence dsx during A . aegypti pupal development . In our study , siRNAs corresponding to different target sequences in exon 2 , which is common to male and female splice variants , were injected into A . aegypti pupae . These targeting experiments demonstrated that Dsx is required for the regulation of sex-specific gene expression during A . aegypti neural development . The results of our initial investigation [30] , in conjunction with studies performed in a variety of insects ( reviewed by [24] ) , support the hypothesis that Dsx regulates the development of sex-specific characters in A . aegypti . Here we test this hypothesis by examining the impact of larval and pupal dsx silencing on the development of sex-specific traits in adult female mosquitoes . Whyard et al . [31] recently used RNA interference ( RNAi ) to target the female-specific isoform of A . aegypti dsx ( dsxF ) during development . Their dsx targeting protocol differed from that which we used [30] in that they used longer pieces of dsRNA ( as opposed to siRNA ) targeting the two female-specific dsx exons ( as opposed to exon two , which is common ) that was delivered by soaking mosquito larvae in dsRNA or by feeding the larvae E . coli expressing dsx-targeting dsRNA ( rather than through microinjection ) . Although the Whyard et al . [31] dsx targeting strategies resulted in highly male-biased populations of mosquitoes , the number of dsx dsRNA-treated larvae that developed into adults was halved relative to the negative controls , and with no significant increase in the number of males observed , suggesting that the majority of females simply failed to survive to adulthood , which did not permit analysis of sex-specific characters in these animals . Our ability to analyze late pupae microinjected with siRNAs targeting a separate region of the dsx gene ( exon two ) suggested that this targeting strategy [30] , which differs from the Whyard et al . [31] procedure as noted above , may facilitate analysis of adult female phenotypes . Such analyses are of great interest given that adult females are responsible for transmission of pathogens that result in human diseases . Indeed , we found that use of a pupal microinjection procedure to deliver siRNA targeting exon 2 [30] , as well as the use of chitosan nanoparticles [32–34] to deliver these same exon 2 targeting siRNAs to A . aegypti larvae , silenced dsx while permitting female survival . Use of these targeting strategies allowed us to examine adult female morphological , physiological , and behavioral phenotypes that result from developmental silencing of dsx .
The A . aegypti Liverpool-IB12 ( LVP-IB12 ) strain ( from D . W . Severson , Notre Dame , IN ) , from which the genome sequence [35] was generated , was used in this investigation . The mosquitoes were reared as described [36] , except that an artificial membrane sheep blood ( HemoStat Laboratories , Dixon , CA ) feeding system was utilized . Mosquitoes were maintained in an insectary at 26°C , at ~80% humidity , and under a 12 hr light/12 hr dark cycle with 1 hr crepuscular periods at the beginning and end of each light cycle . Mosquito larvae were fed on a suspension of dried beef liver powder , and adults were provided cotton soaked with 10% sugar solution . Pupae were sexed on the basis of differing pupal tail morphology as described by Christophers [37] . Adults were sexed on the basis of external characters [37] . Sexes of dsx-silenced adults were further confirmed in a subset of animals through dissection to assess the presence of testes or ovaries . In situ hybridization was performed as previously described [38] . Riboprobes corresponding to the following genes were synthesized according to the Patel [39] protocol: OR 2 ( AAEL005999 ) , OR 9 ( AAEL006005 ) , OR 62 ( AAEL011796 ) , and OR 123 ( AAEL017537 ) and dsx ( AAEL009114; probe corresponded to exon 2 which is common to males and females ) . At least 20 tissue specimens were processed for each in situ hybridization experiment , and at least two replicate experiments were performed . A sense riboprobe was used as a control in all hybridization experiments . Immunohistochemical staining was performed as described previously [33 , 40] using Texas Red-X Phalloidin and TO-PRO-3 iodide , both which were obtained from Molecular Probes ( Eugene , OR ) . Following processing , the tissues were mounted and imaged on a Zeiss 710 confocal microscope using Zen software , and images were analyzed with FIJI ImageJ and Adobe Photoshop CC 2014 software . For OR transcript analyses , mean gray values ( average signal intensity over the selected area ) were calculated for digoxigenin-labeled OR transcript signal in 25 control or experimental antennae combined from two replicate experiments . Data were statistically analyzed using one-way ANOVA followed by the Bonferroni post hoc test . Targeting of dsx ( AAEL009114 ) was performed as described previously [30] . Two siRNAs , dsx-KD A and dsx-KD B , that correspond to different target sequences in exon 2 , which is common to both the male and female dsx splice variants , were used in dsx silencing experiments . The sequences of these siRNA duplexes , which were purchased from Integrated DNA Technology ( IDT , Coralville , IA ) and confirmed through BLAST searches to have no significant homology to A . aegypti genes other than dsx , are as follows: Dsx-KD A: 5’ rCrArGrGrArArCrArGrArCrGrArCrGrArArCrUrUrGrUrCAA3’ / 5’rUrUrGrArCrArArGrUrUrCrGrUrCrGrUrCrUrGrUrUrCrCrUrGrArG3’ , and Dsx-KD B: 5’rCrArArGrArUrCrGrCrUrGrGrArUrGrGrUrArArArGrArUGT3’ / 5’rArCrArUrCrUrUrUrArCrCrArUrCrCrArGrCrGrArUrCrUrUrGrCrG3’ . All phenotypes were confirmed following knockdown ( KD ) with both dsx-KD A and dsx-KD B , suggesting that none of the phenotypes reported herein were the result of off-site targeting by either siRNA . A scrambled version of dsx KD B , an siRNA duplex lacking significant sequence homology to any genes in the A . aegypti genome , was used for control experiments: 5’rGrArArGrArGrCrArCrUrGrArUrArGrArUrGrUrUrArGrCGT3’ / 5’rArCrGrCrUrArArCrArUrCrUrArUrCrArGrUrGrCrUrCrUrUrCrCrG3’ . None of the phenotypes reported were observed in control-injected animals , which were not significantly different than wild type animals for any of the phenotypes assessed . siRNA was microinjected into pupae [30] as described previously . Chitosan/siRNA-mediated targeting of dsx was performed using the procedure described by Mysore et al . [33] , which was adapted from Zhang et al . [32] and is described in detail in Zhang et al . [34] . Silencing of dsx was confirmed through in situ hybridization as discussed in the recent siRNA-mediated dsx gene targeting study [30] . To quantify knockdown levels , mean gray values were calculated as described [41] for digoxigenin-labeled dsx transcript signal in brains and antennae from minimally 20 control or experimental specimens combined from two separate replicate experiments . These data were statistically analyzed using one-way ANOVA followed by the Bonferroni post hoc test . Wing length and area , proboscis , antenna , and maxillary palp lengths were assessed in females following chitosan/siRNA nanoparticle mediated targeting of dsx as described above . For these experiments , structures were dissected from sugar-fed ~10 day old adult female mosquitoes , mounted and analyzed with a Zeiss Axioimager equipped with a Spot Flex camera . Areas and lengths were measured using Fiji Image J software . Wing lengths were measured from the apical notch to the axillary margin , excluding the wing fringe as described in [37] . To minimize measurement errors , all appendage measurements were determined by a single researcher . Data from at least four replicate experiments were combined for statistical comparisons , which were performed using Graphpad Prism 6 software with one-way ANOVA followed by the Bonferroni post hoc test . Maxillary palp and antennal sensillary morphology was further assessed ( following pupal microinjection of siRNA ) through scanning electron microscopy ( SEM ) with an FEI-MAGELLAN 400 FESEM as previously described [42] . Briefly , female heads were placed in acetone for 24 hrs and subjected to critical point drying followed by sputter coating with gold/Iridium . Control vs . dsx-KD olfactory structures were visualized under SEM and assessed for numerical and structural anomalies . Data from replicate experiments were combined and statistically analyzed with one-way ANOVA followed by the Bonferroni post hoc test . Blood feeding behavior was visually assessed through analysis of engorged female abdomens following plasma membrane blood feedings which were one hour in duration . The number of eggs produced per female ( fecundity ) and eggs produced per female that generated first instar larvae ( fertility ) were assessed as described by Hill et al . [43] . The number of fertile females , which served as evidence of successful mating , was also recorded . Survivorship , which was monitored in individual females following completion of the fecundity assays , was performed and analyzed as described by Hill et al . [43] . Ovary length was assessed in four day-old adults prior to blood feeding , as well as in 10 day post blood-fed females . The number of follicles was assessed in four day-old adults prior to blood feeding , while ovariole number and length was assessed five days post blood feeding . For these assays , ovaries were immunohistochemically processed as described above , mounted , and then analyzed with a Zeiss 710 confocal microscope using Zen software . Scanned images were analyzed using FIJI and Adobe Photoshop CC 2014 software . With the exception of lifespan , which was analyzed using Kaplan-Meier survival curves , data were analyzed using Graphpad Prism 6 software with one-way ANOVA followed by the Bonferroni post hoc test . Introns as well as 5’ flanking sequences 0–5 kb upstream of the open reading frames of A . aegypti OR genes were exported from VectorBase [44] . These sequences were searched ( using ClustalW ) for the Clough et al . [45] Dsx consensus binding site sequence: VHHACWAWGWHDN . Sequences with no more than one mismatch are reported . The following genes were studied in this investigation: dsx ( AAEL009114 ) , OR 2 ( AAEL005999 ) , OR 9 ( AAEL006005 ) , OR 62 ( AAEL011796 ) , and OR 123 ( AAEL017537 ) .
siRNA-mediated gene targeting , which was employed in a recent analysis of dsx function in the developing A . aegypti brain [30] , was used to silence dsx during A . aegypti larval and/or pupal development . For larval silencing experiments , dsx-KD A , dsx-KD B , or control chitosan/siRNA nanoparticles were fed to larvae [34] . Silencing of dsx in chitosan/siRNA nanoparticle-fed or siRNA-injected animals was confirmed through in situ hybridization experiments . Quantification of dsx signal through mean gray value analyses in control vs . dsx-silenced animal tissues indicated that significant knockdown levels were achieved ( S1 Fig ) . A summary of phenotypes assessed in this study and the delivery method used for analysis of each phenotype is provided in S1 Table . For analysis of structures/traits that develop in the late pupal stage , dsx-KD A or dsx-KD B siRNA was delivered through pupal microinjection , which generates more effective silencing at the late pupal stage , at which time some recovery of dsx expression is observed in animals that were fed chitosan/siRNA nanoparticles as larvae ( S1 Fig ) . Chitosan/siRNA larval dsx targeting experiments did not impact animal survival to adulthood ( Table 1 , n = 50 per control or experimental condition; four replicate experiments performed , P>0 . 05 ) nor impact our ability to distinguish male and female animals on the basis of their external morphology . However , as discussed further below , this chitosan/siRNA silencing of dsx in A . aegypti larvae led to morphological defects in the wing , proboscis , maxillary palp , and antenna . For analysis of structures/traits that develop in the late pupal stage , dsx-KD A , dsx-KD B , or control siRNAs were microinjected into female pupae [30] . As with the larval targeting experiments , survival to adulthood was not impacted in these microinjection dsx targeting experiments ( n = 50 per control or experimental condition; three replicate experiments performed , P>0 . 05 ) , and the mosquitoes could still be identified as females on the basis of the external morphology . However , as detailed herein , fertility and fecundity defects that correlated with ovary defects were observed in these animals . Olfactory phenotypes were also detected , and female lifespan decreased . A . aegypti body size , which is larger in females , can be assessed through analysis of wing size , a proxy for adult body size [46 , 47] . Wing areas ( p<0 . 0001 , Fig 1A ) and lengths ( p<0 . 0001 , Fig 1B ) are significantly decreased in females fed dsx-KD A or dsx-KD B siRNA nanoparticles as larvae . On average , the areas of dsx-KD A and dsx-KD B wings are 19% and 21% smaller than control-fed animal wing areas , respectively ( Fig 1A ) . Likewise , wing lengths of dsx-KD A animals are 15% smaller than control-fed females , while the lengths of dsx-KD B adult females are reduced by 16% ( Fig 1B ) . Adult females also have an elongated proboscis that is critical for blood feeding . In comparison to control-fed animals , proboscis length is significantly reduced in adults fed with dsx-KD A ( P<0 . 00001 , Fig 1C ) or dsx-KD B ( P<0 . 00001 , Fig 1C ) nanoparticles as larvae . Proboscis lengths of dsx-KD A females are 11% smaller than control-fed females , while the lengths of dsx-KD B adult females are reduced by 18% . Lengths of the antenna ( Fig 1D ) and maxillary palp ( Fig 1E ) were also assessed , and both are significantly reduced in females fed with dsx-KD A or dsx-KD B nanoparticles ( P<0 . 0001 ) . The antennae of animals fed with dsx-KD A nanoparticles are 23% shorter than those of control-fed animals , while those fed dsx-KD B nanoparticles are reduced by 14% ( Fig 1D ) . The maxillary palp of dsk-KD A animals is 11% reduced in length with respect to the control , while maxillary palp length is 18% shorter in dsx-KD B females ( Fig 1E ) . Fertility and fecundity were assessed in dsx KD vs . control A . aegypti females mated to wild type male mosquitoes . The number of eggs laid per female ( fecundity ) is significantly reduced ( P<0 . 0001 ) in adult females that were injected as pupae with dsx-KD A or dsx-KD B siRNA as compared to control-injected females ( Fig 2C ) . On average , females injected with dsx-KD A as pupae lay 16% fewer eggs than control-injected females , while those injected with dsx-KD B lay 21% fewer eggs ( Fig 2C ) . Likewise , fertility ( the percentage of hatched eggs ) is significantly reduced ( P<0 . 0001 ) in adult females that had been injected as pupae with dsx-KD A or dsx-KD B siRNA ( Fig 2D ) . On average , the fertility of females injected with dsx-KD A or dsx-KD B females is 9% and 19% less , respectively , than that of control-injected females ( Fig 2D ) . To explore the cause of this reduced fertility and fecundity , mating , blood feeding behavior , and ovary histology were assessed in dsx-targeted females . In comparison to control females , no significant differences ( P>0 . 05 ) were observed in the percentages of females that took blood meals ( Fig 2A ) or mated ( Fig 2B ) following larval nanoparticle or pupal microinjection delivery of dsx-KD A or dsx-KD B siRNA . Pre-blood meal ovaries were assessed at 4 days post eclosion , at which time the follicles are arrested until the mosquito takes a blood meal [48] . These experiments revealed defects in animals fed dsx-KD A or B siRNAs that were confirmed and quantified in females microinjected with dsx-targeting siRNAs as pupae ( Fig 3 ) . The length of pre-blood meal ovaries in dsx-KD A and dsx-KD B animals is significantly less than that of control-injected females ( P<0 . 0001 , Fig 3A , 3C1 , 3C2 and 3C3 ) . On average , the length of both dsx-KD A and dsx-KD B female ovaries is 26% shorter than control ovaries ( Fig 3A ) . Five days post-blood meal , ovary length in 10–12 day old females that had been injected with dsx-KD A and dsx-KD B siRNA as pupae is also significantly less than that of control-injected females ( P<0 . 0001 , Fig 3B , 3D1 , 3D2 and 3D3 ) . On average , the length of dsx-KD A post-blood meal ovaries is 35% less than that of control ovaries , while dsx-KD B female ovaries are an average 34% shorter in length than control ovaries following blood meals ( Fig 3B; P<0 . 0001 ) . The number of ovarioles per ovary ( Fig 3E ) and ovariole length ( Fig 3F ) are also significantly reduced in post-blood meal ovaries following pupal injection with dsx-KD A or dsx-KD B siRNA ( P<0 . 001 ) . Given the significantly shorter lengths of the female antenna and maxillary palp following silencing of dsx ( Fig 1D and 1E ) , the olfactory systems of these animals were assessed in more detail . The lengths of the most abundant type of antennal sensilla , Trichoid sensilla ( sTrichodea ) were quantified through analysis of SEM images of the adult female antenna ( Fig 4A and 4C ) . The length of these structures in females that had been injected with dsx-KD A or dsx-KD B siRNA as pupae is significantly less than that of control-injected females ( P<0 . 0001 , Fig 4C ) . On average , the length of dsx-KD A female antennal sTrichodea is 36% less than that of control sTrichodea , while dsx-KD B female sTrichodea are 34% shorter in length than control sTrichodea ( Fig 4C ) . The lengths of Basiconic sensilla ( sBasiconica ) were assessed through analysis of SEM images of the adult female maxillary palp ( Fig 4B and 4D ) . Likewise , the lengths of these structures are significantly reduced in females that had been injected with dsx-KD A or dsx-KD B siRNA as pupae ( P<0 . 0001 , Fig 4D ) . On average , the length of dsx-KD A female sBasiconica is 29% less than that of control sBasiconica , while dsx-KD B female sBasiconica are 37% shorter in length ( Fig 4D ) . Expression of several odorant receptor ( OR ) genes was assessed in A . aegypti adults . Bohbot et al . [49] reported female-specific expression of OR 62 and OR 123 that was detected through qRT-PCR studies . WhoIe-mount in situ hybridization detected expression of OR 62 ( Fig 5A ) and OR 123 ( Fig 5D ) transcripts in the antennae of adult females . Sequences that match the Dsx consensus binding site sequence [45] were identified upstream of the OR 62 and OR 123 open reading frames ( S2 Table ) . Although it was not biochemically assessed whether Dsx can bind to these sequences , expression of both OR genes is disrupted in the antennae of adult females that had been microinjected with dsx-KD A ( Fig 5B and 5E ) or dsx-KD B siRNA ( Fig 5 and 5F ) as pupae . Mean gray scale analyses indicated that the OR 62 and OR 123 transcript signals in the antennae of dsx-KD A and dsx-KD B animals were significantly reduced in comparison to those of control-injected animals ( P<0 . 0001; S3 Table ) . Likewise , sequences that match the Dsx consensus binding site sequence [45] were identified upstream of the OR 2 and OR 9 open reading frames ( S2 Table ) . Adult female expression of both genes ( Fig 5G and 5J ) is disrupted in the antennae of females injected with dsx-KD A ( Fig 5H and 5K ) or dsx-KD B ( Fig 5I and 5J ) as pupae . Mean gray scale analyses for these expressions studies demonstrated that the OR 2 and OR 9 transcript signals in the antennae of dsx-KD A and dsx-KD B animals were also significantly reduced in comparison to those of control-injected animals ( P<0 . 0001; S3 Table ) . Thus , dsx silencing resulted in multiple OR expression defects in adult female mosquitoes . The impact of developmental targeting of dsx on adult female survival was also assessed . The median survival rates for animals injected with control , dsx-KD A , and dsx-KD B siRNA were found to be 40 , 23 , and 25 days , respectively . In comparison to control-injected animals , life span was significantly decreased in individuals injected with dsx-KD A ( P<0 . 001 ) or dsx-KD B ( P<0 . 001 ) siRNA ( Fig 6 ) . These data demonstrate that silencing dsx during pupal development results in a significantly shorter female adult lifespan .
Body size is a sexually dimorphic trait in A . aegypti , in which females are larger than males [37] . Loss of dsx function resulted in significantly smaller wing size , a correlate for body size ( Fig 1A and 1B ) . The impact of dsx silencing was observed in a number of other tissues , including the proboscis , antenna , maxillary palp ( Fig 1C , 1D , 1E and 1F ) , ovaries ( Fig 3 ) and sensilla ( Fig 4 ) , all of which were significantly smaller with respect to control animals . Although one might have expected to observe a longer male-like maxillary palp in dsx-silenced adult females , this was not found to be the case . Interestingly , our preliminary data suggest that much like females , silencing of dsx in male mosquitoes results in decreased body size and appendage lengths . These findings suggest that Dsx may function in A . aegypti to positively regulate growth of both sexes . Dsx has been associated with growth in a number of other insects and in additional tissue types . For example , in a study of Nasonia species , Loehlin et al . [50] found that wing size reduction correlated with an increase in dsx wing expression levels that is specific to developing males of this species . Sex-specific Dsx splice forms are known to regulate sexually dimorphic exaggerated male horn development in two species of beetles , O . taurus [18] and the rhinoceros beetle T . dichotomus [19] . In male O . taurus , silencing of dsx reduced horn development in large males , while silencing of dsx in females resulted in induction of ectopic , nutrition-sensitive horn development in females that are otherwise hornless . Comparable results were obtained in T . dichotomus [19] . Dsx also regulates sex-specific mandible growth , which is exaggerated in Cyclommatus metallifer males [21] . Our recent study demonstrated that genes linked to the cell cycle are upregulated in A . aegypti females [30] . These genes , which are likely associated with increased growth of female tissues , may be direct or indirect targets of Dsx . In support of this notion , cyclin dependent kinase 4/6 ( cdk4/6 ) , a positive regulator of cellular growth in D . melanogaster [51 , 52] , is upregulated in the A . aegypti female pupal brain . Dsx consensus binding sites flank the A . aegypti cdk 4/6 gene , and sexually dimorphic expression of this gene in the pupal brain is disrupted by dsx knockdown [30] . Interestingly , Cyclin D , the Cyclin associated with Cdk4/6 in D . melanogaster [53] , was recently identified as a Dsx target gene in Drosophila [45] . It is therefore likely that loss of Cyclin D-Cdk4/6 function may be at least in part responsible for the size differences observed upon silencing of dsx in both sexes of A . aegypti . Additionally , Gotoh et al . [21] identified a link between dsx and the juvenile hormone ( JH ) signaling pathway , demonstrating that female-specific splice variants of C . metallifer dsx contribute to the insensitivity of female mandibles to JH . It would therefore be interesting to determine if sex-specific Dsx splice forms regulate JH-responsiveness in A . aegypti . Loss of Dsx function results in sterility in D . melanogaster [4] and Bombyx mori [20] , and RNAi targeting of dsx during pupal development in T . casteneum was found to result in decreased fertility and fecundity [17] . Decreased female fertility and fecundity were observed when dsx was silenced during female A . aegypti pupal development ( Fig 2 ) . These differences correlated with decreased adult ovary length both pre- and post-blood meal , as well as a decreased ovariole length and number in blood-fed adult females ( Fig 3 ) . In D . melanogaster , Dsx was found to play early roles in the development of the female/male genitalia and analia , which are both derived from the larval genital imaginal disc . D . melanogaster Dsx regulates the anterior/posterior organizer to control growth of female or male genital primordia , and then it acts in a sex-specific manner to direct differentiation of each male or female primordium into the defined adult structures present in either sex [54] . Dsx may have similar roles in A . aegypti , and so it would be interesting to assess its function at the level of the genital primordia , the development of which has not yet been assessed . Here , we focused on later stages of ovary development , which in mosquitoes is dependent upon the acquisition of a blood meal , and which has been better characterized [48] . Vitellogenesis , the synthesis and secretion of yolk protein precursors ( YPPs ) , is a critical event in the mosquito reproductive cycle that is activated in response to the blood meal [55 , 56] . In D . melanogaster , which does not require a blood meal for reproduction , the vitellogenin subunit yolk protein-1 ( yp1 ) and yp2 genes are targets of Dsx [57 , 58] . Similarly , the T . casteneum YPP vitellogenin ( vg ) and vitellogenin receptor ( vgr ) genes were identified as targets of Tc Dsx [17] . Thus , one might speculate that Dsx regulation of YPP genes is conserved in insects . However , the A . aegypti YPP genes lack any obvious Dsx binding sites , and if Dsx regulates expression of these genes in A . aegypti , it is likely to do so indirectly . Interestingly , juvenile hormone ( JH ) is known to regulate expression of A . aegypti Vg-A [59] . Although we were unable to identify any obvious Dsx consensus binding site sequences associated with components of the JH signaling pathway in A . aegypti , Gotoh et al . [21] recently demonstrated that female-specific splice variants of C . metallifer dsx contribute to the insensitivity of female mandibles to JH . It would therefore be interesting to determine if links between Dsx and JH responsiveness exist in A . aegypti . Dsx likely regulates A . aegypti female reproduction in many additional ways . Although we did not detect any significant impacts of Dsx silencing on membrane blood feeding behavior or the ability to mate in the laboratory setting ( Fig 2A and 2B ) , it is not known how these mosquitoes would perform in the wild . Furthermore , Lee et al . [60] recently identified a Dsx-positive neuronal pathway in D . melanogaster that controls sperm ejection and storage . When the neuronal signaling pathway in the brain , which consists of Diuretic hormone 44 ( Dh44 ) and its receptor ( Dh44R1 ) , is suppressed , the brain expedites sperm ejection from the uterus , resulting in decreased fecundity . Thus , Dsx signaling could have multiple impacts on female reproduction in A . aegypti . Gaining a fuller understanding of these impacts will likely require much more detailed knowledge of A . aegypti reproductive behaviors . This investigation has linked Dsx with the expression of OR genes . This linkage , which to our knowledge has yet to be identified in other organisms , provides insight into the regulation of sex-specific olfactory development . Silencing of dsx during pupal development was found to disrupt expression of two female ORs , 62 and 123 ( Fig 5 ) . Although the functions of these ORs have yet to be characterized in A . aegypti , upregulation of the expression of these genes by DsxF may contribute to female-specific olfactory-driven behaviors . The detection of Dsx consensus binding site sequences upstream of the OR 62 and 123 open reading frames ( S2 Table ) suggests that their expression may be regulated directly by Dsx , but this has not yet been directly assessed . Dsx also positively regulates expression of ORs 2 and 9 ( Fig 5 ) . The function of A . aegypti OR 9 is not known . However , OR 2 , which is well conserved among mosquitoes , is known to be activated by indole , a major volatile component of human sweat that is also implicated in oviposition site selection [61 , 62] . To date , analysis of the development of sexual dimorphism in the olfactory system has largely centered on analysis of the roles of fruitless ( fru ) , which also encodes a terminal transcription factor in the sex determination pathway that is spliced in a sex-specific manner [8] . Cachero et al . [63] performed a global search for sexually dimorphic structural differences in the Drosophila brain , as well as a saturating clonal analysis of Fru-positive neurons . They noted that the proportion of cells in the D . melanogaster brain that expresses Dsx is smaller and partially overlaps with Fru , an interesting observation given that Neville et al . [64] suggested that Drosophila Dsx and Fru may act together , either in a physical complex or through co-regulation of target genes , to control sex-specific neural development . It will therefore be interesting to functionally assess the roles of Fru during neural development in A . aegypti . Drosophila researchers have begun to link Dsx and Fru to specific sexually dimorphic neural physiologies , neural circuitries , and behaviors , and so another challenge will be to further develop genetic technologies with the goal of being able to perform comparably technical analyses in mosquitoes . For example , in D . melanogaster , the activation of a set of Fru-positive olfactory receptor neurons ( ORNS ) that express OR67d , which responds to male pheromone cis-vaccenyl acetate , was found to inhibit male courtship of other males and induce female receptivity to other males . The ORNs expressing 67d converge to a single glomerulus , DA1 , in both the sexes , but the projections from the DA1 glomerulus to the protocerebrum were found to be sexually dimorphic , suggesting that differential behaviors induced by this pheromone result from sex-specific neural circuitries [65] . Kohl et al . [66] further demonstrated that sex-specific wiring induces differential responses to cVA pheromone inputs , suggesting that different fru isoforms function as a bidirectional switch to activate different behaviors in males and females . In another study , reduction of Ecdysone receptor-A in FruM-positive neurons , which is associated with an increase in male-male courtship activity , was found to result in significant reduction in the size of two antennal lobe glomeruli , suggesting that EcR-A is required for establishment of male-specific neuronal architecture in the D . melanogaster olfactory system [67] . These findings suggest that in addition to differences in OR expression , changes in the overall neural circuitry responding to the odorant may induce dimorphic behaviors in males and females , and it will be interesting to examine this in A . aegypti in the future . As discussed by Brady et al . [68] , the survival of arthropod vectors is one of the most critical components of pathogen transmission . Increased survival results in the production of more offspring . It also increases the likelihood of the arthropod to become infected , to disperse over greater distances once infected , to survive long enough to transmit the pathogen , and to deliver a greater number of infectious bites during its lifespan . Thus , small changes in survival rates could have large impacts on pathogen transmission [69–72] , and vector control strategies that shorten vector lifespan may represent new alternative control strategies [73–75] . The results of this study demonstrate that targeting dsx during female pupal development significantly reduces adult A . aegypti female lifespan ( Fig 6 ) . The genetic manipulation of sex-determination gene expression in D . melanogaster has been shown to impact lifespan [76] . While overexpression of dsxF during male development was lethal to males and females ( with a limited number of female escapers ) , overexpression of dsxF in adults dramatically reduced the life span of both males and females . Overexpression of the male isoform of fru in males or females yielded similar results . Interestingly an RNAi line targeting fru reduced lifespan in D . melanogaster females only . Shen et al . [76] suggested that it would be interesting to examine potential interactions between the sex determination genes and the insulin/IGF1-like signaling ( IIS ) pathway or dietary restriction , both of which regulate lifespan in a sex-dependent manner . Furthermore , Tarone et al . [77] demonstrated that Yp expression is negatively correlated with longevity in D . melanogaster . Thus , as discussed above , any impact that Dsx might have on Yp expression could underlie the decreased longevity of A . aegypti dsx-targeted females . Finally , silencing dsx in A . aegypti was shown to result in decreased expression of p53 [30] . Overexpression of p53 in the D . melanogaster female nervous system results in increased life span [76] . It is tempting to speculate that downregulation of p53 expression following dsx silencing may contribute to the decreased lifespan observed in dsx-targeted A . aegypti females .
Female mosquitoes differ from males in several morphological , physiological , and behavioral traits that are critical to their ability to transmit diseases . The arthropod disease vector research community has therefore had a long-standing interest in the potential to manipulate sex determination and differentiation genes for controlling disease vectors . Our previous work [30] demonstrated that Dsx regulates sex-specific gene expression in the developing A . aegypti pupal nervous system . The present investigation extended these initial findings through assessment of the effects of developmental siRNA-mediated dsx silencing in adult females . Targeting of dsx resulted in decreased size of the female wing and proboscis ( Fig 1 ) . Decreased fecundity and fertility correlated with decreased ovary length , ovariole length , and ovariole number in females in which dsx was silenced during development ( Figs 2 and 3 ) . Targeting dsx also resulted in disruption of olfactory system development , as evidenced by reduced length of the female antenna and maxillary palp and their respective sensilla ( Figs 1 and 4 ) , as well as disrupted OR expression ( Fig 5 ) . Female lifespan , a critical aspect of mosquito pathogen transmission , was also significantly reduced in adult females following developmental targeting of dsx ( Fig 6 ) . These results demonstrate that developmental silencing of dsx in A . aegypti females , which disrupts development of multiple adult female traits linked directly or indirectly to reproduction and pathogen transmission , may be useful for vector control .
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Only adult female mosquitoes , which require blood meals for reproduction , bite humans and spread diseases . The genes that regulate development of sex-specific traits may therefore represent novel targets for mosquito control . Here , we examine the effects of silencing the sex-determination gene doublesex ( dsx ) during development of the human disease vector mosquito Aedes aegypti . Targeting of dsx resulted in decreased length of the female wing and proboscis , ovary and reproductive defects , and disruption of olfactory system development . Female lifespan , a critical aspect of mosquito pathogen transmission , was also significantly reduced in adult females following developmental targeting of dsx . The results of this investigation demonstrate that silencing of dsx during A . aegypti development disrupts multiple sex-specific morphological , physiological , and behavioral traits of adult females , a number of which are directly or indirectly linked to mosquito reproduction and pathogen transmission . The results obtained also connect Dsx to development of the mosquito olfactory system , suggesting that A . aegypti will be an excellent system in which to further assess the developmental genetics of sex-specific chemosensation .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion",
"Conclusion"
] |
[] |
2015
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siRNA-Mediated Silencing of doublesex during Female Development of the Dengue Vector Mosquito Aedes aegypti
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microRNAs are small non-coding RNAs that are important regulators of gene expression in a range of animals , including nematodes . We have analysed a cluster of four miRNAs from the pathogenic nematode species Haemonchus contortus that are closely linked in the genome . We find that the cluster is conserved only in clade V parasitic nematodes and in some ascarids , but not in other clade III species nor in clade V free-living nematodes . Members of the cluster are present in parasite excretory-secretory products and can be detected in the abomasum and draining lymph nodes of infected sheep , indicating their release in vitro and in vivo . As observed for other parasitic nematodes , H . contortus adult worms release extracellular vesicles ( EV ) . Small RNA libraries were prepared from vesicle-enriched and vesicle-depleted supernatants from both adult worms and L4 stage larvae . Comparison of the miRNA species in the different fractions indicated that specific miRNAs are packaged within vesicles , while others are more abundant in vesicle-depleted supernatant . Hierarchical clustering analysis indicated that the gut is the likely source of vesicle-associated miRNAs in the L4 stage , but not in the adult worm . These findings add to the growing body of work demonstrating that miRNAs released from parasitic helminths may play an important role in host-parasite interactions .
microRNAs ( miRNAs ) were first discovered in the free-living nematode Caenorhabditis elegans and have now been found in the cells of plants , animals and humans . These small RNAs are post-transcriptional regulators of gene expression and their dysregulation has been linked to a range of different pathologies including immune-related diseases [1] and cancer [2] . miRNAs act by binding to complementary sequences often located in the 3’UTR of target genes in the context of the RNA Induced Silencing Complex ( RISC ) . This results in the degradation of the mRNA and/or repression of translation [3] . Parasitic nematodes , such as Haemonchus contortus , are closely related to C . elegans both belonging to nematode clade V [4] . By small RNA library sequencing and bioinformatics using available genome sequence data , we identified a total of 192 miRNAs from H . contortus L3 and adult worms in a previous study [5] . Of these , 44 were conserved between H . contortus and C . elegans , suggesting that the majority of H . contortus miRNAs were species-specific . H . contortus is an important parasite of small ruminants; it has a typical trichostrongyle life cycle in which sheep become infected by ingestion of infective third stage larvae ( L3 ) . These develop into L4 stages in the abomasum and then into adult worms , which have a blood-sucking habit resulting in anaemia and , in acute cases , death . An interesting aspect of miRNA research stems from the finding that small RNA species can be secreted from cells and are detectable in the circulation [6–8] . A similar phenomenon has been observed for a number of parasitic helminths such as Heligosomoides polygyrus [9] , Dirofilaria immitis [10] , Brugia malayi [11] and Schistosoma mansoni [12] . In all these worms , miRNAs were shown to be released into the excretory-secretory ( ES ) products collected in vitro . In some cases , specific miRNAs were also detected in the serum of infected patients or animals suggesting that some helminth parasites release miRNAs in vivo as well as in vitro [13] . In an elegant study , Buck et al . [9] demonstrated that not only were miRNAs secreted by adult H . polygyrus , but that many were contained within exosomes , small cell-derived vesicles . Moreover , exposure of mouse cells to exosomes resulted in downregulation of host genes important for immune responses , such as Dusp1 and il-33r . Those data provided the first example that secreted parasitic nematode miRNAs have the potential to regulate host immunity [9] . miRNAs are often found clustered in genomes and , in the original study of Winter et al . , [5] , eight H . contortus miRNA clusters containing 23 separate miRNAs were identified . hco-miR-5352 was identified in that study and was shown to belong to a cluster of four miRNAs that appeared to be closely linked in the H . contortus genome . hco-miR-5352 was of particular interest as comparative analysis showed that it was conserved in the gastro-intestinal nematode Ascaris suum but not in other clade III nematodes [5] . Here we provide a detailed characterisation of the hco-miR-5352 cluster: we investigate its expression in different life cycle stages of H . contortus , its conservation in other parasitic nematodes and its presence in H . contortus excretory-secretory products ( ES ) . Furthermore , we identify additional small RNA sequences in the ES products and EV of L4 and adult H . contortus and carry out comparative analysis with miRNAs within EV of the related parasitic nematode Teladorsagia circumcincta . The potential relevance of these miRNAs in the parasite life cycle is discussed .
Experimental infections were carried out at the Moredun Research Institute , UK , as described previously [14] . All experimental procedures were examined and approved by the Moredun Research Institute Experiments and Ethics Committee ( MRI E46 11 ) and were conducted under approved UK Home Office licence ( PPL 60/03899 ) in accordance with the 1986 Animal ( Scientific Procedures ) Act , UK . Adult H . contortus of the strain MHco3 ( ISE ) were harvested from sheep 28 days post-infection ( p . i . ) with 5000 infective L3 at the Moredun Research Institute . L4 were recovered at day 7 p . i . At post-mortem the abomasum was opened and washed with 0 . 9% saline solution to remove the worms , which were then extensively washed in saline solution . The microarray was prepared by LC Sciences and contained 609 potential H . contortus miRNAs ( not all of which were accepted by miRBase ) and 238 C . elegans miRNAs present in miRBase release 15 . The microarray was probed with RNA isolated from the following stages of H . contortus: sheathed L3 , exsheathed L3 cultured for 24h at 37°C ( activated L3 ) , day 7 p . i . L4 , adult males and females recovered 28 days p . i . and adult female gut tissue . RNA was extracted from whole worms , by grinding to a fine powder in a liquid nitrogen-cooled pestle and mortar before adding 1 ml of Trizol ( Life Technologies ) and proceeding exactly according to the manufacturer’s instructions . For host tissue , small pieces of abomasum or abomasal lymph node were collected at post-mortem from two sheep at 28 days p . i . with 5 , 000 L3 , at which time male and female adult worms were present and the females were producing eggs . Equivalent tissue samples were harvested from two uninfected , pathogen-free sheep . Tissues were immediately added to tubes containing RNAlater . Care was taken to remove abomasal tissue from areas without any adult H . contortus present . Tubes were stored at -20°C , as recommended by the manufacturer , and RNA was isolated as required ( see below ) . 30 mg of abomasal tissue or 10 mg of lymph node tissue were added to a hard tissue homogenizing CK28 tube ( Bertin Instruments ) and 1 ml of Trizol reagent added to each tube . Lymph node samples were homogenised using a Precellys 24 homogenizer ( Bertin Instruments ) for 2 x 50 second cycles at 6000 rpm , while abomasal tissues were homogenised for 5 x 23 second cycles at 6200 rpm , with 2 minutes on ice between each cycle . Following centrifugation at 14 , 000 rpm for 10 minutes at 4°C , 0 . 2 ml of chloroform was added per 1 mL of Trizol reagent and processing continued according to the manufacturer’s instructions . The final RNA pellet was resuspended in RNase free water and yield and purity assessed using a Nanodrop spectrophotometer . PolyA tailing of RNA samples and cDNA synthesis was carried out using the miRNA 1st-Strand cDNA Synthesis Kit ( Agilent Technologies ) according to the manufacturer’s instructions . Samples were stored at -20°C . RT-PCR was performed using the miRNA qRT-PCR Master Mix protocol ( Agilent Technologies ) on RNA extracted from whole adult worms , adult worm ES or abomasal and lymph node tissue from infected or uninfected sheep . Samples were analysed in the Agilent Mx3005P qRT-PCR System using MxPro QRT-PCR software ( Agilent Technologies ) . All RT-PCR reactions were carried out in triplicate and mean values plotted using Microsoft Excel . Primer sequences are shown in S1 Table . For a normaliser miRNA for use with sheep tissues , the expression of three ovine miRNAs ( oar-miR-26a , oar-miR-103 and oar-miR-122a ) was assessed . These were selected based on the expression of the bovine orthologues in a range of tissues [15] . oaR-miR-26a was selected as it could be amplified consistently from all ovine abomasal and lymph node samples tested . Approximately 100 adult worms ( mixed males and females ) or 250 L4 were cultured in 25 ml of RPMI supplemented with 1% glucose and 100 μg per ml penicillin/ streptomycin . Spent medium containing ES products was replaced with fresh medium every 24 hours for three days and was pooled as described below . ES was centrifuged at 1500 g for 5 minutes to remove released eggs and hatched L1 stages from adult ES or any debris from L4 ES , then filtered through a 0 . 22 μm filter and stored at -80°C . ES was concentrated from approximately 50 ml to 1 ml using Vivaspin 20 ml 10 , 000 MWCO sample concentrators and then stored at -80°C . Extracellular vesicles ( EV ) were collected by ultracentrifugation of 12 . 5 ml ES from adult worm or L4 cultures at 100 , 000 g for 2 h . The supernatant was aspirated carefully and retained ( EV-depleted fraction ) . The final pellets were washed twice with PBS , re-suspended in 100–200 μl of PBS and stored at -80°C . In some experiments EV were processed for transmission electron microscopy exactly following the method of Tzelos et al . , [16] . RNA was extracted from total adult worm ES pooled from 24 and 48 h cultures for construction of a small RNA library at the University of Glasgow Polyomics Facility . In subsequent experiments , four additional libraries were constructed by LC Sciences using RNA isolated from EV or ES that had been depleted of EV , from cultures of adult worms or L4 stages . In the latter cases , ES was pooled from 24 , 48 and 72 h cultures to optimize the amount of material available . In all cases , RNA was extracted using the Qiagen QIAzol system; 200 μl of concentrated ES or EV was added to 1 ml of QIAzol reagent and RNA extraction carried out according to the manufacturer’s instructions . RNA integrity was determined using an Agilent 2100 Bioanalyzer . All small RNA libraries were generated from RNA samples using the Illumina Trueseq™ Small RNA Preparation kit according to the manufacturer’s instructions and RNA sequenced using an Illumina GAIIx . For comparative purposes , concentrated EV was also obtained from the ES products of L4 stage of T . circumcincta and total RNA extracted as detailed above for H . contortus . Homologues of the hco-miR-5352 cluster were identified using BLASTN searches of WormBase ParaSite ( http://parasite . wormbase . org/ ) with default search parameters . Sequences obtained from BLAST analysis were then aligned against the H . contortus sequence to determine pairwise similarity . Secondary structure prediction of the potential miRNA precursor sequences was carried out using Mfold 3 . 4 ( http://mfold . rna . albany . edu ) . Sequence alignments were then carried out using Geneious version 6 ( http://www . geneious . com [17] ) using the following settings: Cost Matrix: 65% Similarity; Gap open penalty: 12; Gap extension penalty: 3; Alignment type: Local alignment ( Smith Waterman ) Phylogenetic trees were created using Geneious with the settings as follows: Cost Matrix: 65% Similarity; Gap open penalty: 12; Gap extension penalty: 3; Alignment type: Local alignment ( Smith Waterman ) ; Genetic Distance Model: Tamura-Nei; Tree build Method: Neighbour-joining; Outgroup: no outgroup . Illumina sequence reads from the total ES library were processed to clip adapter sequences and identify duplicate reads . miRDeep2 was used to map the reads to a list of miRNA precursor , mRNA , tRNA and rRNA sequences as described in Winter et al . , [5] . Sequence reads from the EV and EV-depleted RNA libraries were mapped to H . contortus miRNAs from miRBase ( version 21 , 2014 ) and the H . contortus genome ( h contortus . PRJEB506 . WS248 . genomic . fa . gz released on 04/09/2015 ) by LC Sciences , using a proprietary script . Reads that did not map to H . contortus were mapped to other nematode miRNAs in miRBase ( namely Caenorhabditis elegans , Caenorhabditis brenneri , Caenorhabditis briggsae , Ascaris suum , Pristionchus pacificus , Brugia malayi , Strongyloides ratti and Panagrellus redivivus ) . The script aligned reads to the mature miRNA sequences and allowed for up to three mismatches in the 3’ end of the alignment . Mapped reads were normalised across the EV-enriched and EV-depleted libraries by calculating the geometric mean for each miRNA across the four samples . The raw reads were then adjusted based on the geometric means , allowing normalisation to miRNA read count . The median value of the adjusted read counts for each library were calculated ( library size parameter ) and the raw reads were then normalised based on the library size parameter . To identify reads mapping to the same list of precursor miRNA , mRNA , tRNA and rRNA sequences as described above and used by Winter et al . , [5] , Mapper in miRDEEP2 was used ( https://www . mdc-berlin . de/36105849/en/research/research_teams/systems_biology_of_gene_regulatory_elements/projects/miRDeep [18] ) . This preformed a more stringent identification of H . contortus small RNAs than the initial LC analysis ( no mismatches in the first 18 nucleotides ) . Hierachical clustering was carried out using the matrix visualisation and analysis platform GENE-E version 3 . 0 . 240 ( http://www . broadinstitute . org/cancer/software/GENE-E/ ) to identify groups of miRNAs with similar expression patterns from the various H . contortus ES libraries , both EV-enriched and EV-depleted . Data was normalised by scaling all numeric variables in the range [0 , 1] and normalised data imported into Gene-E . Row distance was calculated using the one-minus Pearson correlator metric , complete linkage and clustered by row . All other settings were default . BLAST+ 2 . 4 . 0 was downloaded from NCBI and set up locally . RNA reads from the T . circumcincta library were set as queries for BLAST searching against the miRBase hairpin miRNA database ( Release 21 ) , with the BLAST parameters E-value being 0 . 01 and word-match size between the query and database sequences of 7 . Statistical analysis: Statistical significance was calculated using an analysis of variance test performed on MiniTAB Statistical Software version 16 ( Minitab Inc , State College , PA , www . minitab . com ) .
The hco-miR-5352 cluster consists of four miRNA loci ( hco-miR-61 , hco-miR-5352 , hco-miR-43 and hco-miR-5895 ) all oriented in the same direction and closely linked in the genome , spanning a region of only 422 bp . The developmental expression of the miRNAs in the hco-miR-5352 cluster was first investigated from a microarray containing all known H . contortus miRNAs . The array was probed with RNA from different life cycle stages of H . contortus and from gut tissue dissected from adult female worms . Minimal expression of the hco-miR-5352 cluster sequences was observed in L3 stages ( both sheathed L3 and activated L3 , which had been exsheathed and cultured in vitro ) , the L4 stage and in adult female gut tissue . In contrast , all four miRNAs in the cluster were abundant in adult worms , with females showing higher levels of each miRNA compared to male worms ( Fig 1 ) , although this is only significant for hco-miR-5352-3p ( p<0 . 05; log2>2 . 0 ) . Hierarchical clustering of the microarray expression data showed that the four miRNAs in the hco-miR-5352 cluster group together based on their enrichment in adult female worms , suggesting that they may be co-expressed ( see life cycle stage microarray ) . For all four miRNAs in the cluster , expression of both arms of the mature miRNA ( 3p and 5p arms ) was identified in the original sequence data of Winter et al . , [5] . The dominant miRNA from each hairpin was submitted to miRBase ( 3p for hco-miR-61 , hco-miR-5352 and hco-miR-43 and the 5p for hco-miR-5895 ) . Microarray data demonstrated that , while the expression of hco-miR-43-5p is much lower than the 3p arm , it is still expressed at a reasonable level in female worms ( see microarray data ) . BLASTN searches of the WormBase ParaSite database ( parasite . wormbase . org ) identified homologues of the miR-5352 cluster in an additional ten clade V nematode species: Teladorsagia circumcincta , Oesophagostomum dentatum , Nippostrongylus brasiliensis , Necator americanus , Strongylus vulgaris , Heligmosomoides polygyrus , Cylicostephanus goldi , Ancylostoma duodenale , Ancylostoma ceylanicum and Dictyocaulus viviparus . The specific databases from which members of the miR-5352 cluster were identified , the scaffold number and position and the location of these parasites within the host is shown in S2 Table . A multiple alignment of the cluster sequences from these clade V organisms along with the sequence of the hco-miR-5352 cluster is presented in Fig 2 . The D . viviparus cluster sequence is detailed below . For all four miRNAs , there was significant identity between the precursor sequences across all nine species , ranging from 61 . 9 to 78 . 8% pairwise identity . The identity increased to over 95% when only the mature sequences were considered . All of these sequences were annotated manually using Geneious and RNA-fold and all form a stereotypical miRNA precursor hairpin loop . From the BLAST searches , multiple sequences containing the hco-miR-5352 cluster were identified from H . polygyrus and T . circumcincta ( parasite . wormbase . org ) . From the contig location coordinates , T . circumcincta sequences 1 and 2 were located at 144782–144965 and 144994–145200 of contig 190 while H . polygyrus sequences were also located close together at 48067–48260 and 48281–48489 on scaffold 0001328 . Both sets of sequences showed near 100% identity to each other , suggesting that they may have arisen from recent duplication , although it is also possible their identification in regions of close proximity may result from poor assembly of these genomic regions . T . circumcincta sequence 3 was located on a different contig and was shorter ( see S1 Fig ) . Asu-miR-5352 is the only other miRNA in miRBase designated as miR-5352 and the precursor miRNA shows 70% identity to the H . contortus sequence ( S2 Fig ) [19] . Six versions of Ascaris miR-43 were identified in miRBase , showing 69% identity to hco-miR-43 , however , Ascaris miR-61 and miR-5895 were not identified in the current version of miRBase . hco-miR-5352 cluster precursor and mature sequences , along with A . suum miR-43 and 5352 sequences , were aligned to both A . suum and Ascaris lumbricoides scaffolds that contained miR-5352 . The mature Ascaris miRNA sequences were very similar , differing by only one base ( 99 . 8% pairwise similarity ) as shown in S2 Fig . The Ascaris miRNAs were organised in the same order as in H . contortus , albeit with a much larger gap between the two precursors . Asu-miR-5352 was then used to screen for miR-5352 sequences in other clade III species , revealing their presence only in other ascarid parasites . miR-5352 and miR-43 were identified from Toxocara canis , while Anisakis simplex contained only miR-5352 . In addition to the clade III species , the mature miR-5352 sequence was also identified from the bovine lungworm Dictyocaulus viviparus . The region of alignment was 23 bp long and was 95 . 7% similar to the H . contortus mature sequence ( S3 Fig ) . Further searches identified a 100% match to the mature hco-miR-43 and a 90 . 9% match to the mature hco-miR-61 sequence . From the location , the three matches described above were clustered together . In depth analysis of the surrounding regions identified a sequence aligning to hco-miR-5895 , with 87 . 9% identity . Mfold analysis of the predicted precursor sequences indicated that the D . viviparus miRNAs were able to fold into hairpin loops ( S4 Fig ) . A phylogenetic tree using the sequences of the miR-5352 cluster from WormBase ParaSite is shown in S5 Fig , demonstrating that the cluster in A . suum and D . viviparus is more distantly related to that in the other species . From all the parasitic nematodes for which genome data is available , only worms from the sub-order Strongylida contained the complete hco-miR-5352 cluster , with the exception of the super-family Metastrongyloidea . Neither Angiostrongylus costaricensis nor A . cantonensis contained any member of the hco-miR-5352 cluster As the miR-5352 cluster appeared to be expressed only in worms that reside at mucosal surfaces , we next determined whether it may be secreted from the parasites and could be detected in parasite ES or in infected tissues . Mixed sex adult H . contortus were cultured in vitro and ES collected over a 48 h period . RNA was extracted from concentrated ES and qRT-PCR carried out to detect miR-5352 , with whole adult female H . contortus cDNA being used as a positive control . In addition , several worms were removed prior to in vitro culture and were dissected to remove the worm intestine , from which RNA was also prepared . Fig 3A presents the analysis of hco-miR-5352 expression in adult female whole worms , adult ES and adult gut samples . qRT-PCR showed that hco-miR-5352 could be detected in the ES products and female worm extract but not in gut tissue , consistent with microarray data . While it was not possible to quantify the hco-miR-5352 signal precisely , a constitutively expressed miRNA , hco-miR-50 , could be amplified from all three samples . As the miRNAs in the hco-miR-5352 cluster were enriched in adult female worms , but not expressed in the adult female gut tissue , we assayed the presence of hco-miR-5352 in eggs/L1 collected from in vitro cultured worms . miR-5352 could be detected in eggs/L1 by qRT-PCR at a similar level to that of the adult female ( Fig 3B ) . To determine whether Haemonchus miRNAs could be detected in tissue from infected sheep , indicative of the release of parasite miRNAs in vivo , RNA was extracted from small pieces of abomasal tissue or from the abomasal lymph nodes dissected from H . contortus infected and pathogen-free sheep at post-mortem . Two members of the cluster , hco-miR-5352 and hco-miR 5985 , were selected on the basis of their lack of similarity to sheep miRNAs , while hco-miR-5960 was also assayed , as it is the most abundant Haemonchus miRNA in ES material ( see below ) . The data in Fig 4 shows the signal obtained from infected and uninfected tissues for all three miRNAs normalised to oar-miR-26a ( see Materials and Methods and S6 Fig ) . For abomasal tissue samples and particularly the lymph node , hco-miR-5352 could be detected at a greater level in infected relative to uninfected animals ( P<0 . 0001 ) , while hco-miR-5895 gave no signal from tissue from uninfected animals but was detected in infected tissue samples ( P<0 . 001 ) . In contrast , levels of hco-miR-5960 showed no significant difference between infected and uninfected tissues . Interestingly for hco-miR-5352 , the signal from the draining lymph node in infected sheep was significantly higher than that from the abomasal tissue ( P = 1 . 59E-05 ) . The data presented above demonstrated that hco-miR-5352 could be detected in adult worm ES material using qRT-PCR . In order to characterise the complement of small RNA species present in H . contortus ES , a small RNA library was constructed from adult worm ES . Over 15 million reads were sequenced and mapped to previously identified H . contortus RNA sequences [5] . Alignments were accepted if the read was longer than 18 base pairs and had no more than one mismatch . The initial study of Winter et al . , [5] identified 462 potential miRNA sequences from adult whole worm and L3 stages of H . contortus , of which 211 were submitted to miRBase and 192 accepted [5] . This number has been revised in the current version of miRBase ( release 21 , http://www . miRBase . org/ , accessed 10/01/2015 ) and now consists of 195 miRNAs . Of these 195 accepted H . contortus microRNAs , 85 were found in the adult ES small RNA library . In addition , a further 83 sequences were identified that mapped to miRNAs that had been rejected from the miRBase list for a variety of reasons including: not folding according to miRBase programs or the Computational Identification of microRNA program ( CID-miRNA ) [20] , similarity to existing rRNA or tRNA sequences , or low counts ( <10 reads ) in the adult library . All reads from both the adult whole worm and adult ES small RNA libraries were mapped to all 462 potential miRNA sequences as well as to tRNA , rRNA and mRNA sequences obtained by Winter et al . , [5] . In the ES small RNA library , 77% of the total reads mapped to ribosomal RNA sequences , with mRNAs being the second most abundant class with 22% of the reads ( Fig 5 ) . Transfer RNAs ( tRNAs ) comprised the smallest group ( 0 . 1% ) with miRNAs representing 0 . 2% of the reads . In comparison , in the adult whole worm small RNA library , rRNAs made up 32% of the reads , followed by miRNAs ( 14% ) . Reads from tRNAs constituted the smallest group ( 4% ) with mRNAs comprising 14% [5] . Due to the difference in total read numbers between the adult ES library and adult worm extract library , the miRNAs were ranked by the number of reads . Tables 1–3 show the ten most abundant miRNA sequences for the adult whole worm and adult ES libraries , including miRNA sequences that were not accepted by miRBase . Five of the ten most abundant sequences in the adult ES library ( Table 1 ) are not accepted miRNAs; in the adult whole worm library , only hco-0325 is not an accepted miRNA . hco-0129 , hco-0135 and hco-0134 were rejected as miRNAs due to their low number ( <10 reads ) in the adult whole worm library . However , in the ES library , their abundance is fourth , fifth and sixth respectively and all show read numbers >500 . These results demonstrate that there are differences between adult whole worm and adult ES libraries and that there are sequences in the ES that are likely to be novel miRNAs , although their expression is low in adult whole worm libraries . Three of the top ten most abundant miRNAs in the adult ES library , hco-miR-61 , hco-miR-5895 and hco-miR-0325 , also appear in the top ten list for the adult whole worm library . hco-miR-87b , on the other hand , is the tenth most abundant miRNA in the adult whole worm library but does not appear at all in the ES library ( Table 2 ) . The large variation in the relative abundance of these miRNAs suggests that there is selective release of miRNAs in the ES . Table 3 shows a comparison of the rankings showing only the miRBase-accepted miRNAs ranked by total normalised reads . The miRNAs of the hco-miR-5352 cluster are highly abundant in both libraries and show similar rankings , with expression of hco-miR-5352 being the lowest of the four , followed by hco-miR-43 . In the whole worm adult library , hco-miR-61 expression is higher than that of hco-miR-5895 , but in the ES library , the reverse is true . miRNAs have previously been shown to be present in both the ES supernatant and in EV purified from nematode ES products [9] . To determine whether H . contortus ES contains EV , adult worm ES was collected , ultracentrifuged and processed for transmission electron microscopy . A large number of small circular structures were identified of a size consistent with EV , as shown in Fig 6 . These structures were similar in appearance to those identified in both H . polygyrus and T . circumcincta ES [9 , 16] . To further investigate the RNA content of H . contortus ES , four additional small RNA libraries were generated using ES from adult worms or L4 that had been separated into either EV-enriched or EV-depleted fractions . The number of total reads obtained from each of these samples were lower than that from the unfractionated adult ES RNA library described above . In total , the four libraries contained over 23 million reads that mapped to the H . contortus genome . Table 4 shows the proportion of different RNA species mapping to known Haemonchus RNAs , as defined in Winter et al . , [5] . For the L4 stage , the EV-depleted library showed enrichment of miRNA reads , which was not observed with the L4 EV-enriched library , nor in the adult libraries . Reads were also mapped to nematode sequences in miRBase version 21 and to the H . contortus genome by LC Sciences , as described in Methods . S3 Table shows the normalised reads for the miRNAs in the EV and EV-depleted supernatant libraries mapped to all nematode miRNAs in miRBase . This list also includes star strand miRNAs , such as hco-miR-5960-3p , which , while not specifically stated in the miRBase database , can be inferred from the precursor sequence . Using these data , an analysis of the abundance of specific miRNAs in EV-enriched versus EV-depleted ES material was carried out . Some miRNAs , such as the cluster member hco-miR-5895-5p , were present at similar levels in both adult EV and EV-depleted libraries , as were hco-miR-5899-3p and hco-miR-40b-3p . In contrast , other miRNAs were abundant in adult EV but had negligible read counts in the adult EV-depleted libraries ( see S3 Table ) . These data are presented by hierarchical clustering in Fig 7 and demonstrate the enrichment of specific miRNAs in different ES libraries . In particular , this analysis highlights the relative abundance of certain miRNAs in the L4 EV-enriched library . In the L4 libraries , hco-miR-5885a-3p , hco-miR-5885b-3p , hco-miR-5885c-3p , hco-miR-5908-3p , hco-lin-4-5p , hco-miR-83-3p and sequences homologous to cel-let-7-5p and asu-miR-100a-5p , not previously identified from H . contortus , are all highly expressed in the L4 EV-enriched and have lower read counts in the L4 EV-depleted libraries . This is consistent with data on secreted miRNAs of H . polygyrus that showed enrichment of let-7 , mir-100 and mir-60 in the vesicle rather than supernatant fraction [9] . On the other hand , hco-miR-5960-5p , hco-miR-45-3p , hco-miR-5352-3p , hco-miR-87a-3p and hco-miR-84a-5p are all enriched in the L4 EV-depleted libraries . The data in Table 3 highlighted differences in the expression profile of miRNAs between the adult total ES and the adult whole worm libraries with regard to read numbers . However , this table does not show the changes in the miRNA abundance across life stages or their presence in gut tissue . It was therefore of interest to determine if the expression of miRNAs in the ES correlated with their developmental expression in different life-cycle stages and gut localisation . The data from the small RNA libraries were cross-referenced with the microarray data generated previously . Each library was sorted by maximum reads and the ten most abundant miRNAs were selected and compared to the microarray signal . A number of miRNAs did not have corresponding microarray data as they are not considered as miRNAs and are not in the miRBase database and were excluded from the analysis . S4 Table shows the ten miRNAs that were most highly expressed in the adult total ES library . hco-miR-5960-5p and 3p were the first and second most abundant , respectively , and both are more highly expressed in the adult male compared to the female . Both versions of hco-miR-5960 are highly expressed in the H . contortus gut , dissected from female worms . hco-miR-5960-3p also differs slightly from the 5p version by being highly expressed in the L4 stage as well . Of the remaining eight miRNAs , only hco-miR-228-5p was highly expressed in the L3 stage , the remaining miRNAs having low expression in the L3 stage . These eight miRNAs also showed higher expression in the adult female compared to the male and were not abundant in the gut tissue . Overall , this suggests that most of the abundant miRNAs present in the adult worm total ES are not released from the gut . A similar pattern was observed for the ten most abundant miRNAs in the adult EV-enriched or adult EV-depleted supernatant ( S5 Table and S6 Table , respectively ) . In contrast , 40–50% of the top ten miRNAs in the L4 EV and EV-depleted libraries were enriched in gut tissue ( S7 Table and S8 Table , respectively ) To investigate whether EV containing miRNAs were also present in a nematode closely related to H . contortus , EV were purified from the ES of the L4 stage of T . circumcincta exactly as described previously [16] , and a small RNA library generated , as described for H . contortus . The absence of any available T . circumcincta miRNA data necessitated mapping reads from the small RNA library to the entire miRBase database , including H . contortus sequences ( miRBase release 21 ) . BLAST was used to query the miRBase mature sequence database with the reads from the T . circumcincta small RNA library . This method identified 13 , 637 miRNAs across 190 different species . S9 Table shows a comparison of the miRNAs found in T . circumcincta L4 EV and H . contortus L4 EV . It is notable that of the top 20 most abundant T . circumcincta EV miRNAs , 14 were found in the H . contortus L4 EV library . Interestingly a number of these ( asu-miR-100a-5p , miR-60-3p , miR-71-5p , let-7-5p , lin-4-5p and miR-5885a , b , c-3p ) were also present in EV libraries prepared from H . polygyrus adult stage [9] , while miR-100a-5p and let-7-5p were recently identified in adult EV of the clade I nematode Trichuris muris [21] . This suggests there is some conservation of the repertoire of nematode miRNAs packaged into EVs .
In this study we characterised a cluster of four closely linked miRNAs from the pathogenic nematode H . contortus . The miR-5352 cluster was shown to be present in an additional ten parasitic nematodes , nine of which were clade V species . None of these sequences had previously been reported as miRNAs and none are included in miRBase , with the exception of the Ascaris miR-5352 and miR-43 sequences . The genome of this clade III species contained only miR-5352 and miR-43 , based on current sequence data , as did T . canis , consistent with its close relationship to Ascaris . However , the cluster is not present in other clade III nematodes , such as Brugia spp . nor is it present in free-living clade V nematodes such as C . elegans . As the presence of the cluster was not related to nematode clade , we hypothesised that it may be associated with the anatomical site in which the adult worm lives . A common feature of all the nematodes that possess the miR-5352 cluster is that the adult parasites are not tissue dwelling but reside in the gastro-intestinal tract of the host , with the exception of the bovine lungworm , D . viviparus . The adult worms of this species parasitize the lungs of the bovine host , although the larvae do traverse the intestine . These results indicate that the presence of the cluster may relate to life in the gut or at a mucosal site . Of the nematodes that possess the miR-5352 cluster , both H . polygyrus and T . circumcincta have multiple versions of the miR-5352 cluster . The high similarity between these sequences and their proximity in the genome suggest that they may have arisen through gene duplication and divergence , the significance of which is currently unknown . Alternatively , the apparent presence of multiple copies of the cluster may be explained by poor genome assembly , which can be verified from analysis of newer assemblies as these become available . From the H . contortus transcript microarray and qRT-PCR data [5] , the pattern of expression of each of the miRNAs in the hco-miR-5352 cluster was very similar: all four were expressed at low levels in the larval stages , rising dramatically in the adult male and female worms . These data imply that the cluster is likely to be co-ordinately expressed and may indicate a stage-specific function for these small RNAs within adult worms or in interacting with their immediate environment . As the presence of the cluster correlates with the anatomical site in the host , it seemed plausible that some of the miRNAs in the miR-5352-cluster may be secreted and thus have the potential to interact with host cells . Recent data from studies on other parasitic helminths have shown that miRNAs are frequently present in ES products and can be packaged within EV or exosomes [9 , 12 , 16 , 21–23] . As the gut is thought to be the site from which EV are released in H . polygyrus worms [9] , and is the site of intense metabolic activity in adult H . contortus [14 , 24] , the gut was dissected from adult female worms and analysed for miRNA expression . While some miRNAs were highly expressed in gut tissue , the four members of the hco-miR-5352 cluster were not , suggesting that they are unlikely to be gut-derived . However , miR-5352 could be detected in eggs/hatched L1 of H . contortus collected from in vitro cultures at a similar level to that detected in the whole female worm , suggesting that the embryos or the gonad may be the major source of this miRNA in female worms and possibly also ES . Initially , a small RNA library was generated from the ES products of mixed sex adult H . contortus and the small RNA content of the worm ES library compared with that of the whole adult worm , as published previously [5] . A much higher percentage of reads in the ES library mapped to ribosomal RNAs than from the adult worm library , a feature that has been noted in other helminth ES libraries [9] . In addition , in S . mansoni ES products , Nowacki et al . , [12] identified a class of tRNA-derived small RNAs , which were hypothesised to be a possible source of additional small non-coding RNAs . These tRNAs fragments were derived from mature tRNAs and could inhibit protein synthesis by interfering with the cap binding complex eIF4F [25] . Direct comparison of the miRNA sequences and their relative abundance in adult worms and their ES products showed that all four members of the miR-5352 cluster appeared in the top ten miRNAs in adult worm ES indicating that these are secreted in vitro . However , there were clear differences in the abundance of miRNAs in adult extract and those released in ES . These data suggest that there may be selectivity in the release of miRNAs by adult worms and that some miRNAs could have the potential to interact directly with host cells rather than functioning within the parasite . To determine whether miRNAs of the miR-5352 cluster may be released during infection , tissues from the abomasum epithelium and draining lymph nodes were analysed for the presence of hco-miR-5352 and hco-miR-5895 , as well as hco-miR-5960 , a miRNA that , while not a cluster member , is abundantly secreted in vitro . Elevated levels of both cluster miRNAs were detected in ex vivo tissue from H . contortus infected sheep , indicating that these miRNAs are secreted in vivo and that they have the potential to interact with and influence the function of cells within both the site of infection and regional lymph nodes . In contrast hco-miR-5960 could not be specifically detected , suggesting that it may not be secreted in vivo , or , alternatively , that the levels of secretion in vivo are too low to detect . The apparent enrichment of hco-miR-5352 in the draining lymph node compared to the abomasal tissue is consistent with the filtering action of the lymph node . Ultrastructural analysis showed that adult H . contortus ES contained many small vesicles , consistent in size and appearance with the extracellular vesicles described from other helminth parasite ES products [9 , 16] , although in the present study these were not further characterised . Tzelos et al . , [16] showed that the EV fraction from L4 T . circumcincta was recognised by serum from infected animals , implying that EV are secreted in vivo . The presence of miRNAs in EV from H . contortus ES was confirmed by sequencing small RNA libraries prepared from EV-enriched or EV-depleted ES from adult or L4 worms . Hierarchical clustering analysis clearly differentiated groups of miRNAs that were enriched in one population or another . While the EV-enriched library from the L4 stage contained fewer miRNA reads than the EV-depleted fraction , certain miRNAs were more abundant in the L4 EV fraction , perhaps indicating a role in the L4 stage . At day 7 p . i . , the L4 are closely apposed to the gastric glands of the abomasum , where the parasite may have direct access to host tissues . Studies on the release of EV from the L3 and adult stages of B . malayi showed that L3 secrete an abundance of EV in vitro compared to adult worms , indicating that EV release may be preferentially associated with larval stages , at least in Brugia [11] . However , further comparative studies are required to better understand stage-specific release of both EV and miRNAs in different life cycle stages of H . contortus . It is interesting to speculate as to the source of miRNAs , and EV , within the worm . Although none of the four miRNAs of the miR-5352 cluster were enriched in female gut tissue , it was notable that of the ten most abundant miRNAs present in either L4 EV-enriched or L4 EV-depleted , most were highly expressed in adult worm gut tissue . This observation suggests that these abundantly secreted miRNAs may be released from the worm intestine , as suggested previously for H . polygyrus EV [9] . L4 stages and particularly adult H . contortus worms are voracious blood feeders and EV may be released by regurgitation while feeding . We identified a number of miRNAs common to EVs of H . contortus and T . circumcincta , and a subset of these was also present in EVs of H . polygyrus [9] , as well as Brugia L3 larvae [11] . This observation suggests evolutionary conservation for release of specific miRNAs . Interestingly , some of these ( let-7 , miR-100 ) have identity within the seed region to mammalian miRNAs , implying that they could potentially regulate host genes [21] , or alternatively compete in host miRNA-mediated regulation . In conclusion , the data presented here support the hypothesis that selected miRNAs are released by parasitic nematodes both within EV and free of EV . Further studies are required to elucidate how miRNAs are secreted , particularly those not associated with EV . Are these in complexes with an Argonaute or otherwise stabilised by association with other proteins as has been observed in serum , plasma , and other body fluids [26 , 27] . For example , miRNAs have been identified in plasma , in fractions of high-density lipoprotein ( HDL ) . Furthermore , HDL can deliver miRNAs to recipient cells and lead to altered gene expression [28] . The detection of two H . contortus miRNAs in host tissue provides further evidence of a possible role for miRNAs in parasitism , at least for those parasites that inhabit the gut . Further studies with H . contortus are underway to establish how miRNAs are released by the parasite and whether they have the ability to interact with host cells to influence expression of immune response genes , as described for H . polygyrus .
|
Different species of parasitic worms release microRNAs , small non-coding RNA molecules , some of which are known to interact with host genes to alter the immune response . We characterized a cluster of four microRNAs from Haemonchus contortus , an important parasitic nematode of livestock . The miRNA cluster appeared to be present only in nematode worms that inhabit the intestinal tract and members of the cluster could be detected in the excretory-secretory products of adult H . contortus . Some of these miRNAs were also detected from the site of infection within the sheep host and from the draining lymph node suggesting that they may be released in vivo as well as in vitro . Analysis of excretory-secretory products released from adult worms and L4 larval stages demonstrated that specific miRNAs were contained within EV . These findings support a role for secreted miRNAs in the host-parasite relationship .
|
[
"Abstract",
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2017
|
Conservation of a microRNA cluster in parasitic nematodes and profiling of miRNAs in excretory-secretory products and microvesicles of Haemonchus contortus
|
RNA silencing mediated by small RNAs ( sRNAs ) is a conserved regulatory process with key antiviral and antimicrobial roles in eukaryotes . A widespread counter-defensive strategy of viruses against RNA silencing is to deploy viral suppressors of RNA silencing ( VSRs ) , epitomized by the P19 protein of tombusviruses , which sequesters sRNAs and compromises their downstream action . Here , we provide evidence that specific Nicotiana species are able to sense and , in turn , antagonize the effects of P19 by activating a highly potent immune response that protects tissues against Tomato bushy stunt virus infection . This immunity is salicylate- and ethylene-dependent , and occurs without microscopic cell death , providing an example of “extreme resistance” ( ER ) . We show that the capacity of P19 to bind sRNA , which is mandatory for its VSR function , is also necessary to induce ER , and that effects downstream of P19-sRNA complex formation are the likely determinants of the induced resistance . Accordingly , VSRs unrelated to P19 that also bind sRNA compromise the onset of P19-elicited defense , but do not alter a resistance phenotype conferred by a viral protein without VSR activity . These results show that plants have evolved specific responses against the damages incurred by VSRs to the cellular silencing machinery , a likely necessary step in the never-ending molecular arms race opposing pathogens to their hosts .
Plants fight microbial attacks using both constitutive and induced defenses , which include basal and highly specific resistance [1] . Basal resistance , or PTI ( for PAMP-Triggered Immunity ) , often relies on the detection of highly conserved signature molecules that include fungal polysaccharides or bacterial flagellin , collectively termed pathogen-associated molecular patterns ( PAMPs; [1] , [2] ) . To circumvent this first layer of defense , many host-adapted microbes produce effector proteins that suppress various steps of PTI [3] . As a counter-response , plants have , in turn , evolved classes of specialized receptors called resistance ( R ) proteins that directly detect pathogen's encoded suppressors of PTI , or that sense the molecular consequences of their adverse action on defense-related host factors . R protein activation triggers potent defense responses collectively named Effector Triggered Immunity ( ETI ) that often –albeit not always ( see below ) culminate in Hypersensitive Response ( HR ) , a rapid and localized cell death process thought to limit or preclude pathogens' growth [1] , [2] . As a consequence of the gene-for-gene type of interaction linking these two components , plant R genes and their corresponding pathogen-encoded virulence factors evolve constantly and rapidly , so that HR , a common and ultimate manifestation of ETI , is usually only observed in specific plant species infected with specific pathogen strains . The plant hormones salicylic acid ( SA ) , ethylene and jasmonic acid ( JA ) are crucially implicated in signaling networks underpinning both PTI and ETI [1] , [2] , [4] , [5]; antimicrobial pathogenesis-Related Proteins ( PRs ) , which include taumatine-like proteins and chitinases , are also often induced by both pathways and constitute , therefore , typical molecular markers of pathogen-induced defenses [6] . Although the occurrence of HR is classically used to discern PTI from ETI during bacterial or fungal infections [7] , an HR-independent process known as Extreme Resistance ( ER ) is activated by a number of R proteins during ETI against viruses; ER is characterized by the lack of detectable accumulation of the triggering virus , and is accompanied by the onset of a broad-spectrum antiviral state in the absence of macroscopic or microscopic cell death lesions [8]–[11] . RNA silencing is a conserved regulatory process that has evolved as an antiviral and antimicrobial defense mechanism in plants and animals [12]–[17] . Common features of RNA silencing across organisms include the involvement of double-stranded ( ds ) RNA as an initiator molecule , and accumulation of 21–24 nt small ( s ) RNAs that are processed from dsRNA by the RNAse III-like enzyme Dicer [18]–[20] . sRNAs are then incorporated into Argonaute ( AGO ) -containing effector complexes termed RNA-induced Silencing Complexes ( RISCs ) and , in case of extensive sequence complementarity between sRNA guide and target , AGO catalyses cleavage of the target RNA . Arabidopsis thaliana possesses four Dicer-like ( DCLs ) and ten AGO proteins [21] , among which DCL4 and its surrogate DCL2 , as well as AGO1 and AGO2 , play essential roles as processors and effectors of virus-derived short interfering ( si ) RNAs , respectively [22]–[29] . DCL1- and AGO1-dependent micro ( mi ) RNAs produced from endogenous loci regulate the expression of many transcripts displaying miRNA sequence-complementarity , including mRNAs for transcription factors , enzymes , and regulators of PTI induced , notably , by bacteria [15]–[17] , [30] , [31] . As a consequence of these multiple RNA silencing-based defense layers , plant viruses , pathogenic bacteria , oomycetes and , possibly , fungi , have evolved suppressors of RNA silencing ( SRs ) that apparently target many steps of the siRNA and miRNA pathways [14] , [32]–[37] . SRs are highly diverse in sequence , structure , and activity , and single SRs may target multiple points in RNA silencing pathways [14] , [31] . Several viral SRs ( VSRs ) are known to affect AGO1 function [14] . For example , The Beet western yellows virus P0 protein was suggested to act as an F-box protein targeting AGO proteins for degradation , thereby preventing RISC assembly [38]–[40] . Turnip crinckle virus P38 was recently shown to bind directly and specifically AGO1 through mimicry of host-encoded glycine/tryptophane ( GW ) -containing proteins normally required for RISC assembly/function in diverse organisms [41] , [42] . Physical sequestration of siRNAs is another common property of VSRs in vitro [43]–[47] , although the extent to which this specific feature contributes to effective RNA silencing suppression in vivo remains unclear [42] . The most compelling example of active silencing suppression mediated by siRNA binding is provided by the tombusvirus P19 protein , of which the closely related Tomato bushy stunt virus ( TBSV ) and Carnation Italian Ringspot virus ( CIRV; 97% identity ) are the type representatives . Following its original discovery as a VSR [35] , P19 was co-crystalized as a head-to-tail homodimer in direct association with an siRNA duplex [43] , [48] . Supporting a direct and critical contribution of homodimerization and siRNA binding to the P19 VSR activity , stable point mutant alleles of the proteins lacking either property display complete loss-of-VSR-function phenotypes in both virus-infected and transgenic plants [43] , [49]–[51] . sRNA binding by P19 also explains why its constitutive expression in Arabidopsis promotes developmental defects resembling those of plants carrying mutations in miRNA pathway components . Indeed , it was shown that P19 binds endogenous siRNAs and miRNAs , incurring , in the process , misregulation of the cognate endogenous targets of these molecules [42] , [52] . Remarkable parallels can be drawn between the general framework of silencing activation and its suppression by pathogens on the one hand , and the classical PTI-ETI scheme for resistance , on the other . This has prompted the suggestion that the two processes might be , in fact , manifestations of similar , if not identical , phenomena [31] , [53] . In the case of ( + ) -stranded RNA viruses , for example , viral-derived dsRNA can be assimilated to a PAMP because this molecule is a mandatory product of viral replication . Similarly , the Dicer/AGO consortium orchestrating the antiviral reaction may be conceptually compared to the first defense layer underlying PTI [31] . Pursuing the comparison one step further and taking into account that VSRs are virulence effectors , it can be anticipated that the damages incurred by VSRs to the cellular silencing machinery may be sensed by host-encoded functions comprising , perhaps , dedicated R genes; the effects of such functions would thus be diagnosed , at least partly , by the typical outputs of ETI , including HR [31] . Supporting this notion , at least three VSR proteins from distinct virus families are known to trigger HR-like lesions in a host-specific manner [53]–[58] . It remains largely unknown , however , if these responses are stimulated by intrinsic silencing suppression properties or by other , unrelated functions of the viral proteins involved . Also unclear is whether virus resistance is effectively triggered upon recognition of these VSRs in these specific hosts , and to what extent the output of the induced defense compares with that of classical ETI . The present series of experiments was aimed at addressing these various issues using the well-characterized P19 VSR in tobacco . The results support the idea that RNA silencing and its suppression by viruses can be effectively rationalized within the frame of PTI-ETI , since we demonstrate , in authentic infection contexts , that ( i ) tombusviral virulence ( ii ) suppression of RNA silencing and ( iii ) induction of an ER-type of resistance with molecular features of ETI are all dependent upon the ability of P19 to bind sRNAs . Collectively , the data support the existence of host-encoded sensors that monitor the status/integrity of key RNA silencing components in plants . We propose , consequently , that perturbation of these components by pathogen-encoded SRs may activate potent ETI-like resistance responses . This proposed host counter-counter defensive layer likely constitutes an important driver in the evolution and diversification of SRs from viruses and perhaps other parasites .
TBSV P19 was shown to induce a HR-like response in N . tabacum and other Nicotiana species; a host-specific response strongly evocative of R gene-mediated ETI [54]–[57] , To ascertain further if , indeed , P19 acts as an elicitor of immune responses , we generated transgenic N . tabacum cv . Xanthi lines expressing P19 under the GVG glucocorticoide inducible promoter , which is activated by dexamathazone ( Dex::P19; [59] ) . The expression of P19 was quantified in two independent lines 0 , 12 and 24 hours post Dex application ( hpp ) ; non-transgenic plants sprayed with DEX provided a negative control . While very low P19 transcript accumulation was observed before DEX treatment in the two transgenic lines , it was up to 4000 times higher following DEX application , at 12 and 24 hpp , compared to 0 hpp and to DEX-treated non-transgenic plants ( Figure 1A ) . Accumulation of the P19 protein , mostly under homodimeric form , was also detected by Western analysis in the DEX-induced transgenic lines , but not in non-transgenic lines , using a polyclonal P19 antibody ( Figure 1B ) . Accumulation of P19 following DEX induction correlated with the onset of three key markers of plant defense responses: ( i ) the progressive development of HR-like lesions in the sprayed areas of leaves , ( ii ) the accumulation of distinct PR proteins , PR1 , PR2 and PR3 , at 24 and 48 hpp ( Figures 1C–D ) , and ( iii ) the accumulation of salicylic acid ( SA ) which was 4–5 times higher following DEX application at 24 hpp in the DEX-induced transgenic lines compared to 0 hpp and to DEX-treated non-transgenic plants ( Figure S1 ) . Collectively , therefore , the results presented in Figure 1 and Figure S1 suggest that in N . tabacum , P19 effectively acts as an elicitor of plant defense responses displaying at least superficial characteristics of ETI . To test if P19 effectively induces resistance against TBSV in N . tabacum , Agrobacterium strains expressing either TBSV-GFP or TBSVΔP19-GFP , which is unable to express P19 [26] , were used to inoculate leaves of 5-week old N . tabacum . At 5 days post-infiltration ( dpi ) , virus accumulation was monitored under UV light via the appearance of green fluorescence in infiltrated leaves , and by Western analysis using an anti-GFP antibody . Viral replication was assessed directly in parallel by Northern analysis , using a GFP DNA fragment as a probe , which detects both genomic and sub-genomic RNAs of TBSV-GFP . We found that the presence or absence of P19 expression from TBSV-GFP had dramatically contrasted consequences on virus replication . Thus , GFP was not observed ( Figure 2A–B ) and the viral RNAs were below detection limits of Northern analyses ( Figure 2C ) in TBSV-GFP-inoculated leaves . In sharp contrast , however , both GFP accumulation and viral RNA replication were readily detectable in TBSVΔP19-GFP-infiltrated leaves at 5 dpi ( Figures 2A–C ) . To further characterize the P19-mediated defense response , we used trypan blue staining as a diagnostic of cell death . Leaves were thus inoculated either with TBSV-GFP , P50 from Tobacco mosaic virus ( TMV ) , which induces an HR in N . tabacum carrying the resistance gene N ( as a positive control ) , or GUS as a negative control . We found that the visible and microscopic HR observed in P50-treated plants was absent from TBSV-GFP-infected and control leaves ( Figure S2 ) . These results strongly suggest that extreme resistance ( ER ) was triggered in TBSV-GFP-inoculated leaves of N . tabacum , and implicate , therefore , P19 as the elicitor of this defense . In fact , the results obtained here with P19 in tobacco are highly reminiscent of the well-studied interaction between Potato virus X coat protein ( CP ) and the Rx resistance protein in Solanum tuberosum or tobacco [8] . Indeed , while Rx typically confers ER to PVX in the context of authentic virus infections , isolated and prolonged production of CP , for instance via Agrobacterium-mediated transient expression , does trigger an HR in Rx potato genotypes [8] , [9] , as seen previously and here upon transient and transgenic expression of P19 in specific Nicotiana species ( Figure 1B–C , [56] ) . With both PVX and TBSV , the potent antiviral state accompanying the ER ( e . g . Figure 2C ) probably stops virus replication before the CP or P19 have reached the levels required to trigger an HR [8] , [9] , a phenomenon presumably bypassed when both elicitors are produced in a virus replication-independent manner . The potent ( Figure 2C ) and broad-spectrum [8] antiviral state triggered by ER is suspected to underlie the production of defense-related hormones , including SA , which possesses demonstrated antiviral activities [27] , [60] , [61] . The gaseous hormone ethylene is also important for induction of plant immunity [4] . To investigate the possible roles of these compounds in the ER-like resistance induced by P19 against TBSV , SA-deficient transgenic tobacco plants expressing NahG ( Salicylate hydroxylase; [62] ) and plants insensitive to Ethylene ( ETR; [63] ) were inoculated with TBSV-GFP using Agrobacterium-mediated delivery . At 5 dpi , leaves were observed under UV and samples were harvested for Western analysis using the anti-GFP antibody . Unlike WT plants , both transgenic plants failed to display resistance against TBSV ( Figure 3A–B ) and , accordingly , the P19-dependent induction of PR proteins was compromised in NahG plants ( [64] , [65]; Figure S3 ) . Overall , these results indicate that SA and ethylene are required for the ER induced by P19 against TBSV . We then investigated if the HR-like lesions induced by P19 in tobacco leaves ( Figure 1C ) were SA- and/or ethylene-dependent . As seen in Figure 3C , necrosis was as extensive in leaves of NahG and ETR plants as it was in their non-transgenic counterparts at 5 dpi ( Figure 3C ) , indicating that the HR triggered by P19 , unlike the induced antiviral state , is neither SA- nor ethylene-dependent . Resolving the crystal structure of the P19-siRNA complex granted the identification of point mutations that debilitate the protein's VSR function without impacting its stability [43] . It was notably shown that a double mutation affecting tryptophan residues 39 and 42 ( W39-42R ) was sufficient to abolish siRNA binding by P19 in vitro , with the resulting stable mutant allele being unable to suppress RNA silencing in planta [43] . Using the same allele , we thus tested if the capacity of P19 to sequester siRNAs was required for the elicitation of ER in N . tabacum . We generated transgenic N . tabacum cv . Xanthi lines expressing CIRV P19W39-42R under the DEX inducible promoter ( DEX::P19W39-42R ) . Expression of P19W39-42R was quantified in two independent lines 0 , 12 and 24 hours after DEX application; transgenic line Dex::P19#1 , expressing WT P19 ( Figure 1B ) , was used as a reference for functional P19 levels in these experiments . Upon DEX application onto leaves of five week old plants , quantification of both mRNA ( Figure 4A ) and protein ( Figure 4B ) levels showed that accumulation of the P19 mRNA and of P19 homo-dimers was similar in the two independent DEX::P19W39-42R tobacco lines tested and in the Dex::P19#1 reference line ( Figure 4A–B ) . Remarkably , P19W39-42R was neither able to induce SA accumulation , HR-like symptoms nor to promote accumulation of PR1 , PR2 and PR3 compared to WT P19 ( Figure 3C–D and Figure S1 ) , suggesting that small RNA binding by P19 is necessary to trigger the onset of defense in N . tabacum . The results also show that defense elicitation can occur independently of virus infection , suggesting that binding of endogenous sRNAs by P19 is prerequisite for elicitation . The above results prompted us to investigate if silencing suppression via sRNA binding was sufficient , per se , to trigger the HR-associated defense response elicited by P19 in N . tabacum . To that aim , we used Agrobacterium strains producing various VSRs unrelated to P19 . HcPro from Tobacco etch virus , P15 from Peanut clump virus and P21 from Beet yellows virus are all known to bind sRNAs in vitro , with high affinity for 21 nt-long species ( Figure 5A; [45] ) . In the same in vitro assay , P14 from Pothos latent virus was shown to bind different sizes of sRNAs ranging from 21 nt to 26 nt , while P25 from PVX was , by contrast , devoid of sRNA binding activity ( Figure 5A; [45] ) . We found that , unlike P19 , neither of the above VSRs was able to trigger the HR-like response at 5 dpi following their transient expression in leaves of N . tabacum ( Figure 5B ) . Nonetheless , in a well-established silencing suppression assay based on transient co-expression of a silencing GFP target transgene with VSRs [66] , all of these proteins were clearly able to stabilize GFP accumulation , as assessed under UV illumination ( Figure 5C ) and by Western analysis ( Figure 5D ) . By contrast , GFP accumulation remained low in tissues co-infiltrated with a control Agrobacterium strain expressing the GUS reporter gene ( Figure 5C–D ) . Thus , all the VSRs tested were able to suppress GFP RNA silencing in this assay . The results indicate that the failure of the P19-unrelated VSRs to trigger an HR-like response cannot be explained by their inability to suppress RNA silencing in N . tabacum . Therefore , RNA silencing suppression is , in itself , insufficient to trigger this response . Moreover , given the documented high affinity of some of the VSRs used for siRNAs [42] , [45] , [67] the data suggest that sRNA binding per se is also insufficient to promote defense in N . tabacum . The most parsimonious interpretation of these results entails , therefore , that P19-mediated elicitation of host defenses in Nicotiana species involves the specific recognition of P19-sRNA complexes , or of downstream molecular events triggered by the specific association of both components . Even though none of the above-tested VSRs triggered , on its own , a defense response in N . tabacum , the intrinsic abilities of most of these proteins to bind sRNAs predicted that their co-expression with P19 would compromise the onset of HR-like lesions observed in Agrobacterium-infiltrated tissues ( Figure 1C ) . As shown in Figure 5E , this was indeed the case: the appearance of necrotic tissues was significantly delayed and less extensive at 96 hours in leaf patches that had received the P19-VSR co-treatments compared to leaves co-treated with P19 and GUS as a negative control ( Figure 5E ) . Remarkably , the delayed onset of HR was not observed in co-treatments involving P19 and the P25 protein of PVX , which , unlike all the other VSRs tested , does not bind sRNAs in vitro ( [45]; Figure 5E ) . Western analyses employing a P19 antibody also confirmed that the delayed onset of HR was unlikely to be a consequence of altered levels of P19 homodimers in the P19-VSR co-treated leaves , compared to control leaves ( Figure 5F ) . Given that P14 , P15 , P21 and Hc-Pro are all known to bind sRNA , at least in vitro [45] , we assessed whether the compromised HR-like cell death phenotype observed upon concomitant expression of P19 with these VSRs resulted from a direct competition for sRNA binding , potentially decreasing the amount of P19-siRNA complexes . To address this point we transiently expressed , in N . benthamiana , a HA-tagged version of P19 ( P19HA ) , either alone or in combination with P15 or P21 ( Figure 6 ) . As a source of siRNAs , we used a 35S promoter-driven inverted-repeat ( IR ) construct , corresponding to the 5′ part ( ‘GF’ ) of the GFP sequence , which is processed into 21 nt- and 24 nt-long siRNAs . Northern analysis of the sRNA fraction of P19HA immunoprecipitates showed that , as expected , P19 specifically bound the 21 nt-long GF siRNAs . Both P15HA and P21HA displayed the same 21 nt siRNA size preference as P19 for binding . However , P21 sequestered 21 nt siRNAs significantly more efficiently than the two other VSRs , as shown by the much stronger signal detected in P21HA immunoprecipitates ( Figure 6 ) . This most likely explains the decreased GF siRNA levels observed in P19HA and P15HA immunoprecipitated fractions when these VSRs were concomitantly expressed with P21 ( Figure 6 ) . However , in contrast to P21 , P15 did not alter the amount of siRNA bound by P19 whereas P19 prevented P15 siRNA binding and competed with P21 siRNA binding ( Figure 6 ) . Therefore , in the case of P15 , the compromised P19-triggered HR-like cell death phenotype is unlileky to result from a reduction in the amount of formed P19-siRNA complexes . Overall , these results show that , although necessary , the sRNA binding capacity of P19 is not sufficient for host defense elicitation in N . tabacum , suggesting that the onset of ER is intrinsically linked to the VSR function of P19 and not just the formation of P19-siRNA complexes per se ( Figure 3 ) . To further ascertain this idea , we took advantage of the fact that Rx-mediated ER is triggered by the PVX-encoded CP protein , which does not display any intrinsic VSR activity [68] . Moreover , Rx-mediated ER can be recapitulated in transgenic N . tabacum upon inoculation of PVX-GFP using leaf-infiltration of Agrobacterium . We reasoned that , unlike in the above example where resistance was highly dependent upon the VSR function of the P19 elicitor , Rx-mediated resistance would remain unaffected by co-expression of VSRs with PVX-GFP . As shown in Figure 7 , accumulation of Agrobacterium-delivered PVX-GFP was abolished in leaves of plants expressing transgenic Rx , compared to non-transgenic plants . Furthermore , this pattern remained unaffected by transient co-expression of HcPro , P21 , P15 P14 VSRs , or a control GUS transgene ( Figure 7 ) .
Cross-talk between RNA silencing pathways and both PTI and ETI pathways has been established experimentally in the case of bacterial pathogens [12] , [15] . In all cases so far , PAMP recognition activates endogenous RNA silencing pathways to target negative regulators of disease resistance , leading to potentiation of basal defense [12] , [15] , [31] . Bacterial-encoded SRs , in turn , target this basal defense by inhibiting various , and perhaps multiple , steps of host silencing pathways . The work presented here describes how the activity of the viral suppressor P19 is sensed in specific Nicotiana species to induce immunity against the P19-producing virus . This immunity displays several key attributes of ETI , including the involvement of SA and ethylene , as well as the production of PR proteins . The timing of P19 homodimers accumulation correlates with the extent of cell death and PR proteins production; this is in agreement with data showed previously in which the authors used the same inducible promoter as the one we used in this study [69] . Remarkably , antiviral immunity is also accompanied by a lack of visible HR-like lesions , at least in the context of authentic tombusvirus infection , a phenomenon highly reminiscent of extreme resistance ( ER ) observed , for instance , during the CP-Rx interaction in PVX-infected plants . Further supporting the analogy between P19-mediated defense and the ER triggered by Rx , strong and isolated expression of their respective elicitors ( i . e . P19 or CP , respectively ) promotes the appearance of HR-like lesions in both cases . Nonetheless , a marked difference between the Rx-CP and the P19 systems is the reliance of the latter upon RNA silencing suppression , a function not associated with the CP of PVX [68] . Our findings were , in fact , not completely unprecedented . Hence , the P38 capsid protein of Turnip crinkle virus binds AGO to inhibit its loading with sRNAs [41] , [42] , [70] . P38 was also shown to induce HR-associated defense responses in the Arabidopsis ecotype Dijon-0 and its inbred derivative Dijon-17 [71] , [72] , a level of host specificity that strongly evokes an ETI-type of response . The elicitor of the N resistance gene , which confers ETI to TMV , had been also mapped to the p50 helicase subunit of the viral replicase , p126 . Remarkably , the same domain of p126 was recently identified as being sufficient to suppress RNA silencing in N . benthamiana [73] . Moreover , the helicase enzymatic activity of p50 was found dispensable for both N-mediated resistance and silencing suppression , suggesting that the VSR activity of P50 might stimulate ETI via the activation of N . Seminal work carried out more than a decade ago also provided key insights into the potential contribution of the 2b protein from Tomato aspermy cucumovirus ( TAV2b ) to the induction of ETI , possibly through its VSR activity . Indeed , when expressed from recombinant TMV , TAV2b was found to activate strong host resistance in tobacco , typical of the gene-for-gene interaction linking R proteins to their elicitors [53] . Moreover , the N-terminal region of TAV2b was found critical for both VSR activity and resistance elicitation , suggesting that the same or overlapping domains of the protein are involved [53] . Interestingly , Chen et al . [74] recently showed that Tav2b effectively binds sRNAs , highly reminiscent of the situation presented here with P19 . The seminal observation made with TAV2b led the authors to suspect that RNA silencing and its suppression on the one hand , and ETI on the other , were probably linked phenomena , at least in some cases; this view became strongly substantiated through subsequent work conducted with plant pathogenic bacteria ( reviewed in [31] ) . The data obtained in this manuscript add further strength to this idea by showing the importance of RNA silencing suppression in the resistance mediated by P19 , because immunity to TBSV was only achieved if the protein retained its capacity to suppress gene silencing , for which sRNA binding is a prerequisite . We suspect that the reported ETI-like response triggered by P19 in the absence of visible HR might also strongly contribute to its additional , albeit poorly understood , role as a host-specific determinant of systemic viral movement [51] , [75] . This hypothesis is particularly appealing given the involvement of SA and ethylene in the P19-elicited response in N . tabacum . Indeed both hormones are known to mediate , directly or indirectly , systemic , in addition to localized , defense responses . Immune signaling pathways seem to be widely conserved across fungal , bacterial and viral interactions that lead to ETI in plants . The fact that P19-mediated resistance was compromised by many unrelated VSRs , unlike resistance activated by Rx argues , therefore , against an interference at the level of disease resistance signaling . Moreover , the PVX coat protein ( elicitor of Rx ) does not possess VSR function [68] . The fact that the integrity of the P19 binding domain is required for defense elicitation , together with the failure of PVX P25 , among the VSR tested here , to alter the P19-mediated HR response , suggests that sRNA binding , required for VSR function , is a key component for defense activation in N . tabacum . It is , however unlikely to be sufficient , because none of the other VSRs tested was able to recapitulate , on its own , the defense phenotype induced by P19 when transiently expressed , despite that many of them bind sRNA in vitro and probably in vivo . Additionally , P15 could suppress the P19-mediated HR even though it did not outcompete P19 for siRNA binding in the N . benthamianan transient expression assay . The simplest interpretation of these results , therefore , is that P19 dimers complexed with sRNAs initiate a signal that is specifically sensed in N . tabacum to trigger extreme resistance against TBSV or that a conserved motif or structure important for sRNA binding by P19 is sensed in the plant . A non-mutually exclusive possibility holds that sensing occurs downstream , as a consequence of specific P19-sRNA association in a manner suppressed by the action of VSRs such as P15 , which may share downstream silencing targets with P19 including AGOs . Interestingly , HR-like lesions and PR proteins accumulation could be triggered by P19 in the absence of a viral infection , suggesting that endogenous sRNAs , including siRNAs and miRNAs , which are effectively bound by P19 together with viral-derived siRNAs during infection [43] , [49] , [67] , [76] , form one component of the trigger . Hence , a recent study in transgenic Arabidopsis shows that binding of endogenous miRNAs by VSRs is much less widespread than was originally anticipated . In fact , P19 was , among many VSRs tested ( including several used in the present study ) , the only protein to prevent loading of miRNAs into AGO1 . By contrast , all of the VSRs tested could effectively prevent loading of exogenous siRNAs into AGO1 [42] . This peculiarity may contribute to explain the specific ability of P19 to trigger HR-like lesions and ER in N . tabacum . It is also possible that the binding of P19 to si/miRNAs promotes a specific change in the integrity or conformation of silencing effector proteins , including AGOs , and that these changes are sensed in a host-specific manner . miRNAs have roles in plant basal and race-specific resistance against bacterial pathogens [15] , [16] , [77] . Furthermore , some plant miRNAs appear to have evolved to control R gene expression presumably to prevent the known fitness cost of their constitutive expression in the absence of pathogens [78]–[80] . For example , nta-miR6019 ( 22-nt ) and nta-miR6020 ( 21-nt ) guide the cleavage of the TIR-NB-LRR N transcript from tobacco , which confers resistance to Tobacco mosaic virus [79] . Likewise , Sl-miR482 attenuates expression of a large family of NBS-LRR genes from tomato and its accumulation is decreased in plants infected with Turnip crinkle virus , Cucumber mosaic virus , Tobacco rattle virus and Pst DC3000 [80] . Therefore , given the above context , miRNA sequestration by P19 might generally enhance host immune responses induced by virulent and avirulent pathogens . Interestingly , however , miR168 , which targets the antiviral silencing effector AGO1 , is specifically not sequestered and , in fact , induced by P19 , suggesting that , in this case , miRNA binding by P19 favours viral infection without activating immune responses [81] . We have shown here , with the P19-N . tabacum model , that the general scheme of silencing induction and suppression by plant viruses can be readily accommodated within the classical frame of ETI/PTI . In particular , our study sheds light on an additional layer of defense , whereby hosts can sense and respond to the damages caused by VSRs to the cellular silencing machinery . The existence of this additional layer is also consistent with the fast evolving and highly diverse nature of VSRs . Indeed , potent host counter-counter-defense measures probably impose strong selective pressure on pathogens to accelerate or refine the modeling of their virulence factors , thereby contributing further to the never-ending arms race opposing parasites to their hosts . A future challenge will be to assess the extent to which the phenomenon described here is shared not only among plant-virus , but also plant-bacteria , plant-fungal and plant-oomycetes interactions , and how elucidation of its biochemical and genetic underpinnings might improve our understanding of PTI and ETI at large . Finally , and most importantly , a strong -albeit still speculative- implication of our results is the existence of dedicated host-encoded R proteins that should monitor the status of key RNA silencing components in plants , and perhaps other organisms . Identifying these elusive silencing-associated R proteins and their guardees would certainly constitute a major breakthrough in the field .
Wild type and transgenic plants were grown under conditions of 8 h darkness at 19°C , 16 h light at 22°C , with 70% relative humidity . Independent tobacco ( N . tabacum ) transgenic lines carrying the wild type P19 and its mutant P19W39-42R under Dex inducible promoter [59] were generated using the Agrobacterium tumefaciens leaf disc transformation method [82] . The disarmed pTA7001-Dex-P19 , pTA7001-Dex-P19W39-42R were used for transformation . The transgenic plants generated were named Dex::P19 and Dex::P19W39-42R . A . tumefaciens strains containing the constructs P19 [35] , P25 [68] , P15 [83] , HcPro [67] , P14 and P21 [45] were grown overnight at 28°C in Lauria Bertani ( LB ) broth supplemented with 50 µg/ml kanamycine , 10 µg/ml rifampicine and 25 µg/ml gentamycine . Bacterial cultures were then pelleted at 4 500× g for 15 min and the supernatant was discarded . Pellets were resuspended in 10 mM MgCl2 supplemented with 200 µM acetosyringone and brought to an OD 0 . 5 . These bacterial suspensions were infiltrated in the plant leaves using a syringe . Co-agroinfiltration of mGFP and VSRs were done at 0 . 5 OD . For transgenic plants , a solution of 25 µg/ml Dexametasone supplemented with 0 . 1% v/v Silwet L-77 was sprayed onto leaves of 5 week-old transgenic plants . Samples were harvested at 0 , 12 , 24 and 48 hours after DEX application , immediately frozen in liquid nitrogen , and kept at −80°C before extraction . Total proteins were extracted from 100 to 200 mg of homogeny of frozen leave 200 µl of extraction buffer [25 mM Tris-HCl pH 7 . 5 , 1 mM EDTA , 150 mM NaCl , 10% glycérol , 5 mM dithiothreitol ( DTT ) ] and a protease inhibitor cocktail ( Sigma ) . The crude extract was centrifuged at 12000 g for 15 min . The supernatant was kept and total proteins were quantified by Bradford assay ( Bio-Rad Laboratories , Ontario ) . Samples were diluted in Leammli buffer and boiled for 5 minutes before separation on 12% SDS-PAGE . 50 µg of proteins of each sample was used for Western analysis . Proteins were subjected to gel blot analysis using a rabbit polyclonal PR1 , PR2 or PR3 antibodies , at a dilution of 1 : 8000 [84] . For detection of the GFP , a rabbit polyclonal IgG antibody was used at 1/3000 ( GFP ( FL ) , sc-8334 , Santa Cruz Biotechnology ) . For detection of P19 , we used an affinity purified rabbit polyclonal IgG antibody obtain from GeneScript and raised against a synthetic peptide of the P19 protein ( GNDAREQANSERWDC ) . It was used at 1/300 . Coomassie Blue or red ponceau staining were used to confirm equal protein loading . Horse Radish Peroxidase-conjugated anti-rabbit IgG was used as secondary antibody at 1 : 14500 ( Sigma Aldrich ) . Immunodetection was conducted with chemiluminescent substrate ( Bio-Rad , immun-star kit ) followed by X-ray film exposure . Total RNA was extracted from tobacco tissues using the RNeasy Plant Mini Kit ( Qiagen Science , Maryland , USA ) according to the manufacturer's instructions . 2 µg of each RNA samples were reverse transcribed into cDNA using SuperScript II reverse transcriptase ( Invitrogen ) . Samples were diluted 1/5 in DEPC water and qPCR was performed using POWER SYBR Green ( Applied Biosystems , Warrington , UK ) according to the manufacturer's instruction . Primers used were: qPCR NTAC1F 5′-CTGTACTACTCACTGAAGCACCTC-3 , qPCR NTAC1R 5′- GGCGACATATCATAGCAGGA -3 , qPCR P19F 5′-TTGGTTTCAAGGAAAGCTG-3 , qPCR P19R 5′-GATCCAAGGACTCTGTGCA-3 , qPCR1 . A . tumefaciens strains containing the constructs 35S::TBSV-GFP or 35S::TBSVΔP19-GFP ( Kindly provided by Herman B . Scholthof [26] ) were grown overnight at 28°C in Lauria Bertani ( LB ) broth supplemented with 50 µg/ml kanamycine , 10 µg/ml rifampicine and 25 µg/ml gentamincin . Bacterial cultures were then pelleted at 4 500× g for 15 min and the supernatant was discarded . Pellets were resuspended in 10 mM MgCl2 brought to 0 . 5 OD and supplemented with 200 µM acetosyringone . Bacterial suspensions were then incubated at room temperature for 1–3 hours before being infiltrated into young leaves of 5 week-old Nicotiana tabacum plants , using a syringe . Inoculated plants were grown under conditions of 8 h darkness at 18°C , 16 h light at 20°C with 70% relative humidity . Viral infection was monitored over time under U . V . illumination and samples were collected at 6 dpi , frozen in liquid nitrogen and kept at −80°C before extraction of protein or RNA . A . tumefaciens strain containing the construct 35S::PVX-GFP [85] was used for PVX assays . Infections were conducted as described above , except that the final OD used was 0 . 25 . VSR were co-agroinfiltrated at final OD of 0 . 25 . Total RNA was extracted using TRI reagent ( Sigma ) , precipitated with isopropanol and the RNA pellet was resuspended in 50% deionized Formamide . Analysis was performed as described [66] . The signal was detected using X-ray films . Sample were boiled for 5 minutes in the staining solution [10 ml of lactic acid , 10 g of phenol , 10 ml of glycerol , 10 ml of water , 10 mg of trypan blue , mixed 1∶1 with ethanol] . Samples were then destained using chloral hydrate as previously described [86] , [87] . The cassettes for transient expression of GFFG dsRNA and silencing suppressors have been described previously [66] , [67] . Agrobacterium-mediated transient expression in N . benthamiana leaves was as described previously [88] . For immunoprecipitation experiments , 400 mg of frozen tissue harvested 5 days post-infiltration was ground in liquid nitrogen and homogenized in 3 ml/g of extraction buffer ( 50 mM Tris–HCl , pH 7 . 5 , 150 mM NaCl , 10% glycerol , 0 . 1% NP-40 and complete protease inhibitor cocktail ( Roche ) for 30 min at 4°C . Cell debris was removed by centrifugation at 12000 g at 4°C for 30 min . Extracts were pre-cleared by incubation with Protein A-agarose ( Roche ) at 4°C for 1 h . Pre-cleared extracts were then incubated with anti-HA polyclonal antibody ( Sigma ) and protein A-agarose overnight at 4°C . Immunoprecipitates were washed three times ( 15 min each ) in extraction buffer . Aliquots of the inputs and immunoprecipitates were collected for protein blot analysis . For RNA analysis , immune complex were subjected to Tri-Reagent extraction ( Sigma ) . An amount of one to one v/w of cold 80% MeOH was added to finely ground plant tissue ( 300–500 mg ) for extraction of phenolic compounds . Samples were vortexed then shaken overnight at 4°C . The following morning , the samples were vortexed and centrifuged 16 , 000 g for 10 min . The supernatant was transferred to a new Eppendorf tube , filtered through a 0 . 22-µm syringe filter and 50 to 100 µl injected into HPLC . Samples were injected using Waters 2695 separation module ( Waters Corp . ) and a Lichrospher RP-18 ( 5 µm ) column ( 4 mm×250 mm ) at 30°C , and compounds detected with a Waters 996 diode array scanning 200 nm–400 nm , followed , in tandem , by a Waters 2475 Fluorescence detector , with an excitation wavelength of 290 nm emission and a scan of 300–500 nm . The maximum expected emission for free salicylic acid using this excitation wavelength was at 390–400 nm . The HPLC system was controlled and data analysed with the Empower2 software . Standard free salicylic acid ( Sigma 84210 ) standards were prepared at 100 ng/ml , 250 ng/ml , 500 ng/ml , 1000 ng/ml and injected under the same conditions . The solvents were acidified water ( solvent A: 0 . 1% Phosphoric acid in nanopure water ) and acetonitrile HPLC grade ( solvent B ) with an elution flow rate of 1 mL/min . The gradient used was as follows: time ( min ) /%A/%B: 0/100/0 , 5/95/5 , 10/95/5 , 14/90/10 , 20/80/20 , 23/80/20 , 30/65/35 , 35/65/35 , 43/50/50 , 48/25/75 , 55/0/100 and 60/0/100 . The injected volume was 50 µL for each sample . Three biological replicates for each treatment/time point were extracted and injected independently into the HPLC . Linear regressions were generated between compound concentration ( independent variable ) and peak areas ( dependent variable ) . The equations obtained were used to calculate the concentration of each phenolic compound in the analyzed samples . Every sample was also spiked with 0 . 8 µg/ml free salicylic acid and injected independently to confirm the quantities determined by the software .
|
Multiple and complex layers of defense help plants to combat pathogens . A first line of defense relies on the detection , via dedicated host-encoded receptors , of signature molecules ( so called pathogen-associated molecular patterns , PAMPs ) produced by pathogens . In turn , this PAMP-triggered immunity ( PTI ) may be itself antagonized by adapted pathogens that have evolved virulence effectors to target key PTI components . Host plants react to PTI suppression by producing disease resistance ( R ) proteins that recognize virulence effectors and activate highly specific resistance called Effector Triggered Immunity ( ETI ) . It has been noted that RNA silencing , a sequence-specific antiviral defense response based on the production of virus-derived 21–24 nt small RNAs on the one hand , and its suppression by virulence effectors , called viral suppressors of RNA silencing ( VSRs ) on the other , are conceptually similar to PTI . Here we provide strong support to this hypothesis by showing that extreme resistance is indeed activated following detection , in specific host species , of the VSR activity of a viral virulence effector . The ensuing antiviral immunity displays many characteristics of ETI , suggesting that one or several R proteins must sense the integrity of the host silencing machinery .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"plant",
"pathogens",
"plant",
"pathology",
"plant",
"science",
"biology"
] |
2013
|
Extreme Resistance as a Host Counter-counter Defense against Viral Suppression of RNA Silencing
|
Hereditary footpad hyperkeratosis ( HFH ) represents a palmoplantar hyperkeratosis , which is inherited as a monogenic autosomal recessive trait in several dog breeds , such as e . g . Kromfohrländer and Irish Terriers . We performed genome-wide association studies ( GWAS ) in both breeds . In Kromfohrländer we obtained a single strong association signal on chromosome 5 ( praw = 1 . 0×10−13 ) using 13 HFH cases and 29 controls . The association signal replicated in an independent cohort of Irish Terriers with 10 cases and 21 controls ( praw = 6 . 9×10−10 ) . The analysis of shared haplotypes among the combined Kromfohrländer and Irish Terrier cases defined a critical interval of 611 kb with 13 predicted genes . We re-sequenced the genome of one affected Kromfohrländer at 23 . 5× coverage . The comparison of the sequence data with 46 genomes of non-affected dogs from other breeds revealed a single private non-synonymous variant in the critical interval with respect to the reference genome assembly . The variant is a missense variant ( c . 155G>C ) in the FAM83G gene encoding a protein with largely unknown function . It is predicted to change an evolutionary conserved arginine into a proline residue ( p . R52P ) . We genotyped this variant in a larger cohort of dogs and found perfect association with the HFH phenotype . We further studied the clinical and histopathological alterations in the epidermis in vivo . Affected dogs show a moderate to severe orthokeratotic hyperplasia of the palmoplantar epidermis . Thus , our data provide the first evidence that FAM83G has an essential role for maintaining the integrity of the palmoplantar epidermis .
The skin and most notably its outermost layer , the epidermis , forms an essential barrier against the environment . The soles of the feet and the palms of the hands are covered by the specially structured palmoplantar epidermis , which has to bear the strongest mechanical forces of the entire skin . Epidermolytic palmoplantar keratoderma ( EPPK ) is an inherited disorder characterized by abnormal thickening of the palmoplantar epidermis . It is typically caused by dominant variants in the KRT9 gene encoding keratin 9 , a type I intermediate filament specifically expressed in the suprabasal layer of the palmoplantar epidermis [1] , [2] . Most human EPPK patients are heterozygous for dominant KRT9 variants [1] . However , homozygous Krt9 deficient mice show a very similar phenotype [2] . Related human genodermatoses which may involve the palmoplantar epidermis , but are not exclusively restricted to palms and soles are caused by variants in KRT1 [3] , KRT10 [4] , KRT16 [5]–[7] , and AQP5 encoding the water channel aquaporin 5 [8] . Many genetic defects in the keratin genes themselves have been characterized in keratinizing disorders and provided first insights into the function of specific keratins in the various epithelia . However , much less is known about other molecules that interact with the keratins and are potentially involved in posttranslational modifications of keratins , or other regulatory mechanisms ensuring the mechanical stability of the epidermis [9] , [10] . Spontaneous animal mutants with genodermatoses or other heritable phenotypes of the skin provide an opportunity to identify further components of the complex molecular machinery required to maintain skin function . Due to their special population structure purebred dogs are particularly well suited for genetic analyses [11] . Successful examples for the utilization of dog genetics in skin research include the identification of genes involved in the determination of hair characteristics [12] , ectodermal development [13] , one form of ichthyosis [14] , congenital keratoconjunctivitis sicca and ichthyosiform dermatosis [15] , the excessive skin folding in Chinese Shar Pei dogs [16] , and hereditary nasal parakeratosis [17] . Hereditary footpad hyperkeratosis ( HFH , also known as digital hyperkeratosis ( DH ) or “corny feet” ) is a specific form of an orthokeratotic palmoplantar hyperkeratosis , which has been originally described in Irish Terriers [18] . HFH has also been observed in other related dog breeds , such as e . g . the Kromfohrländer , a young German dog breed founded in 1945 . HFH initially leads to thickened and hardened footpads , which can be recognized in juvenile dogs starting at an age of 18 to 24 weeks . The inelastic pad surface subsequently develops cracks and fissures , which predispose affected dogs to secondary infections . If not properly managed , HFH can thus lead to considerable pain and lameness in affected dogs . HFH is inherited as a monogenic autosomal recessive trait [18] . A previous candidate gene study was not successful in identifying the causative gene [19] . In this study we used genome-wide association studies ( GWAS ) in independent Kromfohrländer and Irish Terrier cohorts and whole genome re-sequencing ( WGS ) to identify the causative genetic lesion for HFH in both breeds .
We collected samples from 13 HFH affected Kromfohrländer and 29 controls and genotyped them with the 173k SNP chip . After removing 95 , 759 markers , which had low call rates ( <90% ) , were non-informative ( MAF <0 . 05 ) , or showed a strong deviation from Hardy-Weinberg equilibrium in the controls ( p<10−5 ) , we retained 77 , 903 markers for the final genome-wide allelic association study . Three best-associated SNPs in the GWAS had identical raw p-values of 1 . 0×10−13 ( Figure 1A ) . The corrected p-value after 100 , 000 permutations was <10−5 . The 159 best-associated SNPs with raw p-values of less than 1×10−4 were all located on chromosome 5 . The genomic inflation factor in this analysis was 1 . 40 . We also performed a GWAS in an independent cohort of Irish Terriers to replicate our findings . For the replication we had 10 cases , 21 controls , and 82 , 671 markers . In the Irish Terrier cohort HFH was also strongly associated with the same region on chromosome 5 with a raw p-value of 6 . 9×10−10 ( Figure 1B ) . The genomic inflation factor in the Irish Terrier analysis was 1 . 32 . As both cohorts showed considerable population stratification we repeated the analyses with a mixed model that corrects for this confounding effect . The same markers as in the initial analyses showed the strongest associations . Subsequently , we applied a homozygosity mapping approach to fine-map the region containing the HFH mutation . We hypothesized that the affected dogs most likely were inbred to one single founder animal . In this scenario , the affected individuals were expected to be identical by descent ( IBD ) for the causative mutation and flanking chromosomal segments . We analyzed the 23 combined cases for extended regions of homozygosity with simultaneous allele sharing . In the associated interval on CFA 5 , all 23 affected dogs were homozygous and shared identical alleles over 36 consecutive SNP markers . We concluded that the causative mutation should be located in the 611 kb critical interval between the closest heterozygous markers on either side of the homozygous segment ( CFA5: 40 , 521 , 040–41 , 131 , 739 CanFam 3 . 1 assembly; Figure 1C ) . A total of 13 genes and loci are annotated in the critical interval on CFA 5 ( Figure 1D ) . In order to obtain a comprehensive overview of all variants in the critical interval we sequenced the whole genome of one affected Kromfohrländer . We collected nearly 293 million paired-end reads from a shotgun fragment library corresponding to 23 . 5× coverage of the genome . We called SNPs and indel variants with respect to the reference genome of a presumably non-affected Boxer . Across the entire genome , we detected ∼6 . 8 million variants of which ∼3 . 5 million were homozygous ( Table 1 ) . Within the critical interval there were 1 , 314 variants of which 16 were predicted to be non-synonymous . We further compared the genotypes of the affected Kromfohrländer with 46 dog genomes of various breeds that had been sequenced in the course of other ongoing studies ( Table S1 ) . We hypothesized that the mutant allele at the causative variant should be completely absent from all other dog breeds in our sample set . After this filtering step only two private variants remained in the critical interval and only one of them was predicted to be non-synonymous , FAM83G:c . 155G>C or Chr5:41 , 055 , 619G>C . We confirmed this variant by Sanger sequencing ( Figure 2 ) and genotyped it in 43 Kromfohrländer , 194 Irish Terriers , and 288 dogs of other breeds . It was perfectly associated with the HFH phenotype ( Table 2 , Table S2 ) . The FAM83G:c . 155G>C variant represents a missense mutation in the FAM83G gene , encoding the family with sequence similarity 83 , member G . The variant changes an arginine codon to a proline codon ( p . R52P ) . SIFT , Polyphen-2 , and PMUT predict that this non-conservative amino acid exchange affects protein function [20]–[22] . The arginine at position 52 is perfectly conserved across all known eutherian FAM83G orthologs ( Figure 3 ) . We confirmed by immunofluorescence that FAM83G is strongly expressed in footpad epidermis , but not the underlying dermis ( Figure S1 ) . Hyperkeratosis of the foot pads is noticed by the owners of both breeds at 4–5 months of age and involves all footpads . With time horny protrusions appear on the rims of the footpads and the pad surface becomes hard and develops cracks ( Figure 4A ) . Affected animals avoid walking on irregular surfaces and may go lame . The nails of affected dogs are very hard and seem to grow faster . We noticed a duller , less wiry , softer coat on an affected Kromfohrländer ( Figure 4C ) . Similar clinical symptoms were noted on 5 HFH affected Irish Terriers . We also performed histopathological examinations of palmoplantar and normal epidermis . We did not observe any obvious changes in the normal epidermis from an HFH affected Kromfohrländer ( data not shown ) . A paw pad biopsy from an affected Kromfohrländer revealed a moderate to severe palmoplantar epidermal hyperplasia associated with papillated epidermal protrusions to the outside . The differentiation of the dermal keratinocytes was morphologically normal . The palmoplantar epidermis was covered by abundant compact orthokeratotic keratin ( Figure 5 ) .
In this study we identified a missense variant of FAM83G as candidate causative genetic defect for HFH in two related dog breeds . We cannot formally rule out the possibility that another , potentially non-coding regulatory variant , in absolute linkage disequilibrium ( LD ) with the FAM83G:c . 155G>C variant is the actual causative variant . However , in our genome re-sequencing data , there is only one other variant in complete LD with the FAM83G variant . This variant is an intergenic SNP more than 15 kb away from the next annotated transcript and thus unlikely to be functionally important . It also has to be considered that our variant detection relied on short read mapping to an imperfect reference genome . Thus , we will have missed variants , which are located in genome segments that are not contained in the reference genome , such as gap regions . We may also have missed non-synonymous variants in genes that are not or not correctly annotated in the dog reference genome . While acknowledging the limitations of the currently available technologies our genome re-sequencing data taken together with the precise genetic mapping and the strictly recessive mode of inheritance of HFH , which suggests a complete loss-of-function allele , very strongly support the causality of FAM83G:c . 155G>C . FAM83G is hardly characterized so far . It has recently been shown that a partial deletion of the Fam83g gene causes the phenotype of the wooly ( wly ) mouse mutant [23] . Wooly mice macroscopically display a rough or matted appearance of their coat . Similar to our findings in dogs and in spite of this clearly visible macroscopic phenotype , microscopic examination did not reveal any consistent anomaly in any of the four murine hair types nor any consistent changes in skin histology [23] , [24] . According to our knowledge the foot pads of wooly mice have not been studied in detail . The FAM83 protein family consists of 8 known members FAM83A – FAM83H . Apart from the single report on murine Fam83g , a physiological in vivo function has so far only been discussed for FAM83H [15] , [23] , [25] . Heterozygous nonsense variants in the last exon of the FAM83H gene have been reported in human patients with autosomal dominant hypocalcified ameliogenesis imperfecta , a disorder of enamel formation during tooth development [25] . However , a FAM83H frameshift variant in the last exon was shown to cause the autosomal recessive congenital keratoconjunctivitis sicca and ichthyosiform dermatosis ( CKCSID ) , also called “dry eye curly coat syndrome” , in the Cavalier King Charles Spaniel dog breed [15] . The apparent discrepancy in the reported phenotypes of human and dog patients with FAM83H variants might be due to the specific nature of the involved variants and calls for further investigations . In this context , it is interesting to note that the FAM83H mutant Cavalier King Charles Spaniels have a defect that shares several phenotypic features with the FAM83G mutant Kromfohrländer and Irish Terriers , such as an altered coat texture , altered nails and palmoplantar hyperkeratosis . The FAM83 members share a conserved protein domain of about 300 amino acids at their N-terminus ( Pfam DUF1669 ) , which shows homology to the phospholipase D catalytic domain . However , as critical catalytic histidine residues are lacking , it is unlikely that this domain actually has a phospholipase activity in the members of the FAM83 family [23] . FAM83G is highly conserved in eutherians ( placental mammals ) . In more distantly related vertebrate species the predicted FAM83G orthologs show a drastically reduced overall homology of the amino acid sequence , which might indicate that FAM83G acquired a new function during eutherian or possibly mammalian evolution . It is tempting to speculate that this new function is related to the evolution of hair and a specialized palmoplantar epidermis in mammals . In conclusion , we have identified a missense variant in FAM83G as most likely causative for HFH in dogs . This provides a first indication of a physiological function of this particular gene in maintaining the integrity of the palmoplantar epidermis . Together with previous data from mice our data also confirm that this gene has an additional role in hair morphology .
All animal experiments were performed according to the local regulations . The dogs in this study were examined with the consent of their owners . The study was approved by the “Cantonal Committee For Animal Experiments” ( Canton of Bern; permit 23/10 ) . We used 13 HFH cases and 30 controls from the Kromfohrländer breed . The phenotype information was extracted from a database that is maintained by the breeding club and based on reports from dogs' owners and evaluations by the breeding committees of the club . One of the 13 Kromfohrländer cases was additionally examined by a board certified veterinary dermatologist ( PR; Figure 4 ) and the clinical diagnosis was confirmed by the histhopathological analysis of a biopsy from the footpad , which was evaluated by a board certified veterinary pathologist ( MMW; Figure 5 ) . In the Irish Terrier breed we initially started our analysis with 26 reported cases and 171 controls . In the Irish Terriers we also primarily relied on phenotypes as reported by the owners . We initially selected 13 owner-reported cases and 21 owner-reported controls for the GWAS and homozygosity mapping . During this analysis we realized that 3 of the owner-reported cases did not carry the disease-associated haplotype . This prompted us to carefully re-evaluate the phenotypes of all Irish Terrier cases . It then turned out that the 3 suspicious dogs had only lesions on one to three feet , whereas all other Irish Terrier cases had lesions on the footpads of all four feet . We therefore used a refined phenotype classification , which required reported lesions on all four feet for HFH cases . Based on this new phenotype classification we assumed the 3 Irish Terriers that did not have lesions on all four feet to represent phenocopies and excluded them from all further analyses . We thus ended up with 23 Irish Terrier HFH cases and 171 controls for the final analysis . We also used additional DNA samples from other breeds that were collected for various research projects at the Institute of Genetics of the University of Bern . For these other samples a non-affected phenotype was assumed as HFH is supposedly confined to the Kromfohrländer and Irish Terrier breeds . We isolated genomic DNA from EDTA blood samples with the Nucleon Bacc2 kit ( GE Healthcare ) and from cheek swabs with the NucleoSpin 96 Tissue DNA Kit ( Macherey-Nagel ) . Genotyping was done on illumina canine_HD chips by GeneSeek/Neogen ( Kromfohrländer , 173 , 662 SNPs called ) or the Centre National de Génotypage , Evry , France ( Irish Terriers , 174 , 376 SNPs called ) . Genotypes were stored in a BC/Gene database version 3 . 5 ( BC/Platforms ) . We used PLINK v1 . 07 [26] to perform genome-wide association analyses ( GWAS ) . In the Kromfohrländer analysis all 13 cases and 29 controls had call rates >90% . We removed 3 , 303 markers with call rates <90% from the analysis . We also removed 94 , 714 markers with minor allele frequency ( MAF ) <5% and 30 markers strongly deviating from Hardy-Weinberg equilibrium in the controls ( p<10−5 ) . The final Kromfohrländer dataset consisted of 42 dogs and 77 , 903 SNPs . In the Irish Terrier cohort with 10 cases and 21 controls , all dogs had call rates >90% . We removed 5 , 723 markers with call rates <90% from the analysis . We also removed 89 , 179 markers with MAF <10% and 366 markers strongly deviating from Hardy-Weinberg equilibrium in the controls ( p<10−5 ) . The final Irish Terrier dataset consisted of 31 dogs and 82 , 671 SNPs . We performed an allelic association study and determined an empirical significance threshold by performing 100 , 000 permutations of each dataset with arbitrarily assigned phenotypes . As both datasets showed considerable population stratification , we also analyzed the data using GenABEL and a mixed model approach [27] . This procedure corrects for cryptic relatedness by using the genomic kinship estimated from the marker data as covariable in the model . In both cohorts the same markers showed the highest association regardless whether the simple PLINK analysis or the mixed model GenABEL analysis was performed . With the correction for population stratification the genomic inflation factors were reduced from 1 . 40 to 1 . 07 in Kromfohrländer and from 1 . 32 to 1 . 02 in Irish Terriers . The corrected p-values ( Pc1df ) for the best associated markers were then 4 . 7×10−8 in Kromfohrländer and 4 . 2×10−6 in Irish Terriers . We also used PLINK to search for extended intervals of homozygosity with shared alleles . The final critical interval was defined by visual inspection of all SNP chip genotypes on chromosome 5 for the 13 Kromfohrländer and 10 Irish Terrier cases in an Excel-file . We used the dog CanFam 3 . 1 assembly for all analyses . All numbering within the canine FAM83G gene corresponds to the accessions XM_003434636 . 2 ( mRNA ) and XP_003434684 . 1 ( protein ) . We analyzed the functional effects of variants in silico with SIFT , Polyphen-2 und PMUT [20]–[22] . We prepared a fragment library with 300 bp insert size and collected 293 , 647 , 193 illumina HiSeq2500 paired-end reads ( 2×100 bp ) or roughly 23 . 5× coverage . We mapped the reads to the dog reference genome using the Burrows-Wheeler Aligner ( BWA ) version 0 . 5 . 9-r16 [28] with default settings and obtained 551 , 317 , 870 uniquely mapping reads . After sorting the mapped reads by the coordinates of the sequence and merging the 2 lanes of data with Picard tools , we labeled the PCR duplicates also with Picard tools ( http://sourceforge . net/projects/picard/ ) . We used the Genome Analysis Tool Kit ( GATK version v2 . 3-6 , [29] ) to perform local realignment and to produce a cleaned BAM file . Variant calls were then made with the unified genotyper module of GATK . Variant data for each sample were obtained in variant call format ( version 4 . 0 ) as raw calls for all samples and sites flagged using the variant filtration module of GATK . Variant calls that failed to pass the following filters were labeled accordingly in the call set: ( i ) Hard to Validate MQ0 ≥4 & ( ( MQ0/ ( 1 . 0 * DP ) ) >0 . 1 ) ; ( ii ) strand bias ( low Quality scores ) QUAL <30 . 0 || ( Quality by depth ) QD <5 . 0 || ( homopolymer runs ) HRun >5 || ( strand bias ) SB >0 . 00; ( iii ) SNP cluster window size 10 . The snpEFF software [30] together with the CanFam 3 . 1 annotation was used to predict the functional effects of detected variants . We considered the following snpEFF categories of variants as non-synonymous: NON_SYNONYMOUS_CODING , CODON_DELETION , CODON_INSERTION , CODON_CHANGE_PLUS_CODON_DELETION , CODON_CHANGE_PLUS_CODON_INSERTION , FRAME_SHIFT , EXON_DELETED , START_GAINED , START_LOST , STOP_GAINED , STOP_LOST , SPLICE_SITE_ACCEPTOR , SPLICE_SITE_DONOR . The critical interval contained 610 , 700 bp and 15 , 160 coding nucleotides , respectively . In our re-sequencing data , we had ≥4× coverage on 566 , 516 bp of the critical interval ( 93% ) and on all 15 , 160 coding bases . Additionally , we searched for structural variations ( deletions , insertions , inversions ) within the critical interval using the software SVDetect [31] . SVDetect calls intrachromosomal and interchromosomal rearrangements from discordant , quality pre-filtered read pairs . As per the authors' suggestion SVDetect software was set to detect rearrangements with 3 or more supporting read pairs using 2 times standard deviation of the insert size as threshold for both deletions and duplications . This analysis identified 15 rearrangements between 100 and 450 bp in size in the critical interval . Most of these variants were within repeats and none of them affected an exon of the annotated genes in the critical interval . The sequence data of the affected Kromfohrländer were deposited in the short read archive of the European Nucleotide Archive ( ENA ) under accession PRJEB6076 . We used Sanger sequencing to confirm the illumina sequencing results and to perform targeted genotyping for selected variants . For these experiments we amplified PCR products using AmpliTaqGold360Mastermix ( Applied Biosystems ) . PCR products were directly sequenced on an ABI 3730 capillary sequencer ( Applied Biosystems ) after treatment with exonuclease I and shrimp alkaline phosphatase . We analyzed the Sanger sequence data with Sequencher 5 . 1 ( GeneCodes ) .
|
The palms and soles of mammals are covered by the palmoplantar epidermis , which has to bear immense mechanical forces and has therefore a special composition in comparison to the epidermis on regular skin . We studied a Mendelian disease in dogs , termed hereditary footpad hyperkeratosis ( HFH ) . HFH affected dogs develop deep fissures in the paw pads , which are the consequence of a pathological thickening of the outermost layer of the epidermis . We mapped the disease causing genetic variant in the Kromfohrländer and Irish Terrier breeds to a 611 kb interval on chromosome 5 . HFH affected Kromfohrländer and Irish Terriers shared the same haplotype indicating descent from a common founder . We re-sequenced the genome of an affected dog and compared it to genome sequences of 46 control dogs . The HFH affected dog had only one private non-synonymous variant in the critical interval , a missense variant of the FAM83G gene . We genotyped this variant in more than 500 dogs and found perfect association with the HFH phenotype . Our data very strongly suggest that the FAM83G variant is causative for HFH . FAM83G is a protein with unknown biochemical function . Our study thus provides the first link between this protein and the palmoplantar epidermis .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"sequencing",
"techniques",
"genome-wide",
"association",
"studies",
"dermatology",
"medicine",
"and",
"health",
"sciences",
"animal",
"welfare",
"animal",
"genetics",
"animal",
"breeding",
"genome",
"sequencing",
"genome",
"analysis",
"molecular",
"biology",
"techniques",
"animal",
"management",
"dermatologic",
"pathology",
"veterinary",
"science",
"veterinary",
"medicine",
"hair",
"and",
"nail",
"diseases",
"molecular",
"biology",
"agriculture",
"genetics",
"biology",
"and",
"life",
"sciences",
"genomics",
"genetics",
"of",
"disease",
"computational",
"biology"
] |
2014
|
A Mutation in the FAM83G Gene in Dogs with Hereditary Footpad Hyperkeratosis (HFH)
|
While many transcriptional regulators of pluripotent and terminally differentiated states have been identified , regulation of intermediate progenitor states is less well understood . Previous high throughput cellular resolution expression studies identified dozens of transcription factors with lineage-specific expression patterns in C . elegans embryos that could regulate progenitor identity . In this study we identified a broad embryonic role for the C . elegans OTX transcription factor ceh-36 , which was previously shown to be required for the terminal specification of four neurons . ceh-36 is expressed in progenitors of over 30% of embryonic cells , yet is not required for embryonic viability . Quantitative phenotyping by computational analysis of time-lapse movies of ceh-36 mutant embryos identified cell cycle or cell migration defects in over 100 of these cells , but most defects were low-penetrance , suggesting redundancy . Expression of ceh-36 partially overlaps with that of the PITX transcription factor unc-30 . unc-30 single mutants are viable but loss of both ceh-36 and unc-30 causes 100% lethality , and double mutants have significantly higher frequencies of cellular developmental defects in the cells where their expression normally overlaps . These factors are also required for robust expression of the downstream developmental regulator mls-2/HMX . This work provides the first example of genetic redundancy between the related yet evolutionarily distant OTX and PITX families of bicoid class homeodomain factors and demonstrates the power of quantitative developmental phenotyping in C . elegans to identify developmental regulators acting in progenitor cells .
Identifying regulators of the intermediate steps that link pluripotency and terminal differentiation is a fundamental challenge in developmental biology . These regulators are comparatively poorly understood for most tissues due to the difficulty of recognizing and isolating cells in these transient intermediate states ( “progenitors” ) and their complex combinatorial logic . Individual transcription factors ( TFs ) acting at these stages often have broad and diverse expression domains that don’t correlate well with specific tissue or cell types [1] , with multiple TFs typically acting together to specify any given intermediate progenitor . Therefore , loss of function can lead to pleiotropic phenotypes , while partial redundancy between regulators can lead to reduced penetrance , making it hard to determine the relationship between expression and biological function . Large-scale screens for gene pairs with synthetic phenotypes , as has been done for yeast [2] can identify genes acting in parallel , but screening at that scale is not feasible in animals . We are overcoming these challenges with a systematic approach to define pleiotropic and redundant progenitor TFs in Caenorhabditis elegans , a simple model organism where lineage relationships are already understood , large-scale gene expression resources allow rapid identify patterns of TF overlap , and powerful tools exist for characterizing mutant phenotypes across all embryonic cells . Previous studies of genetic redundancy in C . elegans have prioritized gene pairs for synthetic lethality testing based on similar functional interactions [3 , 4] , expression patterns [5] and homology or conservation [6 , 7] . Progenitor cells are easily identified in C . elegans because the relationship between cell lineage and fate is known and invariant[8 , 9] . The first several embryonic divisions give rise to founder cells , some of which have clonal or partially clonal cell fates . Most cells , however , retain a multipotent state until the final round of embryonic cell divisions , when two daughters adopt such different fates as a neuron and an epithelial tube or neuron and hypodermal ( skin ) cell . Thus , any TF expressed in a non-clonal progenitor cell or group of lineally related cells ( i . e . lineage ) at any time after the earliest cell divisions but prior to the final round could play a role in progenitor identity . Despite this potential , genetic studies have identified numerous regulators of both early founder cell identity [10–16] and of terminal fate[17–19] , but fewer regulators of intermediate progenitor identity . Automated methods to track cell lineages from confocal microscopy image series have allowed quantitative expression measurements for over 200 transcription factors across every cell of C . elegans embryos [1 , 20–22] , and this EPIC ( Expression Patterns In Caenorhabditis ) dataset suggests many candidate regulators of progenitor identity [1 , 23] . Computer-aided cell tracking of mutant embryos can confirm these regulators by identifying a wide range of pleiotropic defects , from wholesale fate transformations to subtle defects in cell migration or division timing [10 , 14 , 24–27] . Many previous studies of TF function relied on reporter gene expression to infer developmental defects . We reasoned that the complex patterns of cell cycle length asynchrony and cell migration that occur in later embryos may allow identification of defects at single cell resolution without such reporters . We used this approach to characterize the developmental role of the candidate progenitor regulator ceh-36 , which encodes an orthodenticle/OTX homeodomain family transcription factor orthologous to mammalian OTX1 , OTX2 and CRX proteins . A ceh-36 reporter is expressed in multiple progenitor cells , encompassing the precursors of 248 terminal cells with diverse fates including neurons , glia , the excretory ( renal ) system , visceral and body muscles , epidermal and rectal epithelial cells[1] . Vertebrate OTX factors are similarly expressed and required in precursors of diverse tissues [28–37] , suggesting these factors could be conserved regulators of progenitor identity . However , previous studies of ceh-36 mutants identified defects only in the embryonic specification of four neurons [38–40] . The large number of expressing cells combined with the small number of cells known to require ceh-36 raises the question of whether ceh-36 is required across most expressing cells or only a minority of these cells . We found that ceh-36 null mutants are viable embryonically , with partially penetrant larval lethality and superficially normal morphology . Cell lineage tracing of ceh-36 ( - ) embryos revealed variably penetrant defects in cell division patterns or cell migration in over 100 cells that normally express ceh-36 . Double mutants lacking both ceh-36 and the coexpressed PITX-family homeobox gene unc-30 exhibited 100% synthetic lethality and severe morphological defects . These double mutants have dramatically increased rates of defective cell division and migration in coexpressing cells , indicating ceh-36 and unc-30 act in parallel to regulate the development of these cells . This provides the first evidence for genetic redundancy between OTX and PITX homeodomain factors , two bicoid class TFs that are predicted to bind similar sequences , yet diverged prior to the radiation of metazoan species .
A ceh-36 deletion allele that removes the majority of coding regions , including the homeodomain , was annotated as embryonic lethal in WormBase based on limited previous characterization [40 , 41] . After outcrossing , we found that nearly all embryos homozygous for this allele hatched , while ~60% of animals arrest as larvae ( Table 1 , Fig . 1A , B , S1 Table ) . The remainder of ceh-36 ( ok795 ) animals survived to adulthood and were fertile . Most arrested larvae had normal body morphology , with 5 . 6% of L1s containing a small bubble-like “vacuole” at the tip of the head ( Table 2 , Fig . 1C ) . Two other ceh-36 alleles predicted to eliminate or alter the homeodomain displayed similar rates of larval arrest ( Table 1 ) , suggesting this is the null phenotype . A fourth allele , ky640 , which truncates the protein but is predicted to encode a complete homeodomain , displayed lower lethality rates , suggesting it leads to partial loss of function . An extrachromosomal genomic fosmid transgene containing CEH-36::GFP ( + ) rescued ceh-36 ( ok795 ) larval lethality; the 85% survival in this strain corresponds to nearly 100% after accounting for the 25% rate of transgene loss ( Table 1 ) . Consistent with this , 95% of CEH-36::GFP-positive L1s survived . Ectopic expression of CEH-36::GFP under the control of a heat-shock promoter caused extensive lethality when induced prior to the 50-cell stage , while later induction had little effect ( Fig . 1D ) , indicating that CEH-36 is toxic when expressed in these early embryonic cells , but not in later cells . We conclude that ceh-36 is required for robust larval viability but not for gross morphology or embryonic viability . We previously analyzed expression of a 5-kb ceh-36 promoter fusion reporter and identified expression in several major lineages ( Fig . 2 , S1 Fig ) [1] . Since this reporter may not contain all relevant regulatory sequences , we generated transgenic strains using a fosmid clone from the “Transgeneome” project [22] where CEH-36 protein is fused to GFP in the context of the endogenous locus ( Fig . 2A ) . This transgene rescues the higher-penetrance ceh-36 mutant lineage defects and larval arrest phenotype described below ( Table 1 ) . Using lineage analysis , we identified all CEH-36::GFP expressing cells through the comma stage , at which point the embryo starts to move . CEH-36::GFP is expressed in progenitors of 248 terminal cells from six lineages that together produce a mix of diverse cell types including pharyngeal cells , muscles , neurons , glia and specialized cell types , and programmed cell deaths ( Fig . 2B , C ) . CEH-36::GFP is predominantly ( >90% ) expressed symmetrically between left and right symmetric lineages , despite left-right asymmetric expression and function for two of the four neurons previously shown to require ceh-36 [38 , 40] . The spatial expression pattern is similar to the previously analyzed ceh-36 promoter fusion ( Fig . 2B , S2 Fig ) , but includes additional expression in the ABara lineage . We also analyzed a previously published 2-kb promoter fusion reporter [38 , 40 , 42] that we found is expressed in the MSa , MSp and ABalpa lineages but not ABara , ABplp or ABprp , indicating the existence of multiple regulatory elements for ceh-36 in different lineages ( S3 Fig ) . The CEH-36 protein fusion reporter exhibits complex dynamics that we confirmed by single molecule RNA-FISH ( smFISH ) [43] of endogenous ceh-36 mRNA ( see below ) . Expression in the ABpxp , MSaa , and MSpa lineages begins between the 50-cell and 100-cell stages and decreases in most cells after 2–3 cell cycles , prior to morphogenesis ( Fig . 2B ) . However , a few cells maintain stable expression much longer , up to at least comma stage . The CEH-36::GFP expressing cells include progenitors of three neurons previously shown to require ceh-36 ( MI , AWCL and AWCR ) , with additional stronger expression in AWCL , AWCR and the fourth ceh-36-requiring cell , ASEL , beginning after the worm begins to elongate and twitch [38–40] . In total , we found expression of ceh-36 in progenitors of over 30% of embryonic cells suggesting it could play a broad role in embryonic patterning . Its early and transient expression in progenitor cells suggested that ceh-36 might be an important regulator of progenitor identity or function . The lack of obvious morphological defects in ceh-36 mutants suggests that ceh-36 might play a minimal role in the development of most expressing cells . To test this , we searched for defects in lineage patterns and cell migrations in mutant embryos using automated cell tracking . We examined quantitative features of embryonic development , including timing and patterns of cell division , division orientation , and positions in eight ceh-36 ( ok795 ) embryos through the comma stage ( ~400 minutes after fertilization , when nearly all cell divisions have occurred ) and compared these phenotypes to a wild-type reference set [27] ( see Materials and Methods ) and to three embryos expressing a rescuing CEH-36::GFP transgene . We also examined one embryo carrying a second predicted ceh-36 null mutation ( ky646 ) . As detailed below , we found that many cells in ceh-36 ( - ) embryos have partially penetrant defects in both cell cycle timing and cell position ( Figs . 3 , 4 , S2 Table ) . In total , 5 . 1% ( 495/9636 ) of cells in ceh-36 ( ok795 ) embryos were defective in cell division or position , compared with 0 . 3% ( 85/26171 ) of cells in wild-type control embryos ( p < 10-220; chi-squared test ) . This suggests that ceh-36 is broadly important for robust development across its expressing cells . The C . elegans lineage is composed of an invariant pattern of cell divisions and deaths . In wild-type embryos , the division timing is highly stereotyped , with most cells having variability in cell cycle length of less than 5% [27] [44] . We identified 49 cells with cell cycle or lineage timing defects in at least one ceh-36 mutant embryo ( Figs . 3A , D , 4 , S2 Table ) , defined as cells dividing both three standard deviations and at least five minutes earlier or later than expected , not dividing at all , or dividing inappropriately . In addition , three cells failed to undergo programmed cell death when expected , as recognized by the characteristic pattern of chromatin compaction observed for histone-mCherry . For example , in three embryos , MSpaapp , which normally is the first embryonic cell to undergo apoptosis , instead survived and divided , with both sisters migrating into the pharynx to adopt unknown fates ( Fig . 3C ) . In some cases , cells not passing our threshold for defect calling appeared to have different mean cell cycles or positions . For example 35 of 49 cells with cell cycle defects in one or more embryos also had a nominally significant difference in mean cell cycles ( p < 0 . 1; FDR < 0 . 15; S2 Table ) . The CEH-36::GFP fusion protein is expressed in precursors of 86% ( 12/14 ) of cells with cell division timing defects in two or more ceh-36 ( - ) embryos , and 60% ( 21/35 ) of cells with defects in one embryo . This is significantly more than the 30% of all cells that express CEH-36::GFP ( chi squared p < 2 × 10-6 ) . CEH-36::GFP is also expressed in all of the cells with supernumerary divisions or failed cell death . Cell positions are also highly consistent between wild-type embryos , allowing us to identify cell migration defects by comparing cell positions between ceh-36 mutant and wild-type embryos . We identified 124 cells whose deviation from expected position was at least 3 . 5 standard deviations greater than in the wild-type set and that had aberrant neighbors as defined by an empirical neighbor-distance score ( S2 Table; see Methods ) . Position defects were strongly enriched in expressing cells; 81% ( 55/68 ) of cells with position defects in two or more embryos normally express CEH-36::GFP . By comparison , in 22 wild-type embryos examined , only 13 cells had defective positions , in one embryo each . A cell could be misplaced because of a defective migration , in which case it would have both different position and different neighbors than in the wild type . Alternatively , a cell could be misplaced because its normal position is occupied by another cell that migrated inappropriately , in which case its position relative to its normal neighbors would be unchanged . We used these criteria to classify 50 cells with position defects by examining their position and neighbors in 3D visualizations ( Fig . 3E , F ) . We scored 82% ( 41/50 ) of cells as likely defective migrations , while 9/50 ( 18% ) defects could be explained by defective migration of other cells ( S3 Table ) . 100% ( 18/18 ) of higher-penetrance ( seen in at least three of eight ok795 embryos ) position defects examined were scored as likely migration defects . The migration defects include both cells that undergo novel migrations in the mutant ( Fig . 3E ) as well as cells that fail to undergo their expected migrations ( Fig . 3F ) . The cells scored as possible secondary defects were less penetrant , with each identified as defective in one or two embryos . Still , most low-penetrance defects ( 23/32 ) were scored as likely migration defects We observed dramatic defects in eight laterally positioned cells that were born in the correct position but subsequently migrated across the midline to the opposite lateral side of the embryo , sometimes displacing the position of their bilateral counterpart ( e . g . Fig . 3E ) . These lateral migration defects occurred on both sides of the embryo ( 3 L→R , 5 R→L ) and include diverse cell types: neurons ( I1R and I2L ) , pharyngeal cells ( pm3R and mc1DR ) , rectal cells ( left intestinal muscle and anal depressor muscle ) , and tail cells ( Hyp10 and tail spike ) . These defects were all low penetrance ( seen in one or two of eight ok795 embryos ) , but we saw no defects of this class in the 22 wild-type control embryos , and all eight of these cells normally express ceh-36 . This indicates that C . elegans cells’ lateral position is not merely a result of their birth position but is regulated by factors that include ceh-36 . We determined that lateralization defects are maintained through embryonic elongation and not corrected by subsequent cell movements by examining worms expressing FKH-4::GFP , a marker of three visceral muscles ( left and right intestinal muscles and anal depressor; Fig . 3E , S4 Fig ) . 100% of both wild-type and ceh-36 mutant elongated ( pretzel-stage ) embryos have three FKH-4 ( + ) cells , indicating that ceh-36 is not necessary for FKH-4 expression . However , one FKH-4 ( + ) cell is laterally mispositioned in 14% of ceh-36 ( - ) embryos ( Table 3; Fig . 3E ) . This is consistent with the left-right migration phenotype and low penetrance observed in our lineage data ( 1/8 ok795 embryos , 12 . 5% ) , increasing confidence in the low-penetrance defects identified by lineage analysis . Multiple pharyngeal gland cell precursors had cell cycle and position defects in ceh-36 mutants . For example , the daughters of the MSaapapa cell normally produce a pharyngeal gland cell and a programmed cell death and the early division of this cell was the largest division-timing defect we observed in ceh-36 mutants ( Fig . 3A ) . Precursors of four of the five pharyngeal gland cells express CEH-36::GFP and all four of these had partial penetrance defects in cell cycle or position ( Figs . 3A , 4 ) . Since pharyngeal gland cells are known to be required for feeding and viability [45] , we examined them for additional defects by examining expression of the pharyngeal gland marker hlh-6::GFP in elongated ceh-36 ( ok795 ) embryos . We observed altered pharyngeal gland morphology in 20% of ceh-36 ( ok795 ) elongated embryos . An additional 9% of embryos were missing one or more hlh-6::GFP-positive cells ( Fig . 3B , Table 2 ) , suggesting that ceh-36 regulates not only gland cell cycle patterns and morphology but also terminal fate . While only 41% ( 23/56 ) of larvae with normal gland morphology arrested prior to the L4 stage , 92% ( 46/50 ) of larvae with abnormal gland morphology arrested . Thus , defects in pharyngeal gland morphology predict larval arrest in ceh-36 mutants . Defects occurred in 223 unique cells , typically with low penetrance; only 82/223 ( 37% ) cells were defective in two or more ( of eight ) ok795 embryos . Most of the defective cells normally express CEH-36::GFP ( 77% ) , significantly more than the 30% fraction of all cells that express ceh-36 ( p < 10-90 , chi-squared test ) . Most of the defective cells that do not normally express ceh-36 were only called as defective in one embryo . Still , even defects seen in a single embryo were enriched in expressing cells ( 59% of such cells express CEH-36::GFP ) . While cells with prior cell cycle defects were 2 . 9-fold more likely to have position defects ( p < 10-9 ) , 90% of cells with position defects had no detectable cell cycle defect . Only 22 expressing cells had defects in at least 50% of analyzed embryos ( e . g . Fig . 4 , S2 Table ) . The low penetrance of most individual defects may explain the viability of ceh-36 ( - ) embryos . We determined whether cells with low penetrance defects have noticeable defective terminal positions or numbers by examining several fluorescent markers expressed in these cells ( Table 2 ) . Reporters for two cells previously reported as requiring ceh-36 ( MI ( sams-5 ) and ASEL ( gcy-5 ) ) showed the expected terminal defect frequencies in the ceh-36 deletion . As described above , the visceral muscle reporter FKH-4::GFP and the pharyngeal gland reporter hlh-6p::GFP also showed terminal position defect frequencies consistent with the observed embryonic defects . Finally , a FLP-1::GFP reporter reported as expressed in the AVK neuron ( which expresses ceh-36 but was not identified as defective in our analysis ) showed little or no terminal defects ( ~2% ) . Given that the mutant embryos hatch without major morphological defects despite an average 40 cells with position defects and 10 cells with altered division timing , development of C . elegans embryos must be robust to a substantial amount of developmental error . The rescuing CEH-36::GFP transgene expression is typically strongest several divisions before the birth of the terminal cells where most defects were identified , suggesting that defects in ceh-36 ( - ) may result from regulatory events occurring in mitotic progenitor cells . If this is true , partially penetrant defects should preferentially co-occur in closely related cells within a given embryo . We identified 71 examples of defective sister cell pairs in ceh-36-expressing cells . We found preferential co-occurrence of defects in sisters for seven embryos ( p < 0 . 001 ) by using a bootstrap evaluation , and this co-occurrence was only significant in cells expressing ceh-36 . This along with the early and dynamic CEH-36::GFP expression suggests that ceh-36 regulates development in part through its activity in progenitor cells , rather than the terminal cells that exhibit the defects . To confirm that most defects identified in ceh-36 ( ok795 ) embryos result from loss of ceh-36 , we specifically examined high-penetrance ( ≥6 of 8 ok795 embryos ) position defects in an embryo carrying a second predicted ceh-36 null mutation ( ky646 ) . We found four of the five cells examined had similar defects in this embryo . We examined these cells in two ceh-36 ( ok795 ) embryos expressing CEH-36::GFP , and one embryo with mosaic CEH-36::GFP expression , and found that these defects were rescued in all CEH-36::GFP expressing cells . Taken together , these results show that ceh-36 regulates the robustness of cell cycle and migration patterns in many cells . Our analysis did not explicitly test for changes in cell fate , but given the known role of ceh-36 in fate specification [38–40] , there may be additional unidentified cells with defects in fate , but not position or cell cycle timing . Most defects in ceh-36 ( ok795 ) have low penetrance , so other transcriptional regulators likely function in parallel with ceh-36 to ensure robust development . Therefore , we searched for transcription factors that might act redundantly with ceh-36 ( Fig . 5 ) . Previous work demonstrated that the three OTX family members ceh-36 , ceh-37 , and ttx-1 can rescue the others’ mutant phenotypes when expressed in the appropriate cells [39] . We asked if these genes’ early embryonic expression overlaps with that of ceh-36 by lineage analysis of fluorescent reporters and single molecule ( sm ) RNA-FISH [43] . Lineage analysis of a ceh-37 promoter-fusion reporter[46] identified ten cells where its expression overlaps spatially but not temporally with ceh-36; the ceh-37 reporter is expressed after CEH-36::GFP in these cells ( Fig . 5A ) . The ceh-37 reporter is also expressed in several lineages that do not express CEH-36::GFP . ceh-37 transcripts identified by smRNA-FISH did not overlap with positions of ceh-36 transcripts prior to the 50-cell stage and there was only a small amount of overlap between the 50 and 200 cell stages ( Fig . 5B , S5 Fig ) . We could detect no embryonic expression of a ttx-1 promoter reporter prior to morphogenesis and little or no overlap between ttx-1 and ceh-36 transcripts by smRNA-FISH ( Fig . 5B ) . We examined these genes’ expression in ceh-36 ( ok795 ) by smRNA-FISH and observed no ceh-36 transcripts and no changes in ceh-37 or ttx-1 expression . We also observed no substantial increase in ceh-36 ( ok795 ) lethality after ttx-1 or ceh-37 RNAi . This indicates that most ceh-36-expressing cells do not express other OTX homologs in wild-type or ceh-36 mutant embryos , and redundancy with these factors is unlikely to explain the low penetrance of most ceh-36 mutant defects . We mined the EPIC database of embryonic expression patterns [1 , 23] for additional factors coexpressed with ceh-36 , and identified substantial coexpression with the PITX homolog unc-30 . An UNC-30::GFP fosmid “Transgeneome” reporter [22] was transiently expressed at the same time as CEH-36::GFP in the descendants of the ABplp and ABprp progenitor cells ( together “ABpxp” ) , which give rise to diverse cell types , but not in other CEH-36-expressing lineages . We confirmed this expression overlap between endogenous ceh-36 and unc-30 transcripts in ABpxp-derived cells by lineage tracing ( Fig . 5C ) and observed significant overlap of these genes’ endogenous transcripts by smRNA-FISH between the 28-cell and 50-cell stages ( Fig . 5D ) . ceh-36/OTX and unc-30/PITX both encode bicoid-type homeodomains that are predicted to bind similar target sequences [47 , 48] . In addition , the combined frequency of position and cell cycle defects in ceh-36 ( ok795 ) embryos was lower ( 3% ) in the ABpxp lineages than in other CEH-36::GFP-expressing cells ( 12% ) suggesting that ceh-36 may have more redundancy in ABpxp than in other lineages . This suggested the possibility that these two factors might act redundantly to regulate the development of the ABpxp lineages . In addition to its early ABpxp expression , we observed UNC-30::GFP in the six embryonic type D GABA-ergic motorneurons as well as a few other neurons ( PVP , AWA , ASG , AIB , ASI and GLR ) at morphogenesis ( bean stage ) , consistent with the known role of unc-30 in the terminal differentiation of type D neurons [49] ( S6 Fig ) . Consistent with the phenotypes of other unc-30 alleles , the deletion allele unc-30 ( ok613 ) is uncoordinated yet fully viable , with no embryonic or larval arrest ( Table 1 ) . We tested for redundancy between unc-30 and ceh-36 by examining the progeny of a strain homozygous for both unc-30 ( ok613 ) ; ceh-36 ( ok795 ) and carrying the rescuing extrachromosomal CEH-36::GFP fosmid . Animals that had lost the rescuing transgene displayed 100% lethality ( 54% embryonic , 46% larval ) , while embryos expressing CEH-36::GFP had no embryonic lethality and low larval arrest rates ( Table 1 ) , with 75% progressing to L4 . The residual larval arrest rate could result from transgene mosaicism or incomplete rescue by the CEH-36::GFP transgene . This indicates that ceh-36 and unc-30 are redundantly required for viability . The unc-30 ( ok613 ) ; ceh-36 ( ok795 ) double mutants displayed visible phenotypes characteristic of defects in ABpxp-derived cells not observed in either single mutant ( Table 3 , Fig . 6 ) . These included variable abnormalities in body morphology ( Vab ) defects , which are also seen when ABpxp-derived cells fail to act as a substrate for hypodermal enclosure [50 , 51] , “no backend” ( Nob ) tail defects characteristic of severe defects in patterning posterior cells including many derived from ABpxp [52] , and a “rod-like” arrest posture and large edemas near the pharynx characteristic of defects in the excretory system [53] , which is formed by descendants of ABpxp cells . Double mutants did not contain the more anterior head “vacuoles” we saw in ceh-36 ( ok795 ) single mutants; however this phenotype could be masked by the more severe Vab and excretory phenotypes . Taken together , ceh-36 and unc-30 are redundantly required for viability and for aspects of normal development associated with cells produced by ABpxp . The highly penetrant viability and morphological phenotypes of unc-30 ( ok613 ) ; ceh-36 ( ok795 ) double mutants led us to hypothesize that these animals would have more frequent cell lineage and position defects in the cells that normally coexpress both factors . We tested this by automated lineage analysis of six unc-30 ( ok613 ) ; ceh-36 ( ok795 ) embryos that had lost the rescuing CEH-36::GFP transgene ( Fig . 7 , S2 Table ) . We observed a significant increase in cell cycle and cell position defects in the ABpxp lineages of unc-30; ceh-36 double mutants as compared to ceh-36 alone . Double mutant embryos averaged 15 . 5 cell cycle defects and 94 . 7 position defects per embryo in ABpxp compared with 2 . 25 and 11 in ceh-36 single mutants . We also saw a smaller increase in position defects for cells that do not normally express either CEH-36::GFP or UNC-30::GFP ( 64 vs 26 . 1 ) , consistent for a role of the ABpxp cells in migration of cells from other lineages . In contrast , we saw no corresponding increase in cell cycle defects in double mutants for nonexpressing lineages ( Fig . 7 ) , and no corresponding defects in UNC-30 single mutants ( S7 Fig ) . We observed an increased co-occurrence of defects in sister cell pairs in the ABpxp lineages of double mutants ( 27 . 2 sisters pairs per embryo ) compared with ceh-36 ( ok795 ) alone ( 3 . 8 sister pairs per embryo ) . Because unc-30 expression in the ABpxp lineage is even more transient than that of ceh-36 , the increased co-occurrence of defective sister cells likely results from primary defects in progenitor cells . We individually examined ceh-36 ( ok795 ) single mutant and unc-30 ( ok613 ) ; ceh-36 ( ok795 ) embryos for “defect trios” of a defective mother with two defective daughter cells . While ceh-36 ( ok795 ) embryos have on average 1 . 9 of these defect trios ( none in ABpxp ) , double mutant embryos average 13 . 5 defect trios ( 10 . 8 in ABpxp ) . We observed only one defect trio in three unc-30 ( ok613 ) ; ceh-36 ( ok795 ) embryos expressing rescuing CEH-36::GFP , indicating that these defects do not result from unc-30 ( ok613 ) . Together this suggests that unc-30 and ceh-36 cooperate to regulate robust lineage patterning of ABpxp-derived progenitor cells . Two cell divisions ( ABplpappa and ABplppaa ) each displayed a novel type of defect we term “anterior-posterior reversals” in 2 of 6 double mutant embryos . In this class of defect , the anterior daughter of a division adopts the division pattern of the posterior daughter and vice versa . This was evident in the patterns of asymmetric division timing , cell death and migration ( Fig . 7B-D ) . For example , ABplpappap , which undergoes cell death in the wild type , survives and divides in the double mutant . Meanwhile , that cell’s anterior sister ABplpappaa , which should generate RMEV and the excretory canal cell , instead undergoes programmed cell death . Consistent with this being a fate reversal , the division occurs with normal orientation and the daughters of the cell that should have died go on to adopt positions characteristic of RMEV and the excretory canal cell . Defects in these fate-reversed cells or their failure to function correctly in their new location could explain some of the excretory system edema observed in the double mutants . We identified numerous defects in the organization of the ventral midline in the double mutants . Several ABpxp-derived cells failed to respect the midline and crossed to the opposite side; these were distinct from those seen in ceh-36 single mutants ( Fig . 7E ) . Also , in contrast with ceh-36 single mutants , the double mutants had a much larger number of ( presumably nonautonomous ) defects in cells that normally express neither ceh-36 nor unc-30 . Most of these defects ( 43/65 ) were in cells derived from the ABpxa lineages in cells that should form the ventral epidermis . Previous work showed that in the process of ventral enclosure , the epidermal cells migrate over ABpxp-derived substrate cells , some of which are mispositioned in the double mutants . The “leading cells” hyp6/ABpxaappap and hyp7/ABpxaappaa , which initiate ventral enclosure , along with adjacent migrating epidermal cells hyp4 , G2 , and W , had the largest magnitude defects in cell position of nonexpressing cells ( Fig . 7E ) . This suggests that ceh-36 and unc-30 regulate development of the ABpxp-derived substrate for normal epidermal migration and morphogenesis . Several ABpxp-derived cells had position defects of much larger magnitude than we observed in ceh-36 ( - ) alone ( Fig . 7F ) . Among the cells with the largest defects ( average 8 . 9 micron ( >2 cell diameters ) deviation from expected position compared with 1 . 9 microns in ceh-36 alone and 1 . 6 microns in wild type ) were two sister pairs that normally produce two DB neurons and the excretory duct and G1 pore cells ( Fig . 8A , B ) . In wild-type embryos , these cells migrate from anterior lateral positions to the ventral midline where the duct and pore cells connect with the excretory canal cell to form a continuous three-celled tube ( Fig . 8C ) [54] . Previous work showed that the HMX homeodomain transcription factor mls-2 is required for robust development of the excretory duct and pore [55] . MLS-2::GFP is expressed in several lineages including the precursors of the excretory duct and pore ( Fig . 8A , C ) . To determine if loss of mls-2 leads to cell migration defects similar to those seen in unc-30;ceh-36 double mutants , we traced the lineages and cell positions of the excretory system cells in 23 mls-2 mutant embryos ( Fig . 8D ) . We found migration failure or inappropriate migration into the head in 43% ( 10/23 ) of duct cells and 57% ( 13/23 ) of pore cells indicating that mls-2 is required for robust migration of these cells . Consistent with this , previous work [55] found that 5 of 25 mls-2 mutant larvae were missing an excretory tube cell; the difference in rates suggests that in some cases the misplaced cells may eventually migrate to the correct position , or that another cell may sometimes adopt a duct or pore fate . We tested whether mls-2 expression depends on ceh-36 and unc-30 by measuring the expression of a genomically integrated rescuing MLS-2::GFP reporter [55] in unc-30; ceh-36 double mutant embryos . MLS-2::GFP was expressed normally in ceh-36 ( ok795 ) single mutant embryos ( n = 6 ) . Since MLS-2::GFP is expressed later and ~10-fold more strongly than CEH-36::GFP in the excretory duct and pore lineages ( Fig . 8A ) , we were able to compare MLS-2::GFP expression between double-mutant embryos carrying the rescuing CEH-36::GFP with unrescued embryos . We found that in all ( 7/7 ) embryos carrying CEH-36::GFP , MLS-2::GFP was robustly expressed in both the duct and pore precursors , and the duct and pore cells migrated normally . In contrast in six embryos ( 12 duct/pore lineages ) that had lost the rescuing transgene and expressed no CEH-36::GFP , 58% of duct/pore lineages ( 7/12 ) had no MLS-2::GFP expression , with the remaining lineages expressing MLS-2::GFP at lower levels than in wild-type or rescued embryos . Absence of MLS-2::GFP expression predicted migration defects; all seven duct or pore cells with no MLS-2::GFP expression had severe migration defects , while three of five MLS-2::GFP-expressing duct/pore cells migrated normally , sometimes with moderate delays . MLS-2::GFP expression in other lineages that don’t normally express ceh-36 or unc-30 was unaffected . Discontinuities in the excretory tube are associated with formation of edemas and eventual lethality with a characteristic rod-like posture [53–56] . Thus the edemas ( Table 3 ) and rod-like lethality we see in unc-30; ceh-36 double mutants could be explained by the duct and pore migration defects or defects in specification of these cells or the canal cell . We conclude that ceh-36 and unc-30 are required for robust mls-2 expression in ABpxp descendants that give rise to the excretory system , and that misregulation of mls-2 may account for the observed phenotypes in those cells .
The lineage-specific cellular phenotypes and defect penetrance in ceh-36 ( - ) and the ABpxp-specific functional interaction between unc-30 and ceh-36 are consistent with context-dependent roles for these factors . Each factor has distinct expression outside of the early ABpxp coexpression , suggesting that each may work with other factors in these other lineages; indeed , unc-30 is a well-established regulator of motor neuron differentiation later in development [49] , and ceh-36 mutants have partially penetrant defects in lineages where unc-30 is not expressed . Even within ABpxp , most defects were still partially penetrant even in unc-30; ceh-36 double mutants , consistent with the existence of additional redundant factors . One role of these factors is to directly or indirectly regulate the expression of mls-2 . Intriguingly , mls-2 may itself act as a progenitor identity factor , as it regulates the development of lineally-related embryonic cells including glial , excretory and neuronal cells [42 , 55 , 57] and is expressed in these cells’ progenitors . In fact , mls-2 is required for expression of ceh-36 in the AWC neurons [42] , suggesting that ceh-36 ( with unc-30 ) indirectly regulates its own expression later in development . Similarly , ceh-36 and unc-30 can bind to the unc-30 promoter [4] , which is intriguing given that the later expression of unc-30 in GABA-ergic motor neurons occurs in ABpxp-derived cells . We suggest a model in which C . elegans develops robustly with an invariant lineage because each of many lineage-specific TFs [1] , provides a small amount of information to each cell about its lineage history . Combining this information from many TFs allows cells to robustly adopt a fate appropriate to their lineage history ( Fig . 9 ) . This model suggests that the expression of each individual factor could be regulated by lineage mechanisms ( e . g . [1] ) in parallel rather than hierarchically . Another intriguing possibility is raised by our observation of cell cycle and migration defects in cells that nonetheless express appropriate terminal fate markers . This suggests that distinct regulators may modularly control different aspects of each cell’s developmental phenotype ( i . e . one factor regulates fate , another , cell cycle , and yet another , migration ) . Our data suggest that ceh-36 and unc-30 act in embryonic progenitor cells to regulate development , which is distinct from their previously characterized role in neuronal terminal differentiation . They are expressed early and transiently in progenitor cells from multiple lineages and these progenitors give rise to varied cell types , similar to multipotent progenitors in other organisms . The migration and cell division defects that we observe occur across these distinct cell types , and while most defects were observed in terminal cells , they were clustered in the lineage suggesting an underlying defect in the common ancestor . Together this strongly suggests that defects occurred in progenitor cells , although it does not rule out additional roles in the subset of terminal cells where ceh-36 expression persists . Early progenitor factors such as ceh-36 and unc-30 may regulate factors important in later progenitor cells , but they could also directly regulate genes expressed in terminal cells by creating stable chromatin alterations , as was recently demonstrated for another factor [58] . Cell division and migration patterns in unc-30; ceh-36 double mutant embryos do not , however , suggest a switch in fate from ABpxp to its sister ABpxa or any other recognizable sublineage . Thus , other ABpxp factors remain to be discovered or other factors are required to specify alternative progenitor fates . Gene regulatory networks are generally robust against biological noise and often employ transcription factors ( TFs ) with overlapping or redundant functions to decrease transcriptional and phenotypic variability [59] . For example , in C . elegans , redundant pairs of GATA factors regulate intestine development [60] , and similar redundancy exists for T-box factors [6 , 61] and HLH factors [62] . Despite the superficial redundancy of these factors , in some cases the single mutants exhibit decreased robustness in fate determination and partial penetrance phenotypes [60 , 63] . Our finding of similar redundancy between the more divergent homeodomain factors from the PITX and OTX classes indicates that redundancy can occur between factors with ancient divergence . Worms , insects , and vertebrates all have PITX and OTX homologs , indicating these factors diverged prior to the common ancestor of these phyla . This is the first demonstration of a genetic interaction between these factors that could reflect functional redundancy . Since PITX and OTX factors can bind the same sequence motif this redundancy could reflect regulation of shared targets; consistent with this , a large-scale study of TF binding by yeast 1-hybrid analysis identified binding of ceh-36 and unc-30 to highly overlapping sets of promoters [4] . However it is also possible that they work through independent parallel mechanisms . Intriguingly , vertebrate PITX and OTX homologs have some expression overlap in the pituitary and nervous system , and it will be interesting to determine whether they act together in vertebrates . Our approach of studying robustness across an entire organism at a single-cell level provides the opportunity to sensitively identify cells where each factor or combination of factors plays a role . For example , the overlapping functions of ceh-36 and unc-30 in the ABpxp sublineage allowed us to identify their role in regulating mls-2 expression in the developing excretory system . Previous studies identified ceh-36 as a regulator of lateral asymmetry for the MI [40] and ASE [38] neurons . The pharyngeal MI neuron is derived from a right lineage , and the left equivalent lineage produces seemingly equivalent cells except for an epithelial cell , e3D , in place of the MI neuron . Mutations in ceh-36 transform MI into an e3D-like cell , and this asymmetry is driven by asymmetric ceh-36 expression in the MI progenitors and not those of e3D [40] . Surprisingly , the same phenotype occurs in a truncating mutant in histone H3 , likely acting downstream of ceh-36 [64] . The fact that loss of either an asymmetrically expressed factor ( ceh-36 ) or a symmetrically expressed factor ( histone H3 ) leads to the same phenotype underscores that asymmetry in regulatory networks can influence which cells have phenotypes . While we do observe asymmetric CEH-36::GFP expression in MI , we found that most expression in other lineages is L-R symmetric and most penetrant defects were seen in both symmetric pairs . However we did identify defects in lateral identity , such as the migrations of the left intestinal muscle and anal depressor , in cells where ceh-36 expression is normally L-R symmetric . This suggests that ceh-36 contributes to the regulation of lateral identity even in cells where it is symmetrically expressed . Although our approach improved the sensitivity for detection of cellular phenotypes compared to previous studies and methods , it is likely that additional cellular defects remain unidentified . For example we identified many defects in only one or two embryos; further improvements to automated cell tracking methods to increase accuracy and reduce curation time would allow analysis of higher numbers of embryos and more sensitive and reliable identification of lower penetrance defects , In the absence of markers for terminal differentiation , a cell with normal migration and division patterns but altered terminal fate cannot be detected . Repeating lineage tracing with a panel of strains expressing distinct fate markers can increase the power to detect lineage transformations [25] , but this approach is labor-intensive . On the other hand , some of the cell position defects we identified were apparent only by lineage tracing and not when scored using a terminal fluorescent marker in larvae , which reflects the high sensitivity of the quantitative methods and possibly the correction of some position defects later in development . The power of future applications of lineage-based phenotyping methods would be increased by new methods to directly assay fate transformation while maintaining throughput; such as by analyzing multiple fate markers simultaneously in different colors .
ceh-36 ( ks86 ) X ceh-36 ( ky640 ) X ceh-36 ( ky646 ) X ceh-36 ( ok795 ) X ceh-37 ( ok272 ) X ceh-37 ( ok642 ) X unc-30 ( ok613 ) IV unc-119 ( tm4063 ) III mls-2 ( cs71 ) X bwIs2[flp-1::GFP + pRF4 ( rol-6 ( su1006 ) ) ][65] nsIs396[sams-5 3′::4xNLS-GFP + lin-15 ( + ) ] V [40] ntIs1[lin-15 ( + ) ; gcy-5::GFP][38] sEx14784[ceh-37::GFP][46] ujEx173[CEH-36::GFP + unc-119 ( + ) ] ujEx130[CEH-36::GFP + myo-2::mCherry + myo-3::mCherry] oyIs48[ceh-36 2KB promoter::GFP][39] stIs10501[ceh-36 5KB promoter::HIS-24-mCherry][1] ujIs113[pie-1::mCherry::H2B + unc-119 ( + ) ; Pnhr-2::mCherry::histone + unc-119 ( + ) ] II wgIs108[FKH-4::GFP+ unc-119 ( + ) ] I [22] wwIs19[hlh-6::GFP + unc-119 ( + ) ] X [66] csIs55[MLS-2::GFP] X [55] wgIs395[UNC-30::GFP+unc-119 ( + ) ] All strains were grown as previously described [67] . N2 was used as the wild-type reference strain . All manipulations were performed at room temperature ( 21°C ) . Knockout consortium alleles ceh-36 ( ok795 ) and unc-30 ( ok613 ) were outcrossed three times . VC579 ceh-36 ( ok795 ) /szT1 hermaphrodites were mated with males carrying an extrachromosomal copy of ceh-36 ( + ) ::GFP ( ujEx173 ) , and F2 progeny were tested for ok795 , which deletes 406 base pairs of ceh-36 , by PCR . Additional outcrossing of ceh-36 ( ok795 ) was with N2 males . unc-30 ( ok613 ) was outcrossed by mating unc-30 ( ok613 ) hermaphrodites with N2 males and picking F2 Unc progeny . Combinations of reporters with ceh-36 ( - ) were created using a mating strategy that did not produce heterozygous ceh-36 ( - ) hermaphrodites at any step or else were verified using PCR . Combinations of unc-30 ( ok613 ) and ceh-36 ( ok795 ) were created using nT1[qIs51] ( IV;V ) to balance unc-30 ( ok613 ) while testing for ceh-36 ( ok795 ) by PCR . unc-30 ( ok613 ) /nT1[qIs51] ( IV;V ) ; ceh-36 ( ok795 ) males were mated with unc-119 ( tm4063 ) ; ceh-36 ( ok795 ) ; ujEx173[ceh-36::GFP + unc-119 ( + ) ] hermaphrodites , and F2 Unc progeny with the genotype unc-30 ( ok613 ) ; ceh-36 ( ok795 ) ; ujEx173 were isolated . ujEx173 was generated by microparticle bombardment of the CEH-36::GFP Transgeneome fosmid [22] into unc-119 ( tm4063 ) using methods previously described [22 , 68] . ujIs113 was generated by co-bombardment of pAA64H2B ( pie-1::mCherry-H2B::pie-1UTR ) [69] and pJIM20_nhr-2 ( nhr-2promoter::HIS-24-mCherry::let-858YTR ) into unc-119 ( tm4063 ) . ujEx130 was generated by injection of the CEH-36::GFP transgeneome fosmid into ceh-36 ( ok795 ) worms . All strains were grown at 20°C for over two generations before scoring . Young adult hermaphrodites were dissected at room temperature in egg buffer ( 118mM NaCl , 48mM KCl , 2mM CaCl2 , 2mM MgCl2 , 25mM HEPES ) [70] , and embryos with four or more cells were transferred onto NGM plates . Embryos were counted and replaced in the 20°C incubator . Embryonic lethality was determined by counting unhatched embryos on the subsequent two days . Due to a variable rate of larval development for ceh-36 ( - ) mutants , L4 hermaphrodites were picked off the NGM plates and counted as survivors for one week following dissection . We observed no L4 lethality or adult sterility . Similar rates of lethality for ceh-36 ( ok795 ) were obtained by counting eggs laid by free moving ceh-36 ( ok795 ) hermaphrodites and following their progeny to the L4 stage . To track the presence of the fosmid in rescued animals , we generated unc-119 ( tm4063 ) ; ceh-36 ( ok795 ) worms that were doubly rescued by the presence of the fosmid and reduced the larval lethality of ceh-36 ( ok795 ) . This allowed us to score absence of the fosmid by the presence of the Unc phenotype . Lethality checks of unc-30 ( ok613 ) ; ceh-36 ( ok795 ) double mutants followed a similar protocol . unc-30 ( ok613 ) ; ceh-36 ( ok795 ) ; ujEx173[ceh-36::GFP + unc-119 ( + ) ] young adult hermaphrodites were dissected and embryos counted as described above . Embryonic lethality was scored the next morning . Unhatched embryos were mounted in 20μm beads in egg buffer/methyl cellulose [71] and scored for CEH-36::GFP expression in ASE and AWC neurons . All hatched L1s were examined using a fluorescent dissecting microscope for CEH-36::GFP expression in ASE and AWC neurons ( Leica M205FA , Leica Microsystems ) . CEH-36::GFP expressing and non-expressing L1s were transferred to separate plates , and several larvae were found and transferred the following day . L4 survivors were picked off the NGM plates and counted as survivors for one week following dissection . A similar procedure was used to score survival of ceh-36 ( ok795 ) worms carrying wwIs19 ( hlh-6::GFP ) . All strains were grown at 20°C for over two generations before young adult hermaphrodites were dissected at room temperature in egg buffer and embryos with four or more cells were mounted into a solution of 20μm beads in egg buffer/methyl cellulose . Sealed slides containing 10–15 embryos were incubated overnight at 20°C and scored the following morning . Examination of unc-30 ( ok613 ) ; ceh-36 ( ok795 ) double mutant phenotypes followed the above protocol except that embryos were also scored for CEH-36::GFP expression in ASE and AWC neurons following DIC examination to exclude rescued animals . We acquired confocal images with a Leica TCS SP5 resonance scanning confocal microscope ( 67 z planes at 0 . 5 μm spacing and 1 . 5 minute time spacing ) and generated lineages using StarryNite and AceTree as previously described [20 , 21 , 72–75] . Embryos were mounted in egg buffer/methyl cellulose with 20μm beads as spacers [71] and imaged at 22°C using a stage temperature controller ( Brook Industries , Lake Villa , IL ) . We updated the 4D reference model of wild-type C . elegans embryogenesis through the 600-cell stage using eighteen embryos expressing fluorescently tagged histone by tracing four embryos to the comma stage . Deviation of cell-cycle length , division orientation , and anterior-posterior position for eight ujIs113; ceh-36 ( ok795 ) embryos , one ujIs113; ceh-36 ( ky646 ) and five ujIs113; unc-30 ( ok613 ) ; ceh-36 ( ok795 ) ; ujEx173 ( CEH-36 ( + ) ::GFP ) embryos was calculated as previously described [27] . Deviant cell cycle length was defined as beyond three standard deviations and five minutes of the average wild-type cell-cycle length . For position defects , we calculated the expected position of each cell in the embryo based on the overall rotation of the embryo and the wild-type model and scored the distance from the expected position . Cells were considered mispositioned if their mean or maximum distance was more than 3 . 5 standard deviations greater than the wild-type mean . We also developed a heuristic “neighbor distance” score , consisting of the mean distance of the cell to the 10 cells that are closest to that cell in wild-type embryos , and required 3 . 5 standard deviation defects in this score as well . Deviant cell position was confirmed by comparison of time-lapse 3D-models for both mutant and wildtype embryos . Defects in all cells identified through statistical analysis mentioned in the text were confirmed by manual retracing of curated lineages . For bootstrap analysis of defective sister pairs , the number of total defective cells ( X ) and defective sister pairs ( Y ) were separately counted for each embryo as well as for defined subgroups ( e . g . the ABpxp lineage or ceh-36 expressing versus non-expressing ) . Cells born before the onset of ceh-36 expression were not considered . The number of defective sister cells expected by chance was determined by 100 , 000 iterations of counting sister pairs from ( X ) randomly picked cells from a defined subgroup . A p-value was calculated by dividing the total number of iterations equal to or greater than the observed value ( Y ) with 100 , 000 . Mixed-stage embryos were picked into a solution of 10mM sodium azide and 1% methyl cellulose in egg buffer with 25μm beads on top of a glass slide . Coverslips were sealed using petroleum jelly , and embryos became immobilized due to azide and hypoxia . All fluorescent reporters were scored by analyzing confocal GFP and DIC z-stacks of pretzel-stage embryos , which provided a more discrete developmental stage than possible in larvae due to the larval arrest of ceh-36 ( - ) mutants . Positional defects and wild-type variation of fluorescent reporters were measured using LASAF software . Single-molecule RNA FISH was performed as previously described [43 , 76] .
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Animals develop as one initial cell , the fertilized egg , repeatedly divides and its progeny differentiate , ultimately producing diverse cell types . This occurs in large part by the expression of unique combinations of regulatory genes , such as transcription factors , in precursors of each cell type . These early factors are typically reused in precursors of different cell types . The nematode worm Caenorhabditis elegans is a powerful system in which to identify developmental regulators because it has a rapid and reproducible development , yet it shares most of its developmental regulators with more complex organisms such as humans . We used state-of-the-art microscopy and computer-aided cell tracking methods to identify the developmental role of worm homologs of the OTX and PITX genes , whose human homologs play a role in the development of the brain , eye , and pituitary among other tissues . We identified broad roles for OTX in regulating development for many distinct cell types including muscles , neurons and skin , and found a redundant role for both OTX and PITX in a subset of cells . Future studies of these genes should address whether these genes also act redundantly in mammals .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
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The Bicoid Class Homeodomain Factors ceh-36/OTX and unc-30/PITX Cooperate in C. elegans Embryonic Progenitor Cells to Regulate Robust Development
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The regular array of distally pointing hairs on the mature Drosophila wing is evidence for the fine control of Planar Cell Polarity ( PCP ) during wing development . Normal wing PCP requires both the Frizzled ( Fz ) PCP pathway and the Fat/Dachsous ( Ft/Ds ) pathway , although the functional relationship between these pathways remains under debate . There is strong evidence that the Fz PCP pathway signals twice during wing development , and we have previously presented a Bidirectional-Biphasic Fz PCP signaling model which proposes that the Early and Late Fz PCP signals are in different directions and employ different isoforms of the Prickle protein . The goal of this study was to investigate the role of the Ft/Ds pathway in the context of our Fz PCP signaling model . Our results allow us to draw the following conclusions: ( 1 ) The Early Fz PCP signals are in opposing directions in the anterior and posterior wing and converge precisely at the site of the L3 wing vein . ( 2 ) Increased or decreased expression of Ft/Ds pathway genes can alter the direction of the Early Fz PCP signal without affecting the Late Fz PCP signal . ( 3 ) Lowfat , a Ft/Ds pathway regulator , is required for the normal orientation of the Early Fz PCP signal but not the Late Fz PCP signal . ( 4 ) At the time of the Early Fz PCP signal there are symmetric gradients of dachsous ( ds ) expression centered on the L3 wing vein , suggesting Ds activity gradients may orient the Fz signal . ( 5 ) Localized knockdown or over-expression of Ft/Ds pathway genes shows that boundaries/gradients of Ft/Ds pathway gene expression can redirect the Early Fz PCP signal specifically . ( 6 ) Altering the timing of ds knockdown during wing development can separate the role of the Ft/Ds pathway in wing morphogenesis from its role in Early Fz PCP signaling .
Planar Cell Polarity ( PCP ) describes the orientation of a cell within the plane of an epithelium . A primary model for studying the genetic control of PCP has been the organization of an array of cell hairs that point toward the distal tip of the Drosophila wing [1] . Two signaling pathways are known to control Drosophila wing PCP , the Frizzled ( Fz ) PCP pathway and the Fat/Dachsous ( Ft/Ds ) pathway [2] , although the functional relationship between these two pathways remains subject to debate [3] . One model , the Tree-Amonlirdviman model , proposes a tiered structure in which long-range gradients of Ft/Ds signaling provide global polarity information that controls the direction of a local Fz PCP signal [4] . In the case of the wing , the proximal expression of Ds and distal expression of Four-jointed ( Fj ) are proposed to generate opposing activity gradients along the proximal-distal ( P-D ) wing axis that control the direction of the Fz PCP signal [5] , [6] . In contrast , studies in the Drosophila abdomen have led to an alternative ‘Two Pathway’ model in which the Ft/Ds and Fz PCP pathways function independently to organize PCP [7] . The resolution of these distinct models is important since both Fz PCP and Ft/Ds pathways have also been shown to be critical for PCP in vertebrate development [8] , [9] and are implicated in human disease [9]–[11] . In an earlier paper we showed that , in addition to organizing wing hair polarity , the Fz PCP pathway is required for the integrity and orientation of cuticle ridges that traverse the adult wing membrane [12] . However , although wing hairs have a common orientation across the wing , ridges are aligned with the anteroposterior ( A-P ) axis in the anterior wing and with the P-D axis in the posterior wing . Consequently , hair and ridge orientation are approximately orthogonal in the anterior wing , but are closely matched in the posterior wing . This presents the problem of how Fz PCP signaling can lead to these two distinct outcomes in anterior and posterior wing cells . Data from our work , and from other labs , has led us to propose a Bidirectional-Biphasic ( Bid-Bip ) model in which two distinct Fz PCP signaling events occur along different axes of the wing ( Figure 1 and [12] ) . In the model , there is an Early Fz PCP signal aligned with the A-P axis that is approximately symmetric in the anterior and posterior wing . This is followed by a Late Fz PCP signal aligned with the P-D axis . For the model , the direction of Fz PCP signaling is defined as the hair polarity that would be specified by the signal . The concept of two Fz PCP signaling events during wing development is not novel; the existence of a distinct Early Fz PCP signal around 18 hours after pupal formation ( a . p . f . ) has been well established by work in the Strutt lab [13] , [14] . However , the notion that the Early Fz PCP signal is oriented along the A-P axis appears to be a novel feature of our Bid-Bip model [12] . Previous evidence for Fz PCP signaling along the A-P axis of the wing has come from the Adler lab . Adler showed that , in addition to the distal cell non-autonomy associated with fz mutant wing clones [15] , fz clones in the anterior wing show cell non-autonomy posterior to the clone , whilst posterior fz clones show cell non-autonomy anterior to the clone [16] . Moreover , as well as the proximal cell non-autonomy associated with mutant clones of the Fz PCP pathway gene Van Gogh/strabismus ( Vang/stbm ) [17] , anterior Vang/stbm clones show anterior cell non-autonomy and posterior Vang/stbm clones show posterior cell non-autonomy [16] . Adler's data argue for Fz PCP signaling along the A-P axis of the wing that has an opposite orientation in the anterior and posterior wing . This matches the description of our proposed Early Fz PCP signal ( Figure 1 ) . Key to our Bid-Bip model is the notion that different features of the wing are organized by the two Fz PCP signaling events . The model proposes that posterior ridges are organized by the Early Fz PCP signal , while anterior ridges and wing hairs are organized by the Late Fz PCP signal ( Figure 1 ) . This is supported by our finding that early over-expression ( e . g . 10 hours a . p . f . ) of the Sple isoform of the PCP protein Prickle reorients hairs and ridges in both the anterior and posterior wing , whereas late Sple over-expression ( e . g . 19 hours a . p . f . ) reorients hairs and anterior ridges , but not posterior ridges [12] . This observation suggests that posterior ridges are specified earlier than anterior ridges . Consequently , the Bid-Bip model proposes that ridges and hairs are organized by the same Fz PCP signaling event in the anterior wing , but by different ( and differently oriented ) Fz PCP signaling events in the posterior wing . Thus , the model accounts for the differing relationships between ridge and hair orientation observed in the anterior and posterior wing . The model also implies that orthogonal hair and ridge orientation is the normal outcome of a single Fz PCP signaling event in the wing . One further feature of our Bid-Bip model is the proposal that the Early and Late Fz PCP signals differ in the use of the Prickle protein isoforms Pk and Sple within the Fz PCP pathway . Pk and Sple share a C-terminus containing a PET domain and three LIM domains , but the 13 N-terminal amino acids in Pk are replaced by 349 N-terminal amino acids in Sple [18] . In the model , the Early Fz PCP signal employs the Sple isoform and the Late Fz PCP signal employs the Pk isoform . ( For this reason , we will refer to the Early Fz PCP signal as Fz ( Sple ) and the Late Fz PCP signal as Fz ( Pk ) in this paper . ) This agrees with previous work from Strutt that showed the Pk isoform is only required for Late Fz PCP signaling [13] . Consequently , a prediction of the Bid-Bip model is that loss of Pk isoform activity ( i . e . a pkpk mutant ) blocks Late Fz PCP signaling and so only the Early Fz PCP signal occurs . Consistent with this prediction , there is a regular , approximately orthogonal , relationship between hair and ridge orientation across the entire pkpk mutant wing suggesting that only a single Fz PCP signaling event ( i . e . Fz ( Sple ) ) has occurred [12] . Moreover , Adler has shown that in a pkpk mutant wing , the cell non-autonomy of fz− clones is primarily posterior to anterior clones and anterior to posterior clones [16] , suggesting that Fz PCP signaling is principally along the A-P axis and in opposite directions in the anterior and posterior wing . This fits our model's proposal that only the Early Fz PCP signal is active in a pkpk mutant wing ( Figure 1 ) . Our Bid-Bip Fz PCP signaling model differs significantly from previous models of PCP in the Drosophila wing . Therefore , it provides an alternative template for an evaluation of the role of the Ft/Ds pathway in wing PCP . The work presented in this paper addresses the relationship of the Ft/Ds and Fz PCP pathways in the context of our model and concludes that a primary role of the Ft/Ds pathway in wing PCP is to control the direction of the Early Fz ( Sple ) signal .
Membrane ridge orientation differs between the anterior and posterior of the wild-type Drosophila wing [12] . The boundary between these two regions lies in the vicinity of the L3 vein , but is not possible to pinpoint on wild-type wings , as ridge orientation is difficult to determine adjacent to wing veins . Homozygous rhove-1 , vn1 wings lack wing veins L2-5 and display altered wing shape [19] . Using our Cuticle Refraction Microscopy ( CRM ) technique [12] in conjunction with conventional light microscopy , we find that rhove-1 , vn1 wings retain wild-type hair polarity and ridge orientation ( compare Figure 2A with Figure 3A ) . In the absence of veins on the rhove-1 , vn1 wing , it becomes clear that the boundary between anterior A-P and posterior P-D ridge orientation can be mapped to a narrow region , about 2–3 cells wide , that forms an approximately straight line along the P-D axis of the wing ( yellow shaded region in Figure 2A and 2B ) . Our ability to finely map this region implies an abrupt change in PCP on the wing and for this reason we refer to it as a ‘PCP Discontinuity’ ( PCP-D ) . The absence of veins and unusual wing morphology of the rhove-1 , vn1 wing make the location of the PCP-D difficult to pinpoint . To overcome this problem , we over-expressed Argos uniformly during dorsal wing development ( MS1096-gal4; UAS-argos ) . The Argos protein is a negative regulator of EGF signaling and Argos over-expression in the dorsal wing antagonizes longitudinal vein development resulting in variable loss of dorsal longitudinal veins including L3 ( Figure 2C and [20] ) . These wings reveal that the discontinuity in ridge orientation ( i . e . the PCP-D ) maps to the normal location of the L3 vein ( Figure 2D ) . According to our Bid-Bip Fz PCP signaling model ( Figure 1 ) , loss of Pk isoform activity ( i . e . a pkpk mutant ) inactivates the Late Fz ( Pk ) signal , but not the Early Fz ( Sple ) signal [12] . Therefore , since only Early Fz ( Sple ) signaling is active on a pkpk mutant wing , the pkpk mutant hair polarity pattern should reflect the direction of the Fz ( Sple ) signal . At first glance , the intricate swirling hair patterns observed on a pkpk wing appear an improbable signaling output [12] , [18] , [21] , [22] . However , since hair whorls , and other abrupt changes in hair polarity , on a pkpk wing are normally adjacent to wing veins , we hypothesized that an alternate hair pattern might appear in the absence of vein differentiation . To test this , we generated pkpk30; rhove-1 , vn1 homozygous flies , which lack wing veins L2-5 and have no Pk isoform activity . We found that these wings lack most of the abrupt changes in hair polarity normally found on a pkpk mutant wing ( see , for example , Figure S1 ) . However , the approximately orthogonal relationship between hair polarity and ridge orientation , seen in both the anterior and posterior wing of a pkpk mutant wing [12] , is maintained ( Figure 2E and 2F ) . On a pkpk30; rhove-1 , vn1 wing , anterior hairs consistently have a posterior component to their polarity and posterior hairs have an anterior component to their polarity . The boundary between anterior and posterior pointing hairs can be mapped to an approximately straight line , around 2–3 cells wide , along the P-D axis ( yellow shaded region in Figure 2E and 2F ) . This position is also associated with a discontinuity in ridge orientation , which changes abruptly in this region ( Figure 2F ) . To localize this PCP discontinuity , we over-expressed Argos in a pkpk mutant wing ( MS1096-gal4; pkpk30/pkpk30; UAS-argos ) , to induce partial loss of dorsal longitudinal veins ( Figure 2G ) . On these wings , it is clear that the discontinuity in hair and ridge orientation maps to the site of the L3 vein ( Figure 2H ) . In summary , we have identified a PCP discontinuity ( PCP-D ) in the Drosophila wing that maps to the site of the L3 wing vein ( although physical differentiation of the L3 vein is not required for the formation of the PCP-D ) . In wild-type wings , the PCP-D represents a discontinuity in ridge orientation , but not hair polarity . However , in wings lacking Pk isoform activity , the PCP-D represents a discontinuity in both ridge orientation and hair polarity . According to our Bid-Bip Fz PCP signaling model ( Figure 1 ) , only Early Fz ( Sple ) signaling is active in a pkpk mutant wing , therefore we conclude that there is a discontinuity in Fz ( Sple ) signaling at the site of the L3 wing vein . We also note that , although hair polarity in a wild-type wing is not disrupted by the removal of wing veins , pkpk mutant hair polarity is significantly modified by wing vein removal ( see Figure S1 ) . This suggests that the output of the Early Fz ( Sple ) signal is significantly influenced by wing vein differentiation , whereas the Late Fz ( Pk ) signal is not . This observation is not consistent with a previous report which concluded that altered wing vein formation does not affect the pkpk mutant wing hair phenotype [21] . However , we note that the genes we have used to alter vein formation ( rho , ve , argos ) are all components of the EGF signaling pathway , whereas the genes used in the early work ( knirpsri , cubitus interruptus and plexus ) are not EGF components . This raises the possibility that it is altered EGF signaling that modifies the Early Fz ( PCP ) signal rather than the physical differentiation of wing veins . The hypomorphic fat1 ( ft1 ) mutant allele is homozygous viable and affects wing shape , but not hair polarity ( Figure 3D ) . We mapped ridge orientation on a ft1 homozygous mutant wing using our CRM technique [12] , and found that ridges in distal regions of the posterior wing show an A-P orientation , in contrast to the normal P-D orientation ( compare Figure 3F with Figure 3C ) . In contrast , anterior ridges on the ft1 wing retain the normal A-P orientation ( compare Figure 3E with Figure 3B ) . The abnormal wing morphology of viable dachsous ( ds ) mutants makes analysis of wing ridges by our CRM technique challenging , although we were able to confirm that the D region ( between veins L4 and L5 ) of a dsUA071/ds05142 heterozygous wing , retains wild-type hair polarity , but has primarily A-P ridges ( data not shown ) . However , we were largely able to overcome this problem by expressing gene-specific RNAi uniformly in the developing dorsal wing . Uniform expression of ds RNAi ( VDRC transformant 36219GD [23] ) in the dorsal wing ( MS1096-gal4; UAS-ds ( IR ) ) alters wing shape and disrupts crossveins ( Figure 3G ) , but produces only localized hair polarity changes in the proximal wing ( red shaded oval in Figure 3G ) . CRM analysis shows that uniform ds RNAi expression alters posterior ridges to a more A-P orientation ( Figure 3I ) , but does not affect anterior ridges ( Figure 3H ) . Uniform expression of ft RNAi ( VDRC transformant 9396GD [23] ) in the dorsal wing ( MS1096-gal4; UAS-ft ( IR ) ) results in a very similar wing phenotype to ds RNAi expression ( data not shown ) . The control of PCP by the Ft/Ds pathway also requires the four-jointed gene [24] , and we have mapped ridge orientation on wings homozygous for the amorphic fjD1 allele . Homozygous fjD1 wings have altered shape , but hair polarity is disrupted in only a small proximal region ( red shaded oval in Figure 3J ) , the same region affected by uniform ft or ds knockdown . We found that posterior ridges on fjD1 homozygous wings also have a more A-P orientation than wild-type ( Figure 3L ) , but anterior ridges are unchanged ( Figure 3K ) . The phenotypes generated using the VDRC ft and ds RNAi lines are unlikely to result from off-target RNAi activity as they phenocopy the established mutant phenotype of these genes . In addition , we were able to reproduce these phenotypes using independent ft ( JF03245 ) and ds ( JF02842 ) RNAi lines from the TRiP project ( Transgenic RNAi Project , Harvard Medical School ) in combination with the same Gal4 driver ( data not shown ) . Curiously , uniform fj RNAi expression using either the VDRC ( transformant 6774GD [23] ) or TRiP ( JF02843 ) stocks failed to give the characteristic fj mutant wing morphology and so these stocks were excluded from this study . Our findings show that reduced activity of the Ft/Ds pathway genes ft , ds and fj alter ridge orientation in the distal posterior wing to a more A-P orientation , without affecting hair polarity in the same region or anterior ridge orientation . Since our Bid-Bip model proposes that posterior ridges are organized by the Early Fz ( Sple ) signal whereas anterior ridges and wing hairs are organized by the Late Fz ( Pk ) signal ( see Figure 1 ) , these results suggest that reduced activity of Ft/Ds pathway genes can alter the Early Fz ( Sple ) without affecting the Late Fz ( Pk ) signal . The fact that posterior ridges still form when Ft/Ds pathway activity is reduced suggests that the role of the Ft/Ds pathway is not to specify posterior ridges , but to direct ridge orientation , presumably by controlling the direction of the Early Fz ( Sple ) signal . Our analysis of wing ridge phenotypes led us to conclude that reduced Ft/Ds pathway activity can affect the direction of the Early Fz ( Sple ) signal without altering the Late Fz ( Pk ) signal . Since the Late Fz ( Pk ) signal is inactivated in a pkpk mutant wing , the pkpk hair polarity phenotype should reflect the direction of the Early Fz ( Sple ) signal [12] . Consequently , if reducing Ft/Ds pathway activity affects the orientation of the Early Fz ( Sple ) signal , we predict that it should significantly modify the pkpk mutant wing hair phenotype . This turns out to be the case . For example , although a ft1 homozygous wing has wild-type hair polarity , the pkpk30 hair polarity phenotype is substantially modified in a ft1 , pkpk30 double mutant wing ( compare Figure 4A with 4D ) . Specifically , in comparison to a pkpk30 homozygote , ft1 , pkpk30 hair polarity is more distal in both the anterior wing ( compare Figure 4B with 4E ) and in distal regions of the posterior wing ( compare Figure 4C with 4F ) . We see a similar modification of the pkpk hair phenotype when driving uniform ft RNAi expression ( VDRC transformant 9396GD ) in a pkpk mutant wing ( MS1096-gal4; pk30 , UAS-ft ( IR ) /pk30 ) , but with more extensive regions of distal hair polarity in the posterior wing and an anterior component to anterior hair polarity ( data not shown ) . Driving uniform expression of ds RNAi ( VDRC transformant 36219GD ) in the dorsal wing of a pkpk mutant also modifies the pkpk hair phenotype to a more distal polarity in the anterior and distal posterior wing ( Figure 4G , 4H and 4I ) . We also generated flies homozygous for both a pkpk allele and for an amorphic allele of lowfat ( lft ) , a recently identified modulator of Ft/Ds signaling [25] . lftTG2 homozygous wings display altered wing morphology and aberrant posterior ridges , but wild-type hair polarity ( [25] and data not shown ) . In lftTG2 , pkpk homozygous wings , the pkpk hair phenotype is modified to a more distal polarity in the anterior and distal posterior wing ( Figure 4J , 4K and 4L ) , in a similar manner to when ft or ds activity is reduced . Hair polarity on fjD1 , pkpk30 homozygous wings is also more distal than the pkpk30 phenotype . However , this effect is less than observed for reduced ft , ds or lft activity and appears region specific . For example , hair polarity in the A region ( anterior to the L2 vein ) of a fjD1 , pkpk30 wing is entirely distal , but in the B region ( between the L2 and L3 vein ) retains a significant posterior component and so is closer to the pkpk30 phenotype ( data not shown ) . These findings show that reduced activity of the Ft/Ds pathway genes ft , ds , fj and lft modify the pkpk30 hair polarity phenotype to a more distal polarity in the anterior wing and distal regions of the posterior wing . This is despite the fact that hair polarity in these regions is not affected by the reduced activity of the same Ft/Ds pathway genes in a wild-type background ( see Figure 3 ) . In the context of our Bid-Bip model ( Figure 1 ) , this supports our proposal that reduced levels of Ft/Ds pathway activity can alter the direction of the Early Fz ( Sple ) signal without affecting the Late Fz ( Pk ) signal . Moreover , our results suggest that the role of Lft in wing PCP is entirely restricted to regulating the Early Fz ( Sple ) signal . In the posterior wing , reduced Ft/Ds pathway activity modifies the pkpk30 hair phenotype to a more distal polarity in the same regions in which reduced Ft/Ds pathway activity alters ridge orientation to a more A-P orientation ( see Figure 3 ) . Since we propose that a single Fz PCP signal specifies orthogonal hair and ridges , we would expect that a change in the Fz ( Sple ) signal direction that results in distal hair polarity should be associated with A-P ridges . To complement the studies described above , we looked at the effect of over-expressing Ft/Ds pathway genes on the Early Fz ( Sple ) and Late Fz ( Pk ) signals . Uniform over-expression of ft ( MS1096-gal4; UAS-ft ) results in similar wing morphology to loss of ft activity ( Figure 5B ) and alters posterior ridges . ft over-expression alters hair polarity in the same proximal region of the wing affected by reduced Ft/Ds pathway gene activity ( see Figure 3 ) , but also generates variable hair polarity changes in more distal regions of the wing ( red ovals in Figure 5B ) . Uniform over-expression of ds or fj results in a similar wing shape , posterior ridge and hair polarity phenotype to reduced activity of the same genes ( Figure 5C and 5D ) . When ft , ds or fj are uniformly over-expressed in a pkpk mutant wing , the pkpk wing hair phenotype is modified to a more distal polarity in the anterior wing and in distal regions of the posterior wing ( Figure 5F , 5G and 5H ) . These modifications of the pkpk hair phenotype are similar to those generated by reduced activity of the same Ft/Ds pathway genes ( see Figure 4 ) . These results show that uniform over-expression of ft , ds or fj modify the pkpk hair polarity phenotype in regions of the wing not affected by over-expression of these genes alone . In the context of our Bid-Bip model ( Figure 1 ) , this suggests that ft , ds and fj over-expression can alter the Early Fz ( Sple ) signal without affecting the Late Fz ( Pk ) signal . The results also imply that both over-expression , and reduced activity , of Ft/Ds pathway genes modify the direction of the Fz ( Sple ) signal to a more distal orientation . Conventionally , gradients of Ft/Ds activity , arising from localized expression of one or more Ft/Ds pathway genes , have been proposed to organize epithelial PCP [26] . In the wing , proximal Ds expression and distal Fj expression have been proposed to generate Ft/Ds activity gradients that organize hair polarity [6] , [14] , [27] . This proposal is supported by studies that show Ds expression is primarily in the proximal wing at 24–26 hours a . p . f . , shortly before the Late Fz PCP signal [6] , [27] . However , at 17 hours a . p . f . , immediately before the Early Fz PCP signal [12] , [14] , Ds protein is present in a P-D stripe along the centre of the wing blade ( see Figure 6H in [27] ) . We stained ds-lacZ wings at 18 hours a . p . f . and detected a corresponding stripe of beta-galactosidase activity that extends along the majority of the wing blade ( Figure 6A ) . Beta-galactosidase activity reduces gradually both anterior and posterior to this stripe , suggesting symmetric gradients of ds expression along the A-P axis . To localize this ds expression , we stained for beta-galactosidase activity in an 18 hours a . p . f . ds-lacZ wing that also expressed Green Fluorescent Protein ( GFP ) under the control of the engrailed ( en ) promoter ( en-gal4 , UAS-gfp ) . The en promoter drives GFP expression throughout the posterior wing with a sharp anterior boundary 4–5 cells posterior to the L3 vein ( Figure 6B ) . In ds-lacZ/en-gal4 , UAS-gfp wings , the peak of beta-galactosidase activity ( red arrowheads in Figure 6C and 6D ) is located anterior to the anterior boundary of GFP expression ( Figure 6D ) implying that the peak of ds expression maps close to the site of the L3 vein . There is no ds expression within the wing pouch of 3rd instar imaginal wing discs [28]–[30] , and little ds expression within the pupal wing blade at 24–26 a . p . f . [6] , [27] . Therefore , we conclude that ds is expressed transiently at the site of the L3 vein around 18 hours a . p . f . , the time Strutt has defined for the Early Fz PCP signal [13] , [14] . Since we propose that the Early Fz ( Sple ) signal converges at the site of the L3 vein and that ds is required for the normal orientation of the Fz ( Sple ) signal , this makes localized ds expression a strong candidate for an organizer of the Fz ( Sple ) signal . fj expression has previously been proposed to form an opposing gradient to ds in the wing , eye and abdomen [6] , [27] , [31]–[33] . However , although there is beta-galactosidase activity at the anterior and posterior wing margin of a fj-lacZ wing at 18 hours a . p . f . , there is also expression at the distal margin and in distal intervein regions ( [5] and data not shown ) . This pattern of fj expression does not suggest that there are simple opposing gradients of ds and fj expression in the anterior and posterior wing during the period of Early Fz PCP signaling . If gradients of Ft/Ds pathway gene activity control the direction of the Early Fz ( Sple ) signal , we would expect that altering local levels of Ft/Ds pathway gene expression in the pupal wing should reorient the Fz ( Sple ) signal . We initially generated marked clones of ft , ds and fj knockdown or over-expression in a pkpk mutant wing to identify hair polarity changes that result from inducing novel gradients/boundaries of Ft/Ds signaling . However , interpreting the effects of clones of variable shape , size and position on the pkpk mutant hair phenotype proved unfeasible . To overcome this problem , we used the well-characterized sal-Gal4 driver to drive localized over-expression or knockdown of ft , ds and fj in both wild-type and pkpk mutant wings . The sal-Gal4 driver expresses Gal4 protein in the spalt expression pattern [34] ( i . e . between the L2 vein and midway between the L4 and L5 veins ( Figure 7A ) ) , and has been used successfully to generate gradients of Ft/Ds pathway gene expression along the A-P wing axis [27] . Using the sal-Gal4 driver to knockdown ds or ft , or to over-express ds , ft or fj resulted in changes in wing morphology , but did not affect hair polarity outside the main sal-Gal4 expression domain ( see Figure 7D , 7F , 7H , 7J and 7L ) . However , when the same experiments were done in a pkpk mutant wing , specific changes of hair polarity were observed outside of the main sal-Gal4 expression domain . For example , in the A region of the wing ( anterior to the L2 vein ) hair polarity on a pkpk mutant wing is posterior ( see Figure 4 and [12] , [18] , [21] ) . However , hair polarity in the A region of a pkpk mutant wing becomes anterior when sal-Gal4 is used to drive ds knockdown or ft or fj over-expression ( Figure 7E , 7K and 7M ) . In contrast , pkpk mutant wings in which sal-Gal4 drives ds over-expression or ft knockdown retain posterior hair polarity in the A region . In each case , hair polarity within the main sal-Gal4 expression domain resembles the modified pkpk phenotype seen when the same Ft/Ds pathway genes were knockdown or over-expressed uniformly in the wing ( see Figure 4 and Figure 5 ) , with the exception of fj over-expression which maintained the normal pkpk mutant phenotype within the sal-Gal4 expression domain . This last observation is curious , but may be due to the relative levels of expression driven by the MS1096-Gal4 and sal-Gal4 drivers . We note that Ft/Ds pathway gene misexpression can affect hair polarity on a pkpk mutant wing ten or more cell diameters anterior to the main sal-Gal4 expression domain , suggesting a substantial degree of cell non-autonomy . We have found that driving RNAi knockdown of the cell-autonomous tricornered ( trc ) ( VDRC transformant 107923KK [23] ) or forked ( f ) ( VDRC transformant 33200GD [23] ) genes using the sal-Gal4 driver generates occasional cells carrying a mutant hair phenotype anterior to the L2 vein ( data not shown ) . This raises the possibility there may be a gradient of sal-Gal4 expression extending several cell diameters anterior to the L2 vein that could generate corresponding gradients of Ft/Ds pathway gene activity . However , it is also possible that the boundary of Ft/Ds pathway gene expression generated using the sal-Gal4 driver may cause propagation of PCP changes outside of the expression domain , as has been observed in the Drosophila abdomen ( [7] and see discussion ) and in the control of cell proliferation by the Ft/Ds pathway [35] . In the context of our Bid-Bip model , these results suggest that generating gradients/boundaries of Ft/Ds pathway gene expression along the A-P wing axis can alter the direction of the Early Fz ( Sple ) signal , without affecting the Late Fz ( Pk ) signal . Specifically , we find that the Fz ( Sple ) signal is reoriented to point away from a region of reduced ds expression , but not from a region of ds over-expression . This is consistent with the observation that the Early Fz ( Sple ) signal normally points towards high levels of ds expression at the site of the L3 vein . The Early Fz ( Sple ) signal also points away from over-expressed ft or fj , which suggests that there are activity gradients of Ft and Fj that oppose the Ds expression gradient during the period of Early Fz ( Sple ) signaling . To test if gradients/boundaries of Ft/Ds pathway gene expression can alter PCP in the absence of both the Pk and Sple protein isoforms , we used sal-Gal4 to drive ft or fj over-expression , and ft knockdown , in a pkpk-sple-14 homozygous mutant wing . These localized changes in ft or fj expression altered the morphology of the pkpk-sple-14 wing ( compare Figure S2B with S2D , S2F and S2H ) , however , there were no significant changes in hair polarity at the boundaries of the sal-gal4 expression domain . For example , in the A region of a pkpk-sple-14 homozygous wing hair polarity is slightly more anterior than wild-type ( [18] , [22] and see Figure S2C ) , but is not altered when sal-gal4 is used to drive ft or fj over-expression , or ft knockdown ( see Figure S2E , S2G and S2I ) . These results show that gradients/boundaries of Ft/Ds pathway gene expression , which can reorient the Fz ( Sple ) signal , do not alter PCP in the absence of Pk and Sple isoform activity . The Ft/Ds pathway controls wing morphogenesis by determining the orientation of cell divisions and clonal growth [36] and it has been proposed that altered wing hair polarity associated with loss of Ft/Ds pathway activity might also be a consequence of abnormal cell division [37] . Our data show that altered Ft/Ds pathway activity can change wing morphology without affecting hair polarity across most of the wing ( see Figure 3 ) . In the context of our Bid-Bip model , this suggests that the role of the Ft/Ds pathway in wing morphogenesis is largely separable from its role in organizing the Late Fz ( Pk ) signal . However , we find that changes in Ft/Ds activity that alter wing shape consistently modify the pkpk mutant hair phenotype . This suggests that we have been unable to separate the role of Ft/Ds in wing morphogenesis from its role in organizing the Early Fz ( Sple ) signal . To attempt to unlink these activities , we controlled the timing of ds RNAi expression during the development of a pkpk mutant wing . Constitutive expression of ds RNAi in the developing pkpk wing ( using the MS1096-Gal4 driver ) alters wing morphology and changes pkpk wing hair polarity to a more distal orientation ( see Figure 4G , 4H and 4I ) . We controlled the timing of ds RNAi expression in MS1096-Gal4; UAS-ds ( IR ) wings by constitutive expression of Gal80ts , a temperature-sensitive Gal4 inhibitor , that binds and inactivates Gal4 at 18°C , but not at 30°C [38] . Consequently , animals of the genotype MS1096-Gal4/+; pk30 , ds ( IR ) /pk30 , tubP-GAL80ts can be cultured at 18°C ( when Gal80ts is active and inhibits Gal4 ) and then shifted to 30°C at specific times a . p . f . to induce ds RNAi expression in the wing . When flies of this genotype were cultivated continuously at 18°C , they showed a typical pkpk mutant wing phenotype ( Figure 8A and 8B ) , indicating that Gal80ts effectively inhibited Gal4 at this temperature . In contrast , when flies of this genotype were cultivated continuously at 30°C , they displayed wing morphology typical of reduced ds activity ( Figure 8C ) , combined with more distal hair polarity than a pkpk mutant ( Figure 8D ) . Flies shifted from 18°C to 30°C during pupal development showed close to wild-type wing morphology ( e . g . Figure 8E , 8G and 8H ) , but a hair phenotype that was dependent upon timing of the temperature shift . Flies shifted before 30 hours a . p . f . displayed the more distal hair polarity typical of continuous ds knockdown ( e . g . Figure 8F ) and we still observed significant modification of the pkpk hair phenotype when pupae were shifted at 36 hours a . p . f . . However , pupae shifted after 40 hours a . p . f . displayed hair polarity phenotypes within the range of normal pkpk mutant wings . These results show that controlling the timing of ds knockdown during the development of pkpk pupal wings can generate wings that have close to wild-type morphology , but still have a modified pkpk wing hair phenotype . We conclude that the role of ds in wing morphology is largely separable from its role in the Early Fz ( Sple ) signal . In principle , ds knockdown should modify the pkpk wing hair phenotype prior to the Early Fz ( Sple ) signal , but should have no effect after the Early Fz ( Sple ) signal . We find that typical ds RNAi modification of the pkpk phenotype still occurs when pupae are shifted at 30 hours a . p . f . at 18°C ( approximately equivalent to 13 hours a . p . f . at 25°C ) , and still see some modification of the pkpk phenotype when pupae are shifted at 36 hours a . p . f . at 18°C ( approximately equivalent to 17–18 hours a . p . f . at 25°C ) , but not when pupae are shifted at 40 hours a . p . f . at 18°C ( approximately equivalent to 19 hours a . p . f . at 25°C ) . These results are consistent with Strutt's proposal that the Early Fz PCP signal occurs at around 18 hours a . p . f . at 25°C [13] , [14] .
The data presented in this report allow us to refine our Bid-Bip Fz PCP signaling model ( Figure 1 ) , particularly the nature of the proposed Early Fz ( Sple ) signal . We find that the Early Fz ( Sple ) signal is in opposing directions in the anterior and posterior wing and converges precisely at the site of the L3 vein . The site of the L3 vein , therefore , represents a discontinuity in Early Fz ( Sple ) signaling that we have called the PCP-D ( see Figure 9 ) . However , it is clear that physical differentiation of the L3 vein is not required for the formation of the PCP-D . The correspondence of the PCP-D with the site of the L3 vein is perhaps surprising as the compartment boundary ( a barrier to clonal growth that runs a few cells anterior to the L4 vein ) appears a more obvious boundary between the anterior and posterior wing . However , the L3 vein has been defined as a specific region of low Hedgehog signaling within the wing [39] , suggesting this region has the molecular autonomy needed to function as a signaling centre . In addition , recently published work from the Eaton lab has also identified the L3 vein as the boundary between oppositely polarized cells in the anterior and posterior of early pupal wings [40] . We find that both reduced activity and uniform over-expression of Ft/Ds pathway genes have similar effects on the direction of the Fz ( Sple ) signal , which becomes more distal in both the anterior wing and distal regions of the posterior wing . Significantly , the Eaton lab has recently shown that the subcellular localization of Vang/Stbm protein in the early ( 15 hours a . p . f . ) pupal wing of a ds mutant is more distal than wild-type in both the anterior and distal posterior wing ( see Figure 7C in [40] ) . Our results are consistent with the idea that the normal direction of the Fz ( Sple ) signal is controlled by gradients of Ft/Ds pathway activity that can be flattened through either reduced or uniform expression of individual pathway components . We have confirmed an observation made in the Blair lab [27] that ds is expressed transiently in a P-D stripe within the pupal wing blade at around the time of Early Fz PCP signaling ( as defined by Strutt [13] , [14] ) and have localized the peak of Ds expression to the site of the L3 vein , the same location as the wing PCP-D . This implies that there are symmetric gradients of ds expression in the anterior and posterior wing and that the Early Fz ( Sple ) signal points up a ds expression gradient ( Figure 9 ) . This conclusion is supported by our finding that the Fz ( Sple ) signal reorients to point away from localized ds knockdown , but not from localized ds over-expression . The Early Fz ( Sple ) signal also points away from over-expressed ft or fj , which suggests that Ft or Fj activity has the opposite effect to Ds activity on direction of the Fz ( Sple ) signal ( Figure 9 ) . This is the same relationship between Ft , Ds and Fj activity that has been established in the Drosophila eye [41] and abdomen [31] . Recent molecular studies have shown that Fj , a golgi kinase , can phosphorylate cadherin domains within both Ft and Ds proteins [42] , [43] . It has been proposed that this modification increases Ft activity , but decreases Ds activity . We find that reducing ds expression ( or increasing ft or fj expression ) under the control of the sal-Gal4 driver redirects the Early Fz ( Sple ) signal for a significant distance ( ten or more cell diameters ) beyond the sal-Gal4 expression domain . In principle , reducing ds expression within the sal-Gal4 domain should generate a local reversal of the ds expression gradient at the boundary of sal-Gal4 expression ( e . g . the L2 vein ) . This short reversed ds gradient should generate a correspondingly short region of reversed Fz ( Sple ) signal which should be visible ( on a pkpk mutant wing ) as a short region of reversed hair polarity adjacent to the L2 vein . Therefore , the propagation of reversed hair polarity significantly anterior to the L2 vein is surprising . However , a similar propagation of reversed polarity is seen adjacent to loss-of-function and over-expression clones of ds , ft or fj in the Drosophila abdomen [7] , [31] . The model proposed for the propagation of altered polarity in the abdomen [7] may , therefore , also apply to the Early Fz ( Sple ) signal in the wing . Since it has been established that wing hair polarity points down a gradient of Fz activity [16] and we propose that the direction of the Early Fz ( Sple ) signal ( i . e . the hair polarity that would be specified by the signal ) points up a Ds expression gradient , it appears that there are opposing gradients of Ds and Fz activity during Early Fz ( Sple ) signaling . This relationship between Ds and Fz gradients is consistent with that described in the Drosophila eye [32] , although it is opposite to that previously proposed in the wing [6] . Our findings , therefore , may help resolve this discrepancy between the proposed relationships of Fz and Ds activity in the eye and wing that has been highlighted by Strutt , Mlodzik and others [2] , [41] , [44] . From this work , we conclude that for substantial regions of the wing ( including most of the anterior wing and distal regions of the posterior wing ) , Ft/Ds pathway activity can be altered such that the Early Fz ( Sple ) signal is redirected , but the Late Fz ( Pk ) signal remains unaffected . For any specific experiment , this result might be explained by the specific properties of the mutant allele used or by the specific spatial or temporal activity of the Gal4 driver used to drive gene knockdown or over-expression . However , we have shown that numerous alleles , as well as both knockdown and over-expression , of Ft/Ds pathway genes , can redirect the Fz ( Sple ) signal in a similar way , without affecting the Fz ( Pk ) signal in the same region . This suggests that across most of the wing there is a different requirement for the Ft/Ds pathway in the Early Fz ( Sple ) and Late Fz ( Pk ) signals . Moreover , we have found that loss of the Ft/Ds pathway regulator Lft affects the Early Fz ( Sple ) signal , but not the Late Fz ( Pk ) signal . This suggests that the mechanism used by the Ft/Ds pathway to direct the Early Fz ( Sple ) signal differs from that used to organize the Late Fz ( Pk ) signal . What , then , is the role of the Ft/Ds pathway in the Late Fz ( Pk ) signal ? Since the Late Fz ( Pk ) signal organizes hair polarity ( see Figure 1 ) , characterizing the loss of Ft/Ds pathway activity on hair polarity should be informative . We have found that driving ft or ds RNAi uniformly in the wing results in altered wing morphology , but only localized proximal hair polarity changes . This might be due to incomplete gene knockdown , coupled with different requirements for Ft/Ds activity for Late Fz PCP signaling in different regions of the wing . However , it is suggestive that wings homozygous for a fj amorphic allele show only a localized hair polarity phenotype in this same proximal region , implying that Fj is only required for hair polarity in the proximal wing . These results raise the possibility the Ft/Ds pathway is normally only required for hair polarity in the proximal wing . Since neither ft nor ds null flies are adult viable , previous studies have inferred the role of Ft and Ds in wing hair polarity from analyzing phenotypes of viable hypomorphic alleles , clones of amorphic alleles and localized over-expression [6] , [14] , [27] , [45] . Some hypomorphic ds allele combinations display extensive wing hair polarity disruptions [27] , [45] , although the residual activity of these specific alleles has not been well characterized . Wing clones homozygous for amorphic ft or ds alleles can show hair phenotypes , although this is dependent upon the position and/or size of the clone [6] , [14] . However , mutant clones generate ectopic Ft or Ds activity boundaries/gradients in the wing and it is known that localized mis-expression of Ft/Ds pathway genes can generate hair phenotypes in wing regions not affected by uniform over-expression [27] . Most telling , clones of fj affect hair polarity in regions of the wing that are not affected in amorphic fj wings [5] . These results clearly show that mis-regulated Ft/Ds activity can change wing hair polarity . However , they do not definitively establish a role for Ft/Ds pathway in the normal organization of hair polarity outside of the proximal wing . Therefore , it remains possible that Ft/Ds pathway activity is only required for hair polarity in the proximal wing , but mis-regulated Ft/Ds pathway activity can induce changes in hair polarity in other wing regions . This may restrict the normal role of the Ft/Ds pathway to organizing the Late Fz ( Pk ) signal in the proximal wing alone . According to our Bid-Bip model , the two Fz PCP signaling events aligned with different axes of the developing wing allow membrane ridges to be organized in different directions in the anterior and posterior ( see Figure 1 ) . The ability of the insect wing to deform specifically is vital for insect flight and it has been proposed that wing membrane structure helps provide the appropriate wing rigidity and flexibility [46] . In the case of membrane ridges , the membrane should be flexible parallel to the ridges , but be resistant to folding perpendicular to the ridges . The A-P ridges in the anterior wing are perpendicular to longitudinal wing veins which suggests a rigid anterior wing structure , whereas the posterior ridges are almost parallel with longitudinal wing veins suggesting a more flexible posterior wing structure . This organization is typical for Dipteran wings which usually have a well-supported leading edge and a flexible trailing edge . Indeed , we have seen similar ridge organization in wings of other Drosophila species ( data not shown ) . Therefore , the different orientation of ridges in the anterior and posterior wing may have a functional basis . The reason for the uniform distal hair polarity across the Drosophila wing is not well understood , but is conserved in a wide range of Dipteran species suggesting a functional constraint . Therefore , the two Fz PCP signals in different directions during Drosophila wing development may provide a mechanism that allows hairs and ridges to be organized appropriately using a single signaling pathway . Are multiple Fz PCP signaling events active in other Drosophila tissues besides the developing wing ? Intriguingly , the Prickle isoforms , Pk and Sple , play different roles in PCP in numerous Drosophila tissues , including the wing , eye , abdomen and leg [18] , [21] , [47]–[49] . This raises the possibility that there are multiple Fz PCP signals involving differential use of Pk and Sple isoforms in each of these tissues . However , the specific phenotypes associated with loss of either or both isoforms within the different tissues suggest that the details of our Bid-Bip model are unlikely to hold true for all tissues . How can multiple Fz PCP signals occur in different directions in the same developing tissue ? One possibility is that changes in the molecular makeup of the Fz PCP pathway allow it to respond to different global signals within the tissue , or to respond in different ways to the same global signal . In the Drosophila wing , this might result from the differential use of the Pk and Sple isoforms . Alternatively , the individual Fz PCP signals may respond to different global signals present at different times during tissue development or to a single dynamic global cue . The significance of Prickle isoform switching and the possibility of dynamic global PCP signals are ongoing topics of interest in our lab .
Flies were cultured at 25°C on standard yeasted cornmeal media , unless stated otherwise . Fly mutations used in this study were: rhove-1 , vn1 ( J . de Celis ) , pk30 ( D . Gubb ) , dsUA071 ( P . Adler ) , lftTG2 ( K . Irvine ) , UAS-ft , UAS-ds , UAS-fj ( S . Blair ) , P{GD14350}v36219 ( ds RNAi ) , P{GD881}v9396 ( ft RNAi ) , P{GD430}v6774 ( fj RNAi ) ( VDRC ) , TRiP . JF02842 ( ds RNAi ) , TRiP . JF02843 ( fj RNAi ) , TRiP . JF03245 ( ft RNAi ) ( TRiP ) , UAS-argos , ft1 , ds05142 , fjD1 , MS1096-Gal4 , ds2D60B ( ds-lacZ ) , fj9-11 ( fj-lacZ ) , 459 . 2-Gal4 ( sal-Gal4 ) , en-Gal4 , UAS-GFP . S65T ( T2 ) , tubP-Gal80ts ( 10 ) ( Bloomington Stock Center ) . We have described the CRM technique previously [12] . Briefly , adult wings were removed and laid gently on top of a thin layer of clear nail polish with the dorsal surface uppermost . The nail polish was allowed to dry , a cover slip placed on top and sealed with additional nail polish . Wings were viewed using an Olympus BX51 microscope ( Olympus America Inc . ) with the top lens of the condenser removed from the light path and the aperture diaphragm at its narrowest . Prepupae of appropriate genotype were collected and aged for 18 hours at 25°C . Pupal wings were dissected , fixed with 4% formaldehyde ( 20 minutes ) and beta-galactosidase activity assayed using X-gal by standard techniques . Newly unfolded wings were removed carefully from recently eclosed female flies and laid on a clean microscope slide . The wings were left in air , to prevent delaminated cells being washed out of the wing by mountant , and were either viewed directly or under a coverslip using appropriate spacers to prevent the coverslip contacting the wing surface . Wings were mounted in GMM mountant . Discussion in the text refers to the hair polarity across the entire A region of the wing ( between anterior wing margin and L2 vein ) , not just the region shown in Figure 7 . All results described were consistent for the first 10 wings observed of each genotype , except where number of progeny was limiting specifically; sal-Gal4/UAS-ft ( IR ) ( 6/6 wings ) and pk30 sal-Gal4/pk30; UAS-ds ( 6/6 wings ) . Female flies of the genotype MS1096-Gal4/+; pk30 , UAS-ds ( IR ) /pk30 , tubP-GAL80ts were cultured at 18°C and isolated as white pre-pupae . The pre-pupae were incubated between 0 to 48 hours at 18°C before shifting to 30°C . A total of 140 adult wings were mounted in GMM and studied .
|
Planar Cell Polarity ( PCP ) describes the orientation of a cell within the plane of a cell layer . The precise control of PCP has been shown to be vital for normal development in both vertebrates and invertebrates , and failures of PCP have been implicated in human disease . Studies in the fruit fly Drosophila have identified two genetic pathways , the Frizzled and Fat/Dachsous pathways , that are required to organize PCP , although the functional relationship between the two pathways remains unresolved . We have previously proposed a model of Frizzled pathway activity in the Drosophila wing that invokes two consecutive Frizzled signaling events oriented in different directions . The Early and Late Fz PCP signals use different isoforms of the Prickle protein . The goal of this study was to define the activity of the Fat/Dachsous pathway in the context of our Frizzled signaling model . Our results suggest that the Fat/Dachsous pathway has a different functional relationship with each of the Frizzled signaling events . Specifically , we find that by altering Fat/Dachsous pathway activity , we can reorient the Early Frizzled signal without affecting the Late Frizzled signal . This suggests that the functional relationship between the Fat/Dachsous pathway and the Frizzled pathway can vary , even between consecutive Frizzled signaling events within the same set of cells .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"developmental",
"biology/developmental",
"molecular",
"mechanisms",
"cell",
"biology/cell",
"signaling"
] |
2011
|
Two Frizzled Planar Cell Polarity Signals in the Drosophila Wing Are Differentially Organized by the Fat/Dachsous Pathway
|
There is increasing evidence that the microcirculation plays an important role in the pathogenesis of cardiovascular diseases . Changes in retinal vascular caliber reflect early microvascular disease and predict incident cardiovascular events . We performed a genome-wide association study to identify genetic variants associated with retinal vascular caliber . We analyzed data from four population-based discovery cohorts with 15 , 358 unrelated Caucasian individuals , who are members of the Cohort for Heart and Aging Research in Genomic Epidemiology ( CHARGE ) consortium , and replicated findings in four independent Caucasian cohorts ( n = 6 , 652 ) . All participants had retinal photography and retinal arteriolar and venular caliber measured from computer software . In the discovery cohorts , 179 single nucleotide polymorphisms ( SNP ) spread across five loci were significantly associated ( p<5 . 0×10−8 ) with retinal venular caliber , but none showed association with arteriolar caliber . Collectively , these five loci explain 1 . 0%–3 . 2% of the variation in retinal venular caliber . Four out of these five loci were confirmed in independent replication samples . In the combined analyses , the top SNPs at each locus were: rs2287921 ( 19q13; p = 1 . 61×10−25 , within the RASIP1 locus ) , rs225717 ( 6q24; p = 1 . 25×10−16 , adjacent to the VTA1 and NMBR loci ) , rs10774625 ( 12q24; p = 2 . 15×10−13 , in the region of ATXN2 , SH2B3 and PTPN11 loci ) , and rs17421627 ( 5q14; p = 7 . 32×10−16 , adjacent to the MEF2C locus ) . In two independent samples , locus 12q24 was also associated with coronary heart disease and hypertension . Our population-based genome-wide association study demonstrates four novel loci associated with retinal venular caliber , an endophenotype of the microcirculation associated with clinical cardiovascular disease . These data provide further insights into the contribution and biological mechanisms of microcirculatory changes that underlie cardiovascular disease .
Although both macrovascular and microvascular pathology are associated with cardiovascular disease , including coronary artery disease and stroke [1] , [2] , most studies on the genetic determinants of cardiovascular disease have primarily focused on macrovascular disease traits , and genetic analyses of microvascular disease phenotypes are rare [2] , [3] . This paucity of data is due to difficulties in non-invasively assessing the microcirculation . However , retinal arterioles and venules , which range between 50 to 300 µm in diameter , can be directly imaged , and provide an ideal opportunity to study the microcirculation in vivo [4] . Quantitative measurement of retinal blood vessel caliber from photographs allows a non-invasive direct assessment of the human microcirculation [4] . Studies using this technique have shown that changes in retinal vascular caliber ( e . g . , narrower arteriolar and wider venular caliber ) are associated with a range of cardiovascular diseases and their risk factors [5] , [6] , including hypertension [7] , diabetes mellitus [8] , [9] , stroke [10] , coronary heart disease [11] , and cerebral small vessel disease [12] , [13] . Retinal vascular caliber is also an early marker of major eye diseases such as diabetic retinopathy and age-related macular degeneration [14]–[16] . Recent studies suggest that genetic factors may play a role in influencing retinal vascular caliber [17]–[20] , so understanding specific genetic factors underlying retinal vascular caliber could therefore demonstrate novel insights into the mechanisms that contribute to the microvascular pathways of cardiovascular and eye diseases . To identify the underlying genetic determinants of retinal arteriolar and venular caliber , we meta-analyzed results of genome-wide association studies ( GWAS ) of 15 , 358 white participants from four large , prospective population-based cohorts included in the Cohorts for Heart and Aging Research in Genomic Epidemiology ( CHARGE ) consortium [21]: the Age Gene/Environment Susceptibility – Reykjavik Study ( AGES ) [22] , the Atherosclerosis Risk in Communities Study ( ARIC ) [23] , the Cardiovascular Health Study ( CHS ) [24] and the Rotterdam Study [25] . We replicated our findings in four independent cohorts of Caucasian ethnicity [the Australian Twins Study [26] , the UK Twins Study [27] , the Beaver Dam Eye Study ( BDES ) [11] , and the Blue Mountains Eye Study ( BMES ) ] [11] . Finally , in order to examine the association between the replicated hits and cardiovascular diseases , we used data on coronary artery disease from the Wellcome Trust Case Control Consortium ( WTCCC ) [3] , on stroke and myocardial infarction from the Heart and Vascular Health ( HVH ) Study [28] , [29] , on hypertension from the Global Blood Pressure Genetics ( Global BPgen ) Consortium [30] , and on diabetes mellitus from the Diabetes Genetics Replication and Meta-analysis + ( DIAGRAM+ ) Consortium [31] .
The total study sample for the discovery analyses was 15 , 358 and for the replication analyses 6 , 652 . Characteristics of both the discovery and replication samples are presented in Table 1 . A total of 179 single nucleotide polymorphisms ( SNPs ) at five loci surpassed our preset threshold ( p<5 . 0×10−8 ) for genome-wide significance for retinal venular caliber . Collectively , these five independent loci explain 1 . 0–3 . 2% of the variation in retinal venular caliber within our discovery cohorts . The QQ-plots ( Figure S1A ) show departure from the line of identity at approximately p<1 . 0×10−3 . Figure 1A displays the minus log-transformed p-values for the individual SNPs against their genomic position . Table 2 summarizes both the meta-analyzed results and results from each discovery cohort individually for the most significant SNPs at each locus that were associated with retinal venular caliber . No genome-wide significant locus was identified for retinal arteriolar caliber and only one SNP was associated with retinal arteriolar caliber at a significance threshold between 5 . 0×10−8 and 1 . 0×10−6 . The QQ-plot ( Figure S1B ) showed a departure from the line of identity at approximately p<1 . 0×10−4 . Figure 1B displays the minus log-transformed p-values for the individual SNPs against their genomic position . The most significant signal was on chromosome 13q12 ( rs2281827 , per minor allele ( T ) copy 1 . 0 µm ( SE: 0 . 21 ) increase in arteriolar caliber; minor allele frequency ( MAF ) : 0 . 23; p = 3 . 53×10−7 ) . This signal on chromosome 13q12 was located in FLT1 , also known as vascular endothelial growth factor receptor . Table 3 shows the results within each replication cohort for the five loci that were genome-wide significant in the discovery phase . Minor allele frequencies in the replication cohorts were very similar to that in the discovery cohorts . Four out of the five loci showed consistent effects in the combined analyses of the replication cohorts at a Bonferroni-corrected significance threshold of p<0 . 01 ( 0 . 05/5 , as five loci were tested in the replication phase ) , the exception was rs7824557 ( 8p23 ) . The combined analyses of the discovery and replication cohorts yielded an overall p-value of 1 . 61×10−25 for rs2287921 ( 19q13 ) . The corresponding values for the other loci were p = 1 . 25×10−16 for rs225717 ( 6q24 ) , p = 2 . 15×10−13 for rs10774625 ( 12q24 ) and p = 7 . 32×10−16 for rs17421627 ( 5q14 ) . Finally , for rs7824557 ( 8p23 ) the overall p-value did not reach genome-wide significance ( p = 3 . 80×10−7 ) . The regional association plots for these four loci are presented in Figure 2A–2D . After additional adjustments for hypertension and diabetes mellitus , the associations between the four replicated loci and retinal venular caliber remained the same ( Table S1 ) . Table 4 presents the results with clinical cardiovascular diseases for the four loci that were successfully replicated in the replication cohorts . These association results provided evidence for 12q24 as a risk locus for coronary artery disease and hypertension . The allelic odds ratios of rs10774625 were 1 . 13 ( 95% confidence interval ( CI ) : 1 . 03–1 . 24; p = 0 . 008 ) for coronary artery disease and 1 . 06 ( 95% CI: 1 . 01–1 . 12; p = 0 . 019 ) for hypertension . As we found the most convincing evidence for rs10774625 to be associated with coronary artery disease , we examined the association with coronary artery disease for all 10 SNPs on locus 12q24 that were genome-wide significant in the current analysis with retinal venular caliber . Figure 3 shows a plot in which the p-values for these 10 SNPs from the current analysis are combined with those for coronary artery disease from WTCCC . We found that all 10 SNPs were significantly associated with coronary artery disease at a nominal p-value of 0 . 05 suggesting a strong overlap between the association signals of retinal venular caliber and coronary artery disease .
In this meta-analysis of GWAS data from four populations on retinal microcirculation and subsequent replication in four independent cohorts , we identified four novel loci on chromosomes 19q13 , 6q24 , 12q24 and 5q14 that were consistently associated with retinal venular caliber in persons of Caucasian descent at genome-wide significance of <5 . 0×10−8 . The most significant SNPs at each of the four loci were associated with an approximate 2 . 0µm change in retinal venular caliber for each copy of the minor allele . Locus 12q24 was also associated with coronary heart disease and hypertension . We did not find any loci that reached genome-wide significance for retinal arteriolar caliber , and only one SNP reached highly suggestive levels . Our study is the first large study to evaluate common genetic variants of the microcirculation , increasingly thought to play a substantial role in the pathogenesis and development of clinical cardiovascular diseases , including coronary heart disease and stroke . The retinal vasculature provides a non-invasive direct view of the human microcirculation . Retinal venular caliber has been shown to predict a range of subclinical [5] and clinical cardiovascular disease [6] . In a recent meta-analysis , wider retinal venules were associated with a hazard ratio of 1 . 16 ( 95% CI: 1 . 06–1 . 26 ) for coronary artery disease in women while controlling for other known cardiovascular risk factors [11] . Furthermore , wider venular caliber predicted risk of stroke and is associated with progression of cerebral white matter lesions [10] , [12] . Both systemic and environmental factors likely induce variation in retinal venular caliber along with individual genetic differences [5] , [6] , [17]–[20] . Wider retinal venular caliber has been hypothesized to reflect the effects of hypoxia [32] , and an increased nitric oxide production and release of cytokines resulting from activated endothelial cells [33] . This is supported by clinical and epidemiological studies showing larger venular caliber to be associated with systemic biomarkers of inflammation , including C-reactive protein and interleukin-6 , and with impaired fasting glucose metabolism , dyslipidemia , obesity and cigarette smoking [5] , [34] . The most significant SNP associated with retinal venular caliber was in the RASIP1 gene ( rs2287921 , p = 1 . 61×10−25 ) on chromosome 19q13 . RASIP1 belongs to the family of RAS molecules , which have recently been implicated in animal models to be involved in vascular development , endothelial cell migration , capillary tube assembly , blood vessel homeostasis and vascular permeability [35] . Specifically , RASIP1 is expressed in the endothelium of the developing blood vessels and is essential for proper endothelial cell angiogenic assembly and migration [35] . On chromosome 6q24 , the top SNPs were located in or adjacent to VTA1 and NMBR genes . VTA1 encodes a protein involved in trafficking of the multivesicular body , an endosomal compartment involved in sorting membrane proteins for degradation in lysosomes [36] . Neuromedin B ( NMB ) is a peptide that acts by binding to a specific receptor protein ( NMBR ) and is involved in a number of physiological processes including immune defense , thyroid , adrenocortical function and cognition . NMB is also aberrantly expressed by a variety of cancers and is involved in tumor cell proliferation [37] . The signals for association on chromosome 12q24 were spread across a large 1 Mb LD block , including genes such as SH2B3 , ATXN2 and PTPN11 . The most significant SNP was located in ATXN2 . Defects in the ATXN2 are the cause of spinocerebellar ataxia type 2 ( SCA 2 ) , which belongs to the autosomal cerebellar ataxias characterized by cerebellar ataxia , optic atrophy , ophthalmoplegia and dementia . SCA 2 is caused by extension of a CAG repeat in the coding region of this gene . Another gene in this region is SH2B3 , which is expressed by vascular endothelial cells and regulates growth factor and cytokine receptor-mediated pathways implicated in lymphoid , myeloid and platelet homeostasis [38] . Our study showed that the most significant SNP in the SH2B3 region was rs3184504 ( p = 4 . 88×10−11 ) . Interestingly , this variant is associated with type 1 diabetes mellitus , a disease in which the risk of developing complications was found to be associated with wider retinal venular caliber [38] . Recent GWAS studies have shown that several SNPs at the locus 12q24 ( e . g . rs11065987 in ATXN2 and rs11066301 in PTPN11 ) are associated with platelet count , hemoglobin concentration , hematocrit , and blood pressure [39]–[41] . Furthermore , replication in independent case-control series including 9 , 479 cases and 10 , 527 controls have shown odds ratios of 1 . 14 ( 95% CI: 1 . 10–1 . 20; p = 2 . 52×10−9 ) and 1 . 15 ( 95% CI: 1 . 10–1 . 20; p = 7 . 05×10−11 ) per minor allele copy for the association of these two SNPs with coronary artery disease [39] . The corresponding allelic odds ratios for myocardial infarction were 1 . 17 ( 95% CI: 1 . 11–1 . 22; p = 3 . 43×10−10 ) and 1 . 18 ( 95% CI: 1 . 12–1 . 23; p = 2 . 42×10−12 ) [39] . In our discovery cohort , apart from rs10774625 we found nine additional SNPs in the region that were genome-wide significant , including both rs11065987 and rs11066301 ( 1 . 5 increase in venular caliber per minor allele for both ) that have also been shown to be associated with coronary heart disease and myocardial infarction . Finally , in the present study the association results from WTCCC and Global BPgen confirmed locus 12q24 to be a risk locus for both coronary artery disease and hypertension . Specifically , we found a strong overlap between the association signals of retinal venular caliber and coronay artery disease . The most significant SNPs at the 5q14 locus were located in an intergenic region . The closest gene in this region is MEF2C , which is located about 200 kb downstream . Myocyte enhancer factor 2 ( MEF2 ) family proteins are key transcription factors , consisting of four members MEF2A , MEF2B , MEF2C and MEF2D , controlling gene expression in myocytes , lymphocytes , and neurons . MEF2 also plays an important role in cardiogenesis , epithelial cell survival and maintenance of blood vessel integrity . Knockout of MEF2C gene in mice is embryologically lethal due to failure in cardiac development [42] . We did not find any loci that reached genome-wide significance for retinal arteriolar caliber . It is possible that genetic factors play a smaller role in arteriolar caliber , which is strongly associated with increasing age and blood pressure [5]–[8] . It is also possible that multiple genetic loci determine retinal arteriolar caliber and each locus exerts only a very weak association that is not detectable using our current study sample size . Thus , in order to examine genetic associations with retinal arteriolar caliber more fully , we are currently in the process of building collaborations with several other studies to increase the sample size of the discovery cohort . While we have identified four loci associated with retinal venular caliber , the identified SNPs may not represent the causal variants but could be in high linkage disequilibrium ( LD ) with the causal variants , which remain to be discovered . Further fine mapping of this genomic region will be required to facilitate expression and translational studies . Our study suggests that the effect of common genetic variants on retinal vascular caliber is small , and explain only a small proportion of the heritability of these traits [43] . It remains possible that low frequency variants might be important , but GWAS provides poor coverage of rare variants . With the study populations of predominantly Caucasian descent and stringent checks for latent population substructure , the associations are unlikely to be due to population stratification . To conclude , our population-based GWAS demonstrate four novel loci on chromosomes 19q13 ( within the RASIP1 locus ) , 6q24 ( adjacent to the VTA1 and NMBR loci ) , 12q24 ( in the region of the SH2B3 , ATXN2 and PTPN11 loci ) and 5q14 ( adjacent to the MEF2C locus ) associated with retinal venular caliber , an endophenotype of the microcirculation associated with clinical cardiovascular disease . Furthermore , locus 12q24 was also associated with coronary heart disease and hypertension . While further studies are needed to determine the causal genetic variants that underlie the heritability of this endophenotype , our findings will help focus research on novel genes and pathways involving the microvasculature and its role in the pathogenesis and development of cardiovascular disease .
Each cohort secured approval from their respective institutional review boards , and all participants provided written informed consent in accordance with the Declaration of Helsinki . The CHARGE consortium included large prospective community-based cohort studies that have genome-wide marker data and extensive data on multiple phenotypes [21] . All participating studies approved guidelines for collaboration , and a working group arrived at a consensus on phenotype harmonization , covariate selection and analytic plans for within-study analyses and meta-analyses of results . Details of cohort selection , risk factor assessment and retinal vascular caliber measurements in the four studies have been described in Text S1 , section 1 [11] , [22]–[25] . The AGES is a prospective study with subject recruitment from 2002–2006 of 5 , 764 surviving members , aged 66 years and older , of the established Reykjavik Study , a cohort of 19 , 381 participants assembled in 1967 to study cardiovascular disease and its risk factors among those born between 1907 and 1935 [22] . The ARIC study enrolled 15 , 792 men and women ( including 11 , 478 non-Hispanic whites ) from four U . S . communities to investigate the etiology and sequelae of atherosclerosis and cardiovascular risk factors [23] . Participants were between age 45 and 64 years at their baseline examination in 1987–1989 . The CHS enrolled 5 , 888 adults 65 years and older from four field centers to study coronary artery disease and stroke . The baseline examination took place either in 1989–90 or 1992–93 [24] . The Rotterdam Study enrolled 7 , 983 inhabitants from a district of Rotterdam aged 55 years and older to study cardiovascular , neurological , ophthalmic and endocrine diseases . The baseline examination was in 1990–93 [25] . The AGES and Rotterdam cohorts consisted predominantly of Caucasian whites . Only non-Hispanic white participants were included from the ARIC and CHS . Retinal photographs were obtained from participants at the third examination in ARIC and the tenth in CHS . Participants were excluded if their photographs could not be graded ( due to cataract , corneal opacities or poor focus ) or if genotyping data were unavailable ( Table 1 ) . Retinal vascular caliber was measured using standardized protocols and software that were developed initially at the University of Wisconsin and used in the ARIC study and the CHS , and following slight modifications , also in the Rotterdam and AGES studies ( Text S1 , section 2 ) [4] , [5] , [9] , [11] , . In brief , participants underwent retinal photography and optic disc-centered images were used to measure vascular caliber . Pharmacological mydriasis was used in the AGES and Rotterdam studies . For ARIC , CHS and Rotterdam the photographs of one eye were digitized using a high-resolution scanner and for the AGES study , photographs of both eyes were captured digitally . All digital retinal images were analyzed with a semi-automated retinal vessel measurement system and the calibers of all retinal arterioles and venules were measured in an area between half and one disc-diameter from the optic disc margin . The Parr-Hubbard-Knudtson formulae were used to compute summary measures for retinal arteriolar and venular calibers in micrometers ( µm ) and referred to as the “central retinal arteriolar and venular equivalents” [44] . Quality control ( QC ) measures for intergrader and intragrader intraclass correlation coefficients for retinal vascular calibers for each of the cohorts ranged from 0 . 76–0 . 99 in AGES , 0 . 69–0 . 89 in ARIC , 0 . 67–0 . 91 in CHS to 0 . 67–0 . 95 in the Rotterdam Study [4] , . The consortium was formed after the individual studies had finalized their GWAS platform selection . The four studies included used different platforms: the Affymetrix GeneChip SNP Array 6 . 0 for the ARIC study , Illumina HumanCNV370-Duo for the AGES study and the CHS and the Illumina Infinium HumanHap550-chip v3 . 0 for the Rotterdam Study . All studies used their genotype data to impute to the 2 . 2 million non-monomorphic , autosomal , SNPs identified in HapMap ( CEU population ) . Extensive QC analyses were performed in each cohort ( Text S1 , sections 3 and 4 ) [21] . Based on an a priori analysis plan , each study fitted an additive genetic model with a 1-degree of freedom trend test relating the retinal arteriolar or venular caliber to genotype dosage ( 0–2 copies of the minor allele ) for each SNP , adjusting for age and sex . For the CHS and ARIC studies , the analyses were additionally adjusted for study site . We used linear regression models to calculate regression coefficients ( beta ) and their standard errors ( SE ) using the ProbABEL program ( http://mga . bionet . nsc . ru/~yurii/ABEL/ ) in AGES , ARIC and Rotterdam study and the R software in CHS ( http://www . r-project . org ) . Genomic control correction was applied in each study prior to the meta-analysis . To implement genomic control , the λgc value was used to correct the SE as follows: SE_corrected = SE×√λgc . All four cohorts showed low dispersion with inflation factors in the range of 1 . 030–1 . 071 . We conducted a meta-analysis of the beta estimates obtained from the linear regression models from the four cohorts using an inverse-variance weighting using the R software ( MetABEL ) ( Text S1 , section 5 ) [45] . Strand information was available from all cohorts , facilitating the meta-analysis . After QC , filtering , and imputation within each study , we restricted our meta-analysis to the 2 , 194 , 468 autosomal SNPs that were common to all cohorts . We decided a priori on a genome-wide significance threshold of p<5 . 0×10−8 which corresponds to a p-value of 0 . 05 with Bonferroni correction for one million independent tests . For 2 . 2 million tests , it corresponds to an expectation of only 0 . 11 false positives , regardless of test-dependence [46] . Use of this threshold is also supported by LD patterns observed in deep sequencing work within European populations [47] . The genome-wide significant SNPs for each locus from the discovery phase were examined in four replication cohorts . The four replication sample sets included 1 , 709 participants from the Australian Twins Study , 1 , 132 from the UK Twins Study , 2 , 501 from the BDES and 1 , 310 from the BMES . Retinal vascular caliber measurements used the same methodology and formulas as in the CHARGE cohorts . Details of this and the procedures for genotyping are described in the Text S1 , sections 1 and 2 . In brief , in the Australian Twins Study , genotyping was performed on the Illumina Human Hap610W Quad array . In the UK Twins Study , 56% of the participants were genotyped using the Illumina 317k HumanHap duo array , whereas the remaining 44% using the Illumina HumanHap610Quad array . In the BDES , SNPs were genotyped using TaqMan SNP genotyping assays ( Applied Biosystems , CA ) . Finally , in the BMES genotyping was performed using the Illumina 610K array . In order to examine the association between SNPs that were successfully replicated in the current study and cardiovascular diseases , we obtained association statistics for each of these SNPs from several GWA studies . We obtained these data from the WTCCC on 2000 cases with coronary artery disease and 3000 controls [3] , from HVH Study on 501 cases with stroke [28] , 1 , 172 cases with myocardial infarction and 1 , 314 controls [29] , from Global BPgen on 8 , 871 cases with hypertension and 9 , 027 controls [30] , and from DIAGRAM+ on 8 , 130 cases with diabetes mellitus and 38 , 987 controls [31] . Details of each these studies have been described in the Text S1 , section 6 .
|
The microcirculation plays an important role in the development of cardiovascular diseases . Retinal vascular caliber changes reflect early microvascular disease and predict incident cardiovascular events . In order to identify genetic variants associated with retinal vascular caliber , we performed a genome-wide association study and analyzed data from four population-based discovery cohorts with 15 , 358 unrelated Caucasian individuals , who are members of the Cohort for Heart and Aging Research in Genomic Epidemiology ( CHARGE ) consortium , and replicated findings in four independent Caucasian cohorts ( n = 6 , 652 ) . We found evidence for association of four loci with retinal venular caliber: on chromosomes 19q13 within the RASIP1 locus , 6q24 adjacent to the VTA1 and NMBR loci , 12q24 in the region of ATXN2 , SH2B3 and PTPN11 loci , and 5q14 adjacent to the MEF2C locus . In two independent samples , locus 12q24 was also associated with coronary heart disease and hypertension . In the present study , we demonstrate that four novel loci were associated with retinal venular caliber , an endophenotype of the microcirculation associated with clinical cardiovascular disease . Our findings will help focus research on novel genes and pathways involving the microcirculation and its role in the development of cardiovascular disease .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"ophthalmology/retinal",
"disorders",
"cardiovascular",
"disorders/vascular",
"biology",
"genetics",
"and",
"genomics/population",
"genetics"
] |
2010
|
Four Novel Loci (19q13, 6q24, 12q24, and 5q14) Influence the Microcirculation In Vivo
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What are the minimal requirements to sustain an asymmetric cell cycle ? Here we use mathematical modelling and forward genetics to reduce an asymmetric cell cycle to its simplest , primordial components . In the Alphaproteobacterium Caulobacter crescentus , cell cycle progression is believed to be controlled by a cyclical genetic circuit comprising four essential master regulators . Unexpectedly , our in silico modelling predicted that one of these regulators , GcrA , is in fact dispensable . We confirmed this experimentally , finding that ΔgcrA cells are viable , but slow-growing and elongated , with the latter mostly due to an insufficiency of a key cell division protein . Furthermore , suppressor analysis showed that another cell cycle regulator , the methyltransferase CcrM , is similarly dispensable with simultaneous gcrA/ccrM disruption ameliorating the cytokinetic and growth defect of ΔgcrA cells . Within the Alphaproteobacteria , gcrA and ccrM are consistently present or absent together , rather than either gene being present alone , suggesting that gcrA/ccrM constitutes an independent , dispensable genetic module . Together our approaches unveil the essential elements of a primordial asymmetric cell cycle that should help illuminate more complex cell cycles .
Replicatively asymmetric cell cycles exist where the two distinct daughter cells resulting from cell division have distinct abilities to replicate their DNA . This is the case for the many Alphaproteobacteria that reproduce by asymmetric binary fission ( e . g . , Caulobacter and Brevundimonas species ) or budding ( e . g . , Hyphomonas and Hyphomicrobium species ) to produce a motile swarmer cell from a nonmotile stalked mother cell ( see [1] and references therein ) [2] , [3] . Swarmer cells do not replicate their DNA; they must first differentiate into stalked cells . During their motile juvenile phase , swarmer cells expend most of their energy on motility and little on growth [4] , [5] . Caulobacter crescentus [6] , a species that is ubiquitous in water , has for many years been used as a model organism for the study of development and the cell cycle . There are also examples of nonstalked bacteria that exhibit the same asymmetry in replication and motility ( e . g . , Rhodopseudomonas palustris ) [7] , [8] . Indeed , it has been proposed that morphological and functional asymmetry is much more widespread in the Alphaproteobacteria than previously thought [9] . This makes an understanding of asymmetric cell cycle regulation potentially even more relevant . However , the complexity of cell cycle control has made understanding the basic principles difficult . Here , we address this issue by using a minimal modelling approach to determine the core cell cycle regulatory circuit in Caulobacter crescentus . Asymmetric division in C . crescentus yields a motile daughter swarmer ( SW ) cell and a sessile stalked ( ST ) cell . The ST cell immediately reinitiates replication , while the SW cell must differentiate into a ST cell before it can replicate and divide ( Figure 1A ) . These replicative and morphological asymmetries are , in part , controlled by the essential master regulator CtrA through its ability , when activated by phosphorylation ( CtrA∼P ) , to interact with DNA regulatory sequences in the origin of replication ( Cori ) and with many cell-cycle–regulated promoters [10] , [11] . A second regulator of Cori , DnaA , ubiquitous in bacteria as an essential replication initiator , also targets many cell-cycle–regulated promoters [11] , [12] . However , though DnaA levels are reduced in SW cells , they can support plasmid replication [13] , indicating that it is likely not the regulator of replication asymmetry . This role is played by CtrA∼P: high levels inhibit replication in SW cells , whereas low levels in ST cells allow replication to proceed [14] . Instead , DnaA appears to dictate the underlying frequency of replication [15] . Localization of the activator and stabiliser of CtrA , the essential membrane-bound hybrid histidine kinase CckA [16] , specifically at the future SW cell pole , ensures a high level of CtrA∼P in postdivisional SW cells and its removal in the ST compartment [17] . As C . crescentus regulates temporally both the abundance and activation of CtrA to control cell cycle progression [14] , the cell cycle is very robust [18] . It has been proposed [19] that cell cycle progression in C . crescentus is controlled by a cyclical genetic circuit of four essential master cell cycle regulator proteins—DnaA , GcrA , CtrA , and CcrM—that are synthesised and degraded sequentially over the cell cycle . Here , we present a minimal mathematical modelling and experimental approach that challenges this assertion . Our model unexpectedly predicts that the “essential” cell cycle regulator GcrA is dispensable for core cell cycle progression . We experimentally test and verify this prediction . In addition , we experimentally uncover the dispensability of another cell cycle regulator , the methyltransferase CcrM , with simultaneous loss of the GcrA and CcrM module attenuating , rather than accentuating , cellular defects . Our conceptual approach resembles that applied to deciphering the minimal CDK control network in symmetrically dividing fission yeast [20] , although here we study an inherently asymmetric cell cycle and also employ a mathematical modelling approach . We expect our results to hold in other Alphaproteobacteria , and our overall methodology should be useful in the dissection of more complex cell cycles .
Asymmetric cell cycles are dictated by the spatiotemporally varying concentration of a regulatory protein whose presence inhibits DNA replication initiation in the nonreplicating offspring , whereas its absence in the “mother” cell allows replication ( or vice versa ) . This asymmetry must begin at or before the time of compartmentalisation . Since replication factors are generally cytoplasmic and hence likely diffuse and equipartitioned , this suggests the existence of an additional , localised protein that controls the first's activation and/or stability . With the above regulatory module , a diffuse regulatory protein , and its localised activator/stabiliser , basic regulatory control of an asymmetric cell cycle should be possible . However , previous mathematical models [21]–[23] of asymmetric cell cycles ( e . g . , in C . crescentus ) have been much more complex and have not made experimentally verified predictions . Here , we therefore develop a simple , but strongly predictive , mathematical model of an asymmetric cell cycle , as applied to C . crescentus , constructed to include only minimal regulatory elements . The model incorporates GcrA , CckA , and CtrA , but not DnaA or CcrM ( see justifications below ) . The cell cycle regulator GcrA regulates genes involving DNA replication , division , and polar development [24] . Its synthesis is promoted by DnaA ( see below ) and repressed by CtrA∼P . The ctrA gene has two promoters [25]: P1 , activated by GcrA [24] but repressed by CtrA∼P and silenced by full DNA methylation [26] , and P2 , a stronger promoter , activated by CtrA∼P in a positive feedback loop . Halving of the P1 methylation state ( hemi-methylation ) , with associated subsequent P1 activation , is due to movement of the DNA replication fork through the ctrA locus . This event is short in duration compared to other cell cycle timescales and is therefore modelled as a discrete event through the parameter S , which is switched from 0 to 1 at this time . We take the time at which CtrA∼P levels drop below a low threshold as synchronous with the assembly of the replication machinery at Cori and take P1 hemi-methylation to occur a fixed time later ( the time required for replication initiation and subsequent movement of the replication fork past P1 ) . The DNA methyltransferase CcrM , which has been reported to be essential for viability , remethylates the P1 promoter at the adenine within its GAnTC target site [27] . This remethylation ( and as a result , silencing ) occurs in late pre-divisional ( PD ) cells when P1 is already repressed and after ccrM has been activated by CtrA∼P . Therefore , for the model output , it is not relevant exactly when in late PD cells remethylation occurs , and we take it to be synchronous with compartmentalisation ( for the ST compartment ) or SW-ST differentiation ( for the SW compartment ) , when the value of S is switched back to 0 . The vital primary role of CcrM in resetting methylation-dependent promoters , as described above for the ctrA P1 promoter , is essentially discrete . Hence , although switching of P1 methylation is included , we do not explicitly include CcrM in our minimal model . CckA initiates two phosphorelays leading to the phosphorylation ( activation ) and stabilisation of CtrA via a phosphotransferase , ChpT [28] . The active phosphorylated form , CckA∼P , is localised primarily to the pole opposite the stalk ( Figure 1A ) [29] . DivL , an essential noncanonical tyrosine kinase , activates , recruits , and co-localises with CckA [30] , [31] , events for which replication initiation is a prerequisite ( handled in the model by a dependence on the parameter S ) [32] . Upon compartmentalisation , the phosphotransfer from CckA is cut-off in the ST compartment . As a result , CtrA∼P is deactivated and removed from the ST cell progeny , while it remains stable and active in the SW cell ( Figure 1A ) . This distinction is the fundamental origin of the asymmetry between the ST and SW cells . Diffusive exchange of cytoplasmic molecules is largely unimpeded up until the last moments of constriction [33] . Hence we model compartmentalization as a discrete event again through the discrete parameter S , which is switched from 1 to 0 at compartmentalisation but only in the ST compartment . This ST compartment-specific switch is the origin of the ST/SW asymmetry in the model . In the SW compartment , S is also eventually switched back to 0 , but only at a much later time corresponding to the unknown signals initiating SW to ST differentiation . Furthermore , because high CtrA∼P levels activate the essential ftsQA cell division operon [34] , we can take a high threshold level of CtrA∼P as a proxy for compartmentalisation . In summary , our model consists of the minimal regulatory module ( CtrA and CckA ) suggested above , the cell cycle regulator GcrA , the experimentally described ctrA promoter regulation , and three discrete cell cycle events ( replication , compartmentalization , and SW–ST differentiation ) . It has been proposed [19] that methylation of the dnaA promoter by the CcrM methyltransferase promotes dnaA transcription , thereby restarting the cascade of cell cycle regulators with a surge in DnaA synthesis . However , mutation of the putative methylation site in the dnaA promoter does not significantly alter promoter activity [35] . It is therefore unknown what leads to the burst in DnaA synthesis prior to replication initiation . It has recently been suggested that DnaA controls the initiation frequency rather than the acquisition of replication competence [15] . Due to this uncertainty and the focus of our minimal model on asymmetry , we do not explicitly include DnaA in our minimal model; we instead assume that DnaA levels rise as CtrA ( ∼P ) levels drop and take a low threshold in CtrA∼P levels as being synchronous with replication initiation . This assumption is justified by the observation that CtrA and DnaA generally have alternating profiles—that is , when one is low , the other is high and vice versa [36] , even under starvation conditions [37]—and CtrA proteolysis coincides with DnaA binding to Cori [38] . In vitro , CtrA binding displaces DnaA from Cori , indicating competitive binding [38] . The wiring diagram , equations , and parameters of the model are shown in Figure 1B , C . Further description and justification of the model are given in Text S1 , and model SBML files are provided in Texts S2 and S3 . In order to select model parameter values , we used existing literature measurements ( see Text S1 ) . Out of 22 parameters , two are removed by normalisation , six are obtained from experiments , and 10 have constraints placed on their values by experiments . To constrain the model parameters further , we quantitated the relative amount of GcrA and CtrA during the same cell cycle at higher time resolution than previously ( Figure 2A , B ) by semiquantitative immunoblotting and fitted our model to the profiles . The small number of unknown parameters ensures that our model is constrained by the available data ( see Text S1 ) . The model recapitulated the known effects of various experimental perturbations in the methylation state of ctrA P1 or constitutive expression of GcrA ( see Text S1 and Figure S2B ) . Comparisons between the theory and experiment are shown in Figure 2B and Figure S1 , with good agreement . Although the above model is simple and reproduces many features of the C . crescentus cell cycle , it is still more complex than our previous minimal two component module . In particular , we have included GcrA and intricate regulation of CtrA through two promoters . Consequently , we pursued a minimal two component model for C . crescentus by removing GcrA and its regulation of ctrA P1 . Previously it was reported that GcrA-depleted cells die [24] . Accordingly , in previous models removal of GcrA led to failed cell cycle control . However , strikingly , our minimal in silico model predicted a functional cell cycle without GcrA , albeit with an extended period ( Figure S2C ) . The model predicts that P2 promoter feedback , combined with the active ( forward ) CckA phosphorelay , is strong enough to raise CtrA levels without P1 . Prompted by these predictions , we revisited previous experimental approaches used to conclude that GcrA is indispensable . We inactivated gcrA by generalized transduction of a ΔgcrA::Ω allele ( conferring spectinomycin resistance ) into the NA1000 wild-type ( WT ) strain and found that colonies appeared after ∼6 d in rich medium ( Figure 2C ) . By contrast , transduction into a WT strain harboring pMT335-gcrA ( gcrA on multicopy plasmid pMT335 ) gave clones after ∼2 d . This 4-d growth delay may explain why gcrA was first described as essential in complex ( PYE ) medium [24] . On minimal ( M2G ) medium this delay is reduced to ∼1 d . However , the CCNA_01269 ( CC_1211 , henceforth gcrB ) gene of C . crescentus encodes an uncharacterized gcrA paralog with 44% sequence identity to gcrA ( Figure S3 ) . While ectopic expression of gcrB ( from pMT335-gcrB ) can partially compensate for the absence of gcrA , transduction of ΔgcrA::Ω into cells with an in-frame deletion in gcrB ( ΔgcrB ) gave similar results as transduction into WT cells ( Figure 2C ) . PCR analysis ( Figure S4A ) and immunoblotting ( Figure 2G ) confirmed that transductants had exchanged the gcrA gene with the ΔgcrA::Ω allele . Importantly , the pleiotropic phenotypic defects of ΔgcrA::Ω cells ( see below ) were corrected by pMT335-gcrA , partially corrected by pMT335-gcrB but not by the empty vector ( Figure S4B ) . Overall , these experiments confirm a key prediction of our model that GcrA is inessential for cell cycle progression . We first focused on the cell doubling time during exponential growth , as determined by the optical density of a liquid culture . This time is dependent on the type of growth medium used . We found that the doubling time of ΔgcrA::Ω cells was 75% longer than for the WT in PYE , and 40% longer in M2G , qualitatively consistent with the model ( Figure 2D , E ) . The cells also exhibited a lengthened lag phase ( unpublished data ) . However , ΔgcrA::Ω cells have an origin-to-terminus ratio similar to WT cells , demonstrating that this lengthened doubling time is not due to a defect in DNA replication initiation ( Figure 2F , see below ) . Consistent with these results , fluorescence-activated cell sorting ( FACS , Figure S5 ) revealed an increase in cell length ( Figure S5A ) and in chromosome number ( Figure S5B ) in ΔgcrA::Ω versus WT cells . The increase in chromosome number scales with the increase in cell length , which arises from perturbed cytokinesis ( Figure 2D , E , see below ) . Immunoblotting showed a reduction in levels of the MipZ division regulator and the essential late cell division protein FtsN ( Figure 2G ) . Finally , the difference in cell length in ΔgcrA::Ω versus WT cells was attenuated in M2G ( 176% of WT ) compared to PYE ( 264% of WT ) , consistent with the respective increases in doubling time described above . We also observed that ΔgcrA::Ω cells do not undergo the cell-cycle–regulated switch in buoyancy that is exploited to separate swarmer and stalked cells ( Figure S4B ) and are poorly motile on soft ( 0 . 3% PYE ) agar ( Figure S4C ) and in broth ( unpublished data ) . Additionally , ΔgcrA::Ω cells are resistant to the S-layer–specific bacteriophage ΦCr30 and the pilus-specific bacteriophage ΦCbK ( Figure S4B ) consistent with reduced levels of the pilin subunit PilA , the polarity factor PodJ ( both required for pilus assembly ) , and of the S-layer subunit RsaA in ΔgcrA::Ω versus WT cells ( Figure 2G ) . The abundance of CtrA , DnaA , and CcrM was also altered in ΔgcrA::Ω cells: while CtrA was diminished ( as expected from our model ) , the abundances of CcrM and DnaA were elevated ( Figure 2G ) . Importantly , immunoblotting revealed identical defects on protein abundance seen in ΔgcrA::Ω already after 5 h of GcrA depletion using the xylose-inducible promoter ( Pxyl ) . These defects were still present 24 h after depletion but were reversed following re-instatement of gcrA expression for 16 h ( Figure S6A , C ) . To test if one or a combination of these abnormalities impairs growth of ΔgcrA::Ω cells , we screened for ΔgcrB ΔgcrA::Ω cells mutagenized with an himar1 transposon ( Tn ) that form colonies faster than the parent . Backcrossing and mapping identified nine distinct Tn insertions , eight of which were in either the 5′ end of ftsN or in the 3′ end of the upstream gene , CCNA_02087 ( CC_2008 ) , that reads in the same direction as ftsN ( Figure 3A ) . These eight PftsN::Tn insertions attenuate the growth and division defect of ΔgcrA::Ω cells as demonstrated by DIC imaging ( cf . , Figure 3D with Figure 2D ) and by FACS ( Figure S5A , B ) . Moreover , immunoblotting revealed that these insertions restore FtsN to near WT levels ( Figure 3F ) , presumably because of an outwardly facing promoter of the Tn directing ftsN transcription . As before , the origin-to-terminus ratios were similar to the WT ( Figure 3E ) . We also confirmed that transduction of ΔgcrA::Ω into WT cells expressing FtsN from Pvan on pMT335 ( pMT335-ftsN ) or from Pxyl at the chromosomal xylX locus ( ΔftsN xylX::Pxyl-ftsN ) gave colonies after ∼3 d on rich medium ( unpublished data ) , compared to ∼6 d for the WT expressing FtsN from the endogenous promoter . Consistent with functional interaction of ftsN and gcrA , accumulation of ftsN mRNA was shown before to be GcrA-dependent [24] . Using a lacZ-based transcriptional reporter plasmid , we confirmed that PftsN indeed requires GcrA for full activity . After 5 h and 24 h of GcrA depletion , PftsN-lacZ is reduced to 52% and 46% of WT activity , respectively ( Figure 3G ) . Chromatin immunoprecipitation using antibodies to GcrA followed by deep-sequencing ( ChIP-Seq ) revealed that GcrA binds the ftsN promoter ( PftsN ) in vivo ( Figure 3C ) , suggesting that ftsN is a direct target of GcrA . In sum , activation of PftsN by GcrA is critical for efficient growth and division , and reduced FtsN levels cause growth defects in ΔgcrA::Ω cells . Surprisingly , one Tn of the nine insertions was found in the middle of the ccrM gene ( ccrM::Tn , Figure 4A ) . We performed complementation experiments and found that WT cells expressing ccrM from Pvan on pMT335 ( pMT335-RBS-ccrM ) formed colonies on PYE ∼3 d after transduction of ccrM::Tn compared to ∼5 d for WT cells harbouring the empty vector ( unpublished data ) . We also found that ccrM::Tn could be transduced into WT cells on PYE and that genomic DNA extracted from the ccrM::Tn mutant is susceptible to cleavage by the methylation-sensitive restriction enzyme Hinf1 , as is the case for genomic DNA extracted from ΔccrM::Ω cells ( Figure S7A ) [39] . Immunoblotting with antibodies to CcrM provided further confirmation that ccrM::Tn is a null allele ( Figure 4A ) . Therefore , ccrM , like gcrA , is dispensable for viability , consistent with the recent report by Gonzalez and Collier [40] . However , ccrM::Tn colonies take 4 d to form on PYE , similar to ΔccrM::Ω , and present a lengthened doubling time ( Figure 4B ) . This slow growth rate may explain why ccrM was previously reported to be essential [27] . As before , neither the ΔgcrA::Ω mutation nor the ccrM::Tn mutation affect the origin-to-terminus ratio compared to the WT ( Figure 4C ) . To quantitatively evaluate the relationship between gcrA and ccrM , we determined the relative frequency of Tn insertions in WT and ΔgcrA::Ω mutant cells by Tn-Seq following himar1 Tn mutagenesis . Tn insertions were hugely overrepresented along the ccrM coding sequence for ΔgcrA::Ω cells compared to the WT ( Figure S7B ) , being ∼115 times more abundant than the average of insertions over other coding sequences ( Figure 4D ) . By contrast , Tn insertions in scpAB and ftsE , both with promoters bound by GcrA in vivo based on ChIP-Seq ( Table S1 ) , are underrepresented in ΔgcrA::Ω compared to the WT ( Figure 4E ) . Insertions in the region upstream of ftsN were also found to be greatly overrepresented in ΔgcrA::Ω cells compared to the WT ( Figure S7C ) and were even more frequent than in the ccrM sequence ( Figure S7B ) consistent with the number and location of the nine insertions found in the Tn suppressor screen ( Figures 3A and 4A ) . We also observed an increased bias in insertions in the gcrB promoter region ( Table S3 ) , confirming the previously observed partial complementation of ΔgcrA::Ω . Returning to the ccrM::Tn insertion found in the screen , we discovered that it greatly improves the cytokinetic defect of ΔgcrB ΔgcrA::Ω cells ( Figure 4B ) . This was quantified with FACS analyses ( Figure S5 ) , which showed a substantial reduction in cell length and chromosome number , and with a variance much closer to WT than that of ΔgcrB ΔgcrA::Ω PftsN::Tn2 cells . We also observed a reduced lag phase ( unpublished data ) , though there was little or no improvement in the doubling time . The presence of stalked cells in DIC images indicted that morphological asymmetry is at least partially maintained in ΔgcrB ΔgcrA::Ω ccrM::Tn cells ( Figure 4B ) . To investigate this further , we examined the localisation pattern of the stalked pole-specific marker SpmX [41] in ΔgcrA::Ω ccrM::Tn cells and found a unipolar focus of SpmX-mCherry at the same site as the stalk ( Figure S8A ) , confirming that morphological asymmetry is maintained . Consistent with elevated steady-state levels of PilA ( Figure 4A ) , we also observed that the resistance of ΔgcrA::Ω cells to ΦCr30 and ΦCbK is diminished by the ccrM::Tn mutation ( Figure S4B ) . Lastly , we addressed replicative asymmetry by studying the localisation pattern of the centromere-binding protein ParB , which binds to the parS site near the origin of replication . Localisation of GFP-ParB [42] revealed an uneven number of foci in ΔgcrA::Ω cells , consistent with the presence of replicative asymmetry ( Figure S8B ) . Most importantly , time-lapse imaging of GFP-ParB in ΔgcrA::Ω ccrM::Tn cells showed asymmetric duplication and segregation of GFP-ParB ( Figure S8C ) , strongly suggesting that replicative asymmetry is maintained in these cells . We next explored the regulatory basis for the Tn insertion enrichment . The results of the suppressor screen and Tn-seq suggest that lack of FtsN is a significant limiting growth factor in ΔgcrB ΔgcrA::Ω cells . Therefore , we reasoned that the recovery due to the ccrM::Tn insertion might , at least partially , be mediated through ftsN . Accordingly we noted that PftsN also harbors a GAnTC methylation site . We found in ChIP-seq experiments that a polyclonal antibody to N6-methyladenosine ( m6A ) [39] precipitated PftsN efficiently from WT but not from ΔccrM::Ω cells ( Figure 3B ) , indicating that PftsN carries a CcrM-dependent m6A mark overlapping the GcrA target site . To evaluate the importance of this methylation site , we mutated the GAnTC site to GTnTC and found in lacZ–promoter probe assays ( Figure 3H ) that the mutant ( PftsN* ) promoter fires only at 14% of the WT rate in PYE ( 25% in M2G ) , consistent with reduced FtsN levels when ccrM is disrupted ( Figure 4A , lanes 1 and 6 ) . Moreover , PftsN* activity doubles by 24 h after depletion of GcrA from ΔgcrB cells ( Figure 3G ) , consistent with increased FtsN levels in the ΔgcrB ΔgcrA::Ω ccrM::Tn mutant compared to ccrM::Tn ( Figure 4A , lanes 5 and 6 ) , while , as noted earlier , the activity of WT PftsN responds in the opposite fashion ( Figure 3G ) . However , the activities , in absolute units , of PftsN and PftsN* 24 h after depletion of GcrA in the ΔgcrB background are very similar ( 637±8 and 608±6 Miller units , respectively ) . This would suggest that the methylation state of PftsN has an effect only in the presence of GcrA and that therefore the recovery in FtsN levels observed in ΔgcrB ΔgcrA::Ω ccrM::Tn cells compared to ΔgcrB ΔgcrA::Ω ( Figure 4A , lanes 4 and 5 ) is probably not mediated through the ftsN promoter ( assuming the mutation does not have unwanted side effects on the firing of the core promoter ) , at least on a low-copy lacZ-reporter plasmid . Because methylation is transient , the net effect on PftsN may result in the same measured activity as PftsN* , but the timing of methylation could be important or the results may be skewed due to a low level of GcrA expression being maintained from Pxyl even under repressive conditions that is absent in the ΔgcrB ΔgcrA::Ω ccrM::Tn mutant . However , taken together , these data suggest that ftsN is positively regulated by GcrA in the presence of methylation but negatively regulated in its absence . FtsN abundance in ΔgcrB ΔgcrA::Ω ccrM::Tn is still considerably lower than in WT or ΔgcrB ΔgcrA::Ω PftsN::Tn cells , even though the aberrant division of ΔgcrB ΔgcrA::Ω cells is largely repaired . The improved variance in cell length and chromosome number and reduced lag in growth of ΔgcrB ΔgcrA::Ω cells carrying the ccrM::Tn versus the PftsN::Tn mutation ( see above ) is likely due to the pleiotropic nature of the mutation , elevating the expression of many division genes to some extent . This is consistent with the somewhat raised steady state levels of CtrA and MipZ in ΔgcrB ΔgcrA::Ω ccrM::Tn cells compared to ΔgcrB ΔgcrA::Ω ( Figure 4A , lanes 4 and 5 ) . Because ccrM mutants also exhibit a reduction in FtsN abundance compared to WT cells ( Figure 4A ) , we tested if transduction of ΔccrM::Ω into ΔftsN xylX::Pxyl-ftsN yielded colonies on PYE ( with 0 . 3% xylose ) similar to the ΔgcrA::Ω mutation . Transduced ΔftsN xylX::Pxyl-ftsN colonies appeared after ∼4 d , whereas transduced WT colonies expressing FtsN from the endogenous promoter only appeared after 5–6 d ( unpublished data ) . Thus , the growth defect of ccrM mutants can be improved by expression of extra FtsN ( in addition to extra FtsZ [40] ) . Disruption of ccrM ameliorates the cytokinetic and morphological defects of ΔgcrB ΔgcrA::Ω cells but not the doubling time ( Figures 2D and 4B ) . Returning to our model , we asked if these experimental findings could be recapitulated . We found in our simulations that maintaining ctrA P1 in the unmethylated state ( mimicking the loss of ccrM ) in ΔgcrA cells caused only a slight change in cell cycle timing compared to the loss of GcrA alone ( Figure 4F ) , with a ∼3% decrease in the swarmer cell cycle period , consistent with the experimentally observed trend ( Figure 4B ) . Without methylation to suppress early activation , our model suggests that basal ctrA P1 transcription in ΔgcrA cells results in premature synthesis of CtrA and so has a negative effect on cell cycle progression , which cancels the positive effect during CtrA re-accumulation . The same neutral effect on doubling time was also seen in a WT background ( Figure S2B ) consistent with previous results ( Text S1 ) [26] . Our model therefore provides a possible explanation for why the decrease in cell doubling time is small , even though ΔgcrB ΔgcrA::Ω ccrM::Tn cells otherwise show substantial phenotypic recovery versus ΔgcrB ΔgcrA::Ω cells .
Our combined minimal modelling and forward genetics approach in C . crescentus has unexpectedly uncovered gcrA and ccrM as a dispensable genetic module . Strikingly , the core C . crescentus asymmetric cell cycle network can therefore function without two of the four “master” regulators , revealing a high level of robustness . Though both gcrA and ccrM are highly conserved , it is much more common for both to be present or absent in the alphabacterial lineages rather than either gene being present alone ( Figure 5A ) [43] , which supports our findings . Furthermore , gcrA and ccrM are not present in the tree root of the Alphaproteobacteria , whereas ctrA and cckA are [43] , suggesting that our minimal model describes the cell cycle of the primordial Alphaproteobacterium . Within our minimal cell cycle network , we find that asymmetry can be controlled with just two fundamental components: an inhibitor of DNA replication initiation ( CtrA ) and a localised activator that controls the former's cell-type–specific activation ( CckA ) . In Figure 5B we present a schematic of the current biological model of cell cycle regulation in C . crescentus with the dispensable GcrA/CcrM module highlighted . Several elements have yet to be fully understood ( indicated by question marks ) : What triggers the pulse in DnaA concentration at the beginning of the cycle ? Is this mediated by the Lon protease [44] and/or transcriptional control of dnaA [19] ? How are CckA localisation and activation dependent on replication initiation ? What are the mechanisms underlying SW cell differentiation ? The co-evolution of GcrA and CcrM and our results on ftsN regulation are consistent with the recent discovery that promoter binding and transcriptional activation by GcrA is methylation-dependent [39] . Connecting transcriptional activation to chromosome position and cell cycle timing ( via the hemi-methylation caused by the passing of the replication fork ) potentially contributes greatly to cell cycle robustness , an important property especially for oligotrophic bacteria . From the point of view of the circuit shown in Figure 5B the GcrA/CcrM module could contribute to robustness in two ways: ( 1 ) Methylation-regulated transcription of ctrA P1 controls the timing and prevents the early accumulation of CtrA , which would hinder cell cycle progression; ( 2 ) GcrA provides an additional connection between DnaA and CtrA , resulting in more reliable accumulation and activation of CtrA after DNA replication initiation , in addition to the link between DNA replication initiation and CckA localisation/activation . The potential role of the GcrA/CcrM module in the robustness of the timing of cell cycle events may also explain why its absence is primarily observed in obligate endosymbionts and pathogens , as are found in the Rickettsiales . These bacteria have a long generation time as an adaption to the slower growth of the host cell . For example , Rickettsia prowazekii has a doubling time of about 10 h . This is likely reflected in a more relaxed coordination of cell cycle events with replication fork progression , coordination for which the GcrA/CcrM module is of great importance . Thus , without strong selection , it is perhaps not surprising that this module was lost during the evolution of this branch . The success of our approach in reducing an asymmetric cell cycle to its simplest , primordial components illustrates the potential to define the core regulation of sophisticated cell cycles using this strategy . This methodology would be especially useful for eukaryotic cell cycles . The complexity of their regulatory networks has made understanding the basic principles involved a challenging task . Mathematical models of eukaryotic cell cycles , such as those of fission and budding yeast [45]–[47] , have made limited progress in simplifying the regulatory networks or identifying essential , core components . Indeed , progress on identifying redundancy in the cell cycle circuitry has been led by experiments rather than by modelling [20] , [48] . We therefore expect that our minimal modelling approach could play an important role in dissecting these more complex cell cycles .
Caulobacter crescentus NA1000 [49] and derivatives were cultivated at 30°C in peptone yeast extract ( PYE ) rich medium or in M2 minimal salts plus 0 . 2% glucose ( M2G ) supplemented by 0 . 4% liquid PYE [50] . Escherichia coli S17-1 [51] and EC100D ( Epicentre Technologies , Madison , WI ) were cultivated at 37°C in Luria Broth ( LB ) rich medium . We added 1 . 5% agar into M2G or PYE plates , and motility was assayed on PYE plates containing 0 . 3% agar . Antibiotic concentrations used for C . crescentus include kanamycin ( solid , 20 µg/ml; liquid , 5 µg/ml ) , tetracycline ( 1 µg/ml ) , spectinomycin ( liquid , 25 µg/ml ) , spectinomycin/streptomycin ( solid , 30 and 5 µg/ml , respectively ) , gentamycin ( 1 µg/ml ) , and nalidixic acid ( 20 µg/ml ) . When needed , D-xylose or sucrose was added at 0 . 3% final concentration . Swarmer cell isolation , electroporation , biparental mating , and bacteriophage φCr30-mediated generalized transduction were performed as described in [50] , [52] , [53] . Bacterial strains , plasmids , and oligonucleotides used in this study are listed and described in tables below . ΔgcrA::Ω ( SpcR ) transducing phage stock is a φCr30 lysate of LS3707 [24] . Overnight cultures of NA1000 and NA1000 ΔgcrB strains harboring or not pMT335 , pMT335gcrA , or pMT335gcrB plasmids were first washed with fresh liquid medium ( PYE or M2G ) and resuspended at a concentration of 109 cfu/ml . We infected 0 . 5 ml of cells with 50 µL of φCr30 phage stock ( ∼1010 pfu/ml ) , incubated them for 2 h at room temperature , and then plated them on solid PYE or M2G containing spectinomycin/streptomycin antibiotics . Plates were incubated at 30°C , and visible colonies were counted each day . Experimental values represent the average of three biological replicates . Addition of 50 mM vanillate to the plates , to further induce GcrA or GcrB synthesis from pMT335 plasmids ( that harbor gcrA or gcrB under the control of the vanillate-inducible promoter , Pvan [54] ) , did not change the transduction values , presumably due to leaky expression from Pvan . Integration of the ΔgcrA::Ω construction was checked by PCR and confirmed by immunoblot analysis . PCR was done using pro-gcrA and gcrA-EcoRI primers that allow amplification of the gcrA gene only in strains that do not carry the ΔgcrA::Ω allele . Strains harboring the pMT335gcrA complementation plasmid served as a positive control of φCr30 transduction efficiency . The fact that the ratio of NA1000+pMT335 versus NA1000+pMT335gcrA colonies counted on PYE or M2G plates are 0 . 38 and 1 . 05 , respectively , indicates that spontaneous suppressors are not frequent . In support of this , genome sequencing of several ΔgcrA::Ω derivatives failed to reveal suppressor mutations . Cells were grown to exponential phase in PYE or M2G medium and then harvested . Chromosomal DNA was extracted using Ready-Lyse Lysozyme solution ( Epicentre Biotechnologies ) and DNAzol Reagent ( Invitrogen ) and then precipitated in 100% ethanol . The DNA pellet was washed 3 times with 70% ethanol and incubated at room temperature in 8 mM NaOH solution for 4 h . HEPES 1 M ( pH 7 ) was added to neutralize the pH . Cori-fwd/Cori-rev and Ter-fwd/Ter-rev primers were used to amplify ∼170 bp DNA close to the origin ( Cori ) or close to the terminus ( ter ) , respectively , of the Caulobacter crescentus chromosome [55] . Real-time PCR was performed using the Step-One Real-Time PCR system ( Applied Biosystems ) at different DNA dilutions ( 5 µL ) , with 12 . 5 µL of SYBR green PCR master mix ( Quanta Biosciences ) , 0 . 5 µL of each primer ( 10 µM ) , and 6 . 5 µL of water per reaction . PCR assay parameters were one cycle at 95°C for 5 min followed by 40 cycles at 95°C for 15 s , 55°C for 20 s , and 60°C for 15 s . Dilutions of NA1000 extracted DNA were used to generate Cori and ter standard curves , involving all Cori/ter ratios being normalized to the WT value . Average values are from triplicate measurements from two independent DNA extractions . β-Galactosidase assays were performed at 30°C as described previously [52] , [56] . We lysed 50 µL of washed cells at OD660 nm = 0 . 1–0 . 6 with chloroform and mixed them with 750 µL of Z buffer ( 60 mM Na2HPO4 , 40 mM NaH2PO4 , 10 mM KCl , and 1 mM MgSO4 heptahydrate ) . We added 200 µL of ONPG ( 4 mg/ml o-nitrophenyl-β-D-galactopyranoside in 0 . 1 M KPO4 pH 7 . 0 ) , and the reaction was timed . When a medium-yellow color developed , the reaction was stopped with 400 µL of 1 M Na2CO3 . The OD420 nm of the supernatant was determined and the units were calculated with the equation: U = ( OD420 nm * 1000 ) / ( OD660 nm * time ( in min ) * volume of culture ( in ml ) ) . For GcrA depletion experiments in M2G using strain NA1000 ΔgcrB ΔgcrA::Ω xylX::Pxyl-gcrA , M2G supplemented with 0 . 3% xylose overnight cultures were harvested and washed 3 times with M2 minimal salt solution , and then restarted in appropriate M2G or M2GX medium for 5 or 24 h at 30°C . For the 24 h time point , culture dilutions were done to maintain cells in exponential growth phase . Experimental values represent the averages of four independent experiments . For GcrA depletion experiments , overnight cultures of strain NA1000 ΔgcrB ΔgcrA::Ω xylX::Pxyl-gcrA grown in M2G supplemented with 0 . 3% xylose were harvested and washed 3 times with M2 minimal salt solution , and then resuspended in M2G ( GcrA depletion ) or M2GX ( GcrA expression ) medium for 2 , 5 , or 24 h at 30°C ( Figure S6A ) . Then , the 24 h M2G culture was supplemented with 0 . 3% xylose and incubated with the 24 h M2GX culture for an additional 16 h at 30°C . For the 24 h and 40 h time points , culture dilutions were done to maintain cells in exponential growth throughout the experiment . Protein samples were separated by SDS-PAGE and blotted on PVDF ( polyvinylidenfluoride ) membranes ( Merck Millipore ) . Membranes were blocked for 1 h with phosphate buffered saline ( PBS ) , 0 . 05% Tween 20 , and 5% dry milk and then incubated for an additional 1 h with the primary antibodies diluted in PBS , 0 . 05% Tween 20 , 5% dry milk . The different antisera were used at the following dilutions: anti-DnaA ( 1∶20 , 000 ) [38] , anti-GcrA ( 1∶5 , 000 ) [24] , anti-FtsZ ( 1∶20 , 000 ) [57] , anti-MipZ ( 1∶5 , 000 ) [42] , anti-PodJ ( NTD ) ( 1∶10 , 000 ) [58] , anti-FtsN ( 1∶10 , 000 ) [59] , anti-CtrA ( 1∶10 , 000 ) [14] , anti-CcrM ( 1∶10 , 000 ) [27] , anti-PilA ( 1∶10 , 000 ) [60] , and anti-FljK ( 1∶20 , 000 ) [61] . The membranes were washed 4 times for 5 min in PBS and incubated for 1 h with the secondary antibody diluted in PBS , 0 . 05% Tween 20 , and 5% dry milk . The membranes were finally washed again 4 times for 5 min in PBS and revealed with Immobilon Western Blotting Chemoluminescence HRP substrate ( Merck Millipore ) and Super RX-film ( Fujifilm ) . For CtrA and GcrA relative protein quantifications during the cell cycle ( Figure 2B ) , membranes were scanned using the LAS-4000 digital imaging system ( Fujifilm ) and analyzed with the Multi Gauge V3 . 0 software . Each protein quantification value was normalized to the OD660 nm . Both datasets were normalized to their maximum value . Data represent the averages of three independent synchrony experiments . Each membrane was used for only one antibody detection to avoid any cross-reaction . For FtsN relative protein quantifications ( of unsynchronized cultures ) , datasets were normalized to the NA1000 sample value and represent the average of three independent experiments . Cell cultures of 5 ml were grown to exponential phase in M2G medium and then harvested . Cells were washed twice with 5 ml of 100 mM HEPES ( pH 7 . 2 ) and then resuspended in 200 µL of 100 mM HEPES ( pH 2 ) . After 10 min of incubation at room temperature , cells were pelleted and removed . The supernatant pH was neutralized by adding 3 µL of 5N NaOH solution . Samples were separated on 7 . 5% acrylamide SDS-PAGE gel followed by Coomassie Blue staining . PYE or M2G cultivated cells in exponential growth phase were immobilized using a thin layer of 1% agarose . For time-lapse experiments , LT494 cells in exponential growth phase were immobilized using a thin layer of PYE supplemented with 1% agarose . Fluorescence and contrast microscopy images were taken with an Alpha Plan-Apochromatic 100×/1 . 46 DIC ( UV ) VIS-IR oil objective on an Axio Imager M2 microscope ( Zeiss ) with 405 and 488 nm lasers ( Visitron Systems GmbH , Puchheim , Germany ) and a Photometrics Evolve camera ( Photometrics ) controlled through Metamorph V7 . 5 ( Universal Imaging ) . Images were processed using Metamorph V7 . 5 . Cells in exponential growth phase ( OD660 nm = 0 . 3–0 . 6 ) , cultivated in PYE or M2G , were fixed in ice cold 70% Ethanol solution . Fixed cells were resuspended in FACS staining buffer pH 7 . 2 ( 10 mM Tris-HCl , 1 mM EDTA , 50 mM NaCitrate , 0 . 01% TritonX-100 ) and then treated with RNase A ( Roche ) at 0 . 1 mg/ml for 30 min at room temperature . Cells were stained in FACS staining buffer containing 0 . 5 µM of SYTOX Green nucleic acid stain solution ( Invitrogen ) and then analyzed using a BD Accuri C6 flow cytometer instrument ( BD Biosciences , San Jose , CA ) . Flow cytometry data were acquired and analyzed using the CFlow Plus V1 . 0 . 264 . 15 software ( Accuri Cytometers Inc . ) . We analyzed 20 , 000 cells from each biological sample . The forward scattering ( FSC-A ) and Green fluorescence ( FL1-A ) parameters were used to estimate cell sizes and cell chromosome contents , respectively . Relative chromosome number was directly estimated from the FL1-A value of NA1000 cells treated with 20 µg/ml Rifampicin for 3 h at 30°C . Rifampicin treatment of cells blocks the initiation of chromosomal replication , but allows ongoing rounds of replication to finish . Cell growth in PYE or M2G medium was done in an incubator at 30°C under agitation ( 190 rpm ) and monitored at OD660 nm . Generation time values were extracted from the curves using the Doubling Time application ( http://www . doubling-time . com ) . Values represent the averages of at least three independent clones . Midlog phase cells cultivated in PYE were cross-linked in 10 mM sodium phosphate ( pH 7 . 6 ) and 1% formaldehyde at room temperature for 10 min and thereafter on ice for 30 min , then washed three times in PBS , and lysed in a Ready-Lyse lysozyme solution ( Epicentre Biotechnologies , Madison , WI ) according to the manufacturer's instructions . Lysates were sonicated ( Sonifier Cell Disruptor B-30 ) ( Branson Sonic Power Co . , www . bransonic . com ) on ice using 10 bursts of 20 s at output level 4 . 5 to shear DNA fragments to an average length of 0 . 3–0 . 5 kbp and cleared by centrifugation at 14 , 000 rpm for 2 min at 4°C . Lysates were then diluted to 1 ml using ChIP buffer ( 0 . 01% SDS , 1 . 1% Triton X-100 , 1 . 2 mM EDTA , 16 . 7 mM Tris-HCl ( pH 8 . 1 ) , 167 mM NaCl plus protease inhibitors ( Roche , www . roche . com ) ) and precleared with 80 µl of protein-A agarose ( Roche , www . roche . com ) and 100 µg BSA . Polyclonal antibodies to GcrA [24] were added to the remains of the supernatant ( 1∶1 , 000 dilution ) , incubated overnight at 4°C with 80 µl of protein-A agarose beads pre-saturated with BSA , washed once with low salt buffer ( 0 . 1% SDS , 1% Triton X-100 , 2 mM EDTA , 20 mM Tris-HCl ( pH 8 . 1 ) , 150 mM NaCl ) , high salt buffer ( 0 . 1% SDS , 1% Triton X-100 , 2 mM EDTA , 20 mM Tris-HCl ( pH 8 . 1 ) , 500 mM NaCl ) , and LiCl buffer ( 0 . 25 M LiCl , 1% NP-40 , 1% sodium deoxycholate , 1 mM EDTA , 10 mM Tris-HCl ( pH 8 . 1 ) ) and twice with TE buffer ( 10 mM Tris-HCl ( pH 8 . 1 ) and 1 mM EDTA ) . The protein–DNA complexes were eluted in 500 µl freshly prepared elution buffer ( 1% SDS , 0 . 1 M NaHCO3 ) , supplemented with NaCl to a final concentration of 300 mM and incubated overnight at 65°C to reverse the crosslinks . The samples were treated with 2 µg of Proteinase K for 2 h at 45°C in 40 mM EDTA and 40 mM Tris-HCl ( pH 6 . 5 ) . DNA was extracted using phenol∶chloroform∶isoamyl alcohol ( 25∶24∶1 ) , ethanol-precipitated using 20 µg of glycogen as a carrier , and resuspended in 100 µl of water . HiSeq 2000 runs of barcoded ChIP-Seq libraries yielded several million reads that were mapped to Caulobacter crescentus NA1000 ( NC_011916 ) according to the ELAND alignment algorithm ( services provided by Fasteris SA , Switzerland ) . The standard genomic position format files ( BAM ) were imported into SeqMonk ( Braham http://www . bioinformatics . babraham . ac . uk/projects/seqmonk/ , version 0 . 21 . 0 ) to build sequence read profiles . The initial quantification of the sequencing data was done in SeqMonk to allow the comparison of different conditions and to isolate regions of interest . To this end , the genome was subdivided into 50 bp probes , and for every probe an associated value was calculated , a value that derives from the pattern of reads that occurs within the probe region used for the quantitation using the Red Count Quantitation option . To discern between background signal ( modeled with a Poisson or negative binomial distribution ) and candidate peaks , we calculated the ratio of reads per probe as a function of the total number of reads . The overall average read count ( for all probes ) plus twice the standard deviation was used to establish the lower cut-off that separates the background from candidate peaks . Analyzed data are provided in Table S1 with selected peak values highlighted in yellow . Figure 3C focuses this analysis on the ftsN and CCNA_02087 region ( 2 , 235 , 000 to 2 , 238 , 000 bp on the Caulobacter crescentus genome ) . Figure 3B used global m6A ChIP-Seq analysis data obtained in the laboratory [39] . An overnight ΔgcrB ΔgcrA::Ω cell culture was grown in PYE and mutagenized with a himar1 transposon ( Tn ) [62] . To create the himar1 strain collection , transposition was induced by mobilizing the himar1 transposon ( KanR ) from plasmid pHPV414 in Escherichia coli S17-1 into the NA1000 ΔgcrB ΔgcrA::Ω strain and selecting for kanamycin-nalidixic acid-resistant Caulobacter clones that form colonies faster than the parent at 30°C ( ∼5/6 d ) on PYE . Nine distinct Tn clones appearing after ∼3 d were selected and already showed , under the microscope , less filamentous defects than the parent strain . Using φCr30-mediated generalized transduction , the Tn insertions were backcrossed in NA1000 ΔgcrB followed by transduction of the ΔgcrA::Ω strain to verify the phenotypes . Chromosomal DNA of the nine selected suppressors was extracted and partially digested for 4 min with HinP1 restriction . Digested DNA was recircularized by T4 DNA ligase ( Roche ) treatment and then electroporated in E . coli EC100D pir+ 116 ( Epicentre Biotechnologies ) . Plasmids of kanamycin-resistant clones were extracted and mapped using the himar-Seq2 primer that allowed the sequencing of the DNA region on the Caulobacter crescentus chromosome adjacent to the Tn insertion region . Tn collections of >100 , 000 kanamycin-nalidixic acid-resistant clones were collected for NA1000 ( WT ) and ΔgcrA::Ω strains , with the same protocol as previously described [62] . For each collection , all Tn clones were mixed and chromosomal DNA extracted . This DNA was used to generate a barcoded ChIP-Seq library and submitted to Illumina HiSeq 2000 sequencing . Tn insertion-specific reads ( 50 bp long ) were sequenced using the himar-Tnseq primer and yielded several million reads that were mapped to Caulobacter crescentus NA1000 ( NC_011916 ) [63] according to the ELAND alignment algorithm ( Johann Mignolet , Methods , manuscript in preparation ) . The Tn insertion coordinate format files ( BED ) were generated and imported into SeqMonk V0 . 21 . 0 for analyses . Table S3 is an excel table and includes NA1000-TnSeq and ΔgcrA::Ω-TnSeq data , where values are assigned for each Tn integration position on the Caulobacter crescentus chromosome . Both datasets were normalized to the total number of reads of the largest dataset . This file was used to generate Figure S7B and Figure S7C that are respectively focused on the ccrM and ftsN regions . Table S2 is also an excel table and includes ΔgcrA::Ω-TnSeq/NA1000-TnSeq ratio analyses . To this end , the annotated genome was subdivided into coding sequence ( CDS ) probes ( precluding the analysis of noncoding sequences such as promoter regions ) , and for every probe , an associated Tn insertion value was calculated . Both datasets were normalized to the total number of reads of the largest dataset . An average value of all CDS-Tn insertions normalized to the gene size was calculated , and 1% of this normalized value was used to correct each CDS-Tn insertion value . This correction prevents , during ratio calculations , a CDS-Tn insertion value of 0 and excludes also from the analyses CDS that do not share sufficient statistical Tn insertions . This file was used to generate Figure 4D and 4E .
|
Cell cycle regulation is remarkably complex and the fundamental principles difficult to understand , even in simple cells . The bacterium Caulobacter crescentus is a popular model organism to study cell cycle regulation due to the two different daughter cells resulting from cell division: a mobile “swarmer” cell and a “stalked” cell that adheres to surfaces . Here , we use mathematical modelling and genetic experiments to identify the core components of the asymmetric cell cycle of these bacteria . Using our mathematical model we predicted and confirmed experimentally that the transcription factor and cell cycle regulator , GcrA , hitherto thought to be essential , is in fact dispensable . We also identified another master regulator , the methyltransferase , CcrM as dispensable . Furthermore , simultaneous deletion of both GcrA and CcrM removes the severe cell division defects observed on either single deletion , returning cells to near wild-type morphology . We found that GcrA and CcrM constitute an independent , dispensable , genetic module that regulates transcription of cytokinetic proteins during the cell cycle . Phylogenetically , the module is conserved in Alphaproteobacteria , the class of Caulobacter , but is not present in the tree root of the class , suggesting that we have identified the primordial core of the asymmetric cell cycle regulatory circuit in the Alphaproteobacteria .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"systems",
"biology",
"prokaryotic",
"models",
"model",
"organisms",
"molecular",
"cell",
"biology",
"dna",
"modification",
"theoretical",
"biology",
"caulobacter",
"crescentus",
"gene",
"expression",
"genetics",
"regulatory",
"networks",
"epigenetics",
"biology",
"computational",
"biology",
"microbiology",
"microbial",
"growth",
"and",
"development",
"dna",
"transcription"
] |
2013
|
Computational and Genetic Reduction of a Cell Cycle to Its Simplest, Primordial Components
|
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